System and related methods for the characterization of objects through subsurface scattering across one or more edges of luminosity

Abstract

Disclosed herein are various embodiments relate generally to computer vision, graphics, image scanning, and image processing as well as associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to form at least three-dimensional models or images of objects and environments and to identify, characterize, or differentiate material or tissues in objects and environments.

Description

System and Related Methods for the Characterization of Objects Through Subsurface Scattering Across One or More Edges of LuminosityCROSS-REFERENCES TO RELATED APPLICATIONS

[0001] The present application claims the benefit of priority from U.S. Provisional Application Serial No. 63 / 633,439, filed April 12, 2024, the entire contents of which are hereby incorporated by reference into this disclosure as if set forth fully herein. Additionally, the present application incorporates by reference the entire disclosures of the following related patents and / or patent applications: U.S. Patent No. 10,724,853 (“the ‘853 patent”), issued on July 28, 2020 and entitled “GENERATION OF ONE OR MORE EDGES OF LUMINOSITY TO FORM THREE-DIMENSIONAL MODELS OF OBJECTS,” U.S. Patent Application No. 18 / 505,973 (U.S. Pub. 2024 / 0102795)(“the ‘973 app”), filed Nov. 9, 2023 and entitled “GENERATION OF ONE OR MORE EDGES OF LUMINOSITY TO FORM THREE- DIMENSIONAL MODELS OF SCENES”, and U.S. Provisional Application No. 63 / 716,932 (the ‘932 app), filed November 6, 2024 and entitled “REGISTRATION AND TRACKING BY 3D SCANNING THE TOPOLOGY OF ANATOMIC STRUCTURES.”FIELD

[0002] The present disclosure relates generally to surgical navigation, and more specifically to material determination through statistical inference of noise from optical three-dimensional scans.BACKGROUND

[0003] Advances in computing hardware and software have facilitated the generation of three-dimensional models and digital imagery that convey a shape of an object in three- dimensional space. Conventional computing techniques and devices are implemented as three- dimensional (“3D”) scanners to generate three-dimensional models of the surface of an object being scanned. Of these, structured-light scanner systems usually use complex patterns of light and one or multiple camera systems to capture images representing a shape of an object in three dimensions. While traditional structured-light scanner systems are functional, they are not well suited to apply to a wide range of applications, including material identification anddifferentiation and tissue characterization, because these systems typically require materials and resources that make the scanners cost prohibitive. For instance, such scanners employ lasers, as well as other computing hardware and algorithms that need to process the complicated light patterns, lens distortions, and imaging techniques associated with such scanners.

[0004] At least in one approach, a scanning technique using “weak-structured” light has been developed to address one of the limitations of the structured-light scanner systems. A traditional weak- structured light-based scanner typically employs simple incandescent lights and / or a rod (e g., pencil) to capture images from which a surface of an object may be derived. An example of such a scanner system is depicted in FIG. 1. Prior art diagram 100 depicts a simple incandescent light bulb 102 and a rod 114, or any other cylindrical object, such as a pencil, for applying a shadow onto basic plane 110 to capture the shape of prior art object 116. Light bulb 102 includes a filament 104 extending between supports at distance (“d”) 106 within a glass enclosure, which may be formed of a clear, unfrosted glass. Filament 104 typically generating light along a relatively wide range of distances relative to a width of rod 114. Generally, filament 104 may be positioned in a plane that is not parallel to rod 114. A prior art camera 101 may be used to capture images of points that can be used to compute the surface of prior art object 116. To capture the images of points, rod 114 is used to apply a shadow over prior art object 116 to try to determine a relative depth of a pixel on the surface of prior art object 116 as captured by prior art camera 101 (e.g., relative to the pixel at a point in time when prior art object 116 is absent).

[0005] The scanner in FIG. 1 suffers a number of drawbacks. While the scanner of FIG. 1 is functional, the system of prior art diagram 100 may not be well suited to model 3D imagery for three-dimensional objects or to perform material identification and differentiation or tissue characterization. White light bulb 102 and rod 114 may generate a diffuse shadow 120 that includes a minimal illumination zone 121 from a given light bulb 102. At further distances 122 from rod 114, the boundaries between minimal illumination zone 121 and illuminated portions 111 of basic plane 110 become increasingly diffuse. An example of increasing illumination diffusivity may be depicted as increasing from diffusion line 122 out along line 114 within distance (“b”) 126, which illustrates a diffused boundary between minimal illumination zone 121 of minimal illumination and an illuminated portion 111. This diffused boundary makes precisemeasurements of subsurface scattering, which are critical to material identification and differentiation and tissue characterization, difficult or impossible. To counter the deleterious effects of the diffused boundary, conventional approaches to 3D scanning rely on a threshold of illumination in conjunction with temporal or video-frame coordinates and an associated algorithm to define a boundary based on sufficient differences between darkness and lightness. A diffused boundary may reduce accuracy of a surface computed from the captured image of prior art object 116, and this reduced accuracy hinders abilities to characterize materials in prior art object 116. Also, using a threshold of illumination, while operational, may require disregarding luminous effects of different colors, shades, or textures. For example, the color “yellow” may have a higher luminance that may be distinguishable from the effects of the diffused boundary, whereas the color “blue” may have a relatively lower luminance that may be detected as being part of the diffused boundary. As such, blue portion 117 of prior art object 116 may be disregarded due to the implementation of a traditional threshold of illumination. Hence, colors and other luminous effects often cause this disregarding, an inaccuracy that is manifest in conventional 3D scanning and that hampers material or tissue differentiation by preventing the use of different colors or wavelengths of light to be used for such material or tissue differentiation. In some approaches, algorithmic computations are employed to classify whether a pixel is illuminated or not. These known algorithms, however, are usually limited to distinguishing between relatively substantial swings between brightness and darkness. Such thresholding may require resources to customize and adapt the scanner of prior art diagram 100 to specific scanning applications.

[0006] The analysis of subsurface scattered light for measurable physical features is important in material characterization and tissue differentiation. In some approaches, eventual determination of scattering and absorption parameters is performed using look up tables that characterize different components of the pattern of light after Fourier transform. The three- dimensional correction of optical parameters is disclosed in the prior art; however, such correction is for scatter and absorption properties and does not use a direct inference of scattering properties from distances along the surface of an object. Spatial Frequency Domain Imaging (SFDI) is also known to be used for interrogating tissue; however, pattern projection in SFDI is typically done with a digital micromirror device, which displays gray values using pulse width modulation (PWM). For a sinusoidal image, gray values are needed to prevent unwantedharmonics from contaminating the image, but PWM limits the rate at which patterns can be displayed. Square-wave SFDI is faster; however, the mathematical model is complex and cumbersome in characterizing tissue or identifying materials in an object.

[0007] Thus, what is needed is a solution for facilitating techniques to identify, characterize, differentiate, and distinguish, materials in objects, including tissue, without the limitations of conventional techniques.SUMMARY

[0008] A biopsy is an examination of tissue from a living body to discover the presence, cause, or extent of a disease. Usually, a biopsy is performed by removing tissue from a living body to perform the examination. Some types of biopsies may be performed with in vivo tissue; however, these types of biopsies usually require contact with the living tissue, which can damage the tissue or be a source of contamination. Optical biopsy techniques exist in which light is used to identify and characterize tissue; however, these techniques often involve contact between an optic fiber and the tissue, which can also damage or contaminate the tissue as other methods requiring contact. During biopsy of brain tissue, it is particularly important to avoid contact with the surface of the brain or any underlying structures. Thus, there is a need to provide a non- invasive and non-contact means of performing optical biopsy, which can provide high-speed and high-quality scans of subsurface tissue and which can characterize that information in order to determine tissue type or whether disease is present in the tissue.

[0009] The system and related methods for the characterization of objects through subsurface scattering across one or more edges of luminosity disclosed herein was developed in part to provide a system and method for scanning objects in three-dimensions, measuring the properties of light scattered across extremely sharp shadows, and analyzing those measurements to characterize the nature of the material(s) in objects, including differentiating between different types or tissue or identifying known and unknown tissue types. In a broad embodiment, system and method(s) described herein relates to characterizing objects using shadow caster scanners, which project one or more edges of luminosity across an object, measure subsurface scattering information across the one or more edges of luminosity to determine various physical features of the object, and generate a characterization model of the object from the information.

[0010] For example, a shadow caster scanner similar to those disclosed in the related patent applications listed above and configured for material identification and distinction and / or tissue differentiation and characterization may be used to scan objects undergoing material interrogation. In some embodiments, one critical innovation in the shadow caster scanner as used according to present disclosure is its ability to generate an extremely sharp shadow with high-contrast edges of luminosity at the shadow’s boundary with minimal penumbra. Such sharp shadow is generated by using a light source, which is typically linear in the most preferred embodiment, and a shadow caster with an edge contained in a plane, which also contains the light source. Having the light source and shadow caster edge in the same common plane provides the advantage of generating these high-contrast edges of luminosity, or shadow edges. During a scan, the edge of the shadow caster occurs in different positions relative to the light source, thereby causing the sharp shadow with its edge of luminosity to be projected across an object or area being scanned, and an image capture device, such as a camera or video camera, records images of the object and area being illuminated by the light source while the sharp shadow and edge of luminosity are projected across it. The positions and locations of the light source, shadow caster, and camera are all known for each point on the object or area that is scanned. In some embodiments, the shadow caster casts a sharp shadow, which appears as a stripe or band of darkness, and the apparatus of the present disclosure projects this dark stripe across an object or area being scanned by controlling the shadow caster while recording images of how the sharp shadow is distorted. The sharp shadow of this dark stripe in this embodiment would have two primary edges of luminosity: one that transitions from light to dark and one that transitions from dark to light. For example, the intensity of light across a light-to-dark edge of luminosity transitions from a pixel value of 255 (e.g., fully illuminated, or “white”) to a pixel value of 000 (e.g., no illumination, or “black”) over a small distance, while the intensity of light across a dark-to-light edge of luminosity transitions from pixel value 000 (e.g., no illumination, or “black”) to a pixel value 255 (e.g., fully illuminated, or “white”) over a small distance. (These values are examples only, and other enumeration may be used describe pixel illumination values.) For a perfectly scattering surface, this small distance is the penumbra of the shadow, and the penumbra size is roughly described as the size of the light times the distance between the shadow caster and the object divided by the distance between the shadow caster and the light source. For scattering surfaces that are less than perfect, this small distance is a convolution ofthe penumbra and the scatter profile width, as described below. Multiple light sources, shadow casters, and cameras may also be used to expedite scanning or for evaluating various physical features of the object or area being scanned. In some embodiments, the shadow caster is physically moved to sweep the edges of luminosity over the subject being scanned for material interrogation. In other embodiments, the shadow caster is generated as an opaque region of a transparent liquid crystal matrix, and the edges of luminosity are swept across the subject being scanned for material interrogation by making the first opaque region transparent and then generating other opaque regions in sequence across the liquid crystal matrix. In either case the edge of the shadow caster remains in a common plane with the light source.

[0011] For the purposes of this disclosure, a shadow edge plane is defined as the planar geometric projection from the center of the light source to the edge of the shadow caster and beyond. The shadow edge is defined as the location where the edge of luminosity of the sharp shadow intersects an object or area being scanned. The shadow drop-off is defined as the reduction of light into the shadow region, specifically from the shadow edge into the shadow. The scatter width, or reflectance profile length (these terms are synonymous), is defined as the characteristic width of subsurface scattering that is affected by the absorption of the material, the level of its scattering, and the asymmetry of the scattering, and is the perpendicular distance along the 3D surface of an object or area being scanned from the shadow edge and into the shadow.

[0012] The present disclosure relies in part on the measurement of subsurface scattering of light in a material. In general, when a “pencil-beam” of light is incident on a partially scattering surface, portions of the energy is distributed to surrounding regions of the point of incidence, which is a spreading influence. If that same “pencil-beam” does not encounter scattering but is instead partially absorbed as it propagates into the medium, the energy is distributed along a limited path, which is a constraining influence. If the “pencil-beam” encounters scattering, the loss of light to the transverse directions impedes the “forward” propagation of the light, and it does not statistically propagate “forward” as far. In other words, the scattering constrains the light in the “forward” direction, so scatter is not necessarily a spreading influence in all directions. The light scattering characteristics of a homogeneously scattering medium can largely be described by these two characteristics: scattering and absorption. Both scattering andabsorption limit the “forward” propagation of light; however, only scattering causes the light to deviate from the “forward” direction. A third factor, anisotropy, characterizes the tendency of the light to scatter in a forward direction, rather than a backward direction. With the present disclosure, for flat homogenous objects, either in transmission or reflection, experiments designed to determine these optical components can be relatively simple to analyze, because the geometry involved with the present disclosure, such as the positions and locations of the light source, shadow caster, and camera, is well understood. However, it is often desired to determine the optical parameters scattering, absorption, and anisotropy factor, in a more complex topological situation. Because the light scattering characteristics of a given surface may depend on both the angle of illumination and the angle of observation, knowing the geometry involved with the present disclosure is necessary for analyzing these physical features for the purposes of material characterization or tissue differentiation. According to some embodiments, the methods of the present disclosure primarily use spatial-based analysis for tissue differentiation and do not necessarily differentiate between absorption and scattering parameters, as much as determine the scatter width of a material being identified for a given angle of incidence and angle of observation. By way of example, in addition to scatter width, the physical features which are measurable by the present disclosure and useful for characterizing materials or tissue include, but are not limited to: surface curvature of the tissue, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, forward reflectance profile length from dark to light across the edges of luminosity or shadow edges, backward reflectance profile length from light to dark across the edges of luminosity, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on the object from the light source(s) of the shadow caster scanner, the angle of observation of the image capturing device(s) of the shadow caster scanner, optical parameters, light intensity values, normalized light intensity values, pixel intensity, normalized pixel intensity, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like.

[0013] Practically, the polarization of light also plays an important part in determining scattering characteristics of an object. Specular reflections help determine the nature of the very top surface of objects, most easily seen using horizontal polarization (S-polarized for a horizontal surface scanned). P-polarized light (vertical for a horizontal surface scanned), especially at Brewster’s angle, will experience at least partially repressed reflection, and more energy will enter the tissue. Polarized light may therefore be used to gain the proper reference surface, as well as obtain the most scattering information. Light that contains both S and P polarizations could be used in conjunction with a polarizer at the lens of the camera to perform polarimetry. It is important to note that the polarization from a sheet polarizer incorporated into, for example, a liquid crystal matrix or liquid crystal display (LCD), delivers slightly different polarization to a given point on a surface. That is, the optical path from a part of the line of lights to a point on the scanned surface has upon it a different projected orientation of the polarizer. These directions may be constrained by timing individual lights. In practice, however, the overall polarization is about the same.

[0014] Additionally, some embodiments of the present disclosure address recipes to build phantoms, which are used to train algorithms or artificial intelligence (Al) or for informing libraries of physical features relevant to material identification or tissue characterization, or which serve as standards, requiring knowledge of illumination wavelengths and the spectrum of the color sensors used.

[0015] In some embodiments, the present disclosure describes methodologies for identifying tissue types and other items in a 3D scan by analyzing scattering statistics derived from frames used to generate a three-dimensional data representation (or “3D reconstruction” or “point clouds”) of a scanned object. Various tissues and materials exhibit differing optical penetration lengths, and these properties are further modulated by distinct combinations of wavelengths. In some embodiments, the present disclosure describes determining the spatial profile of luminosity edges projected by a linear LED array in combination with a parallel shadow caster. By way of example, using the methods described herein, a penumbra extension of shadows cast onto an object can be quantitatively determined, which may then be used to infer effective optical penetration lengths. For example, regions with large penumbra sizes correspond to high opticalpenetration lengths, while regions with sharp shadow boundaries indicate shorter penetration lengths.

[0016] In some embodiments, the present disclosure describes three-dimensional data representations (or “3D reconstructions” or “point clouds”) generated from such frames. For example, the frames comprising the 3D scans generally possess sharp boundaries unless they encounter regions characterized by high optical penetration, such as translucent polymers or biological tissues like brain matter. In such cases, an enlarged penumbra results, causing observable noise in the 3D reconstruction. Therefore, optical penetration length can correlate with the noise levels present in the 3D scan. Additional contributors to scan noise include relative object motion, variations in light exposure, light collection efficiency, and the intrinsic noise characteristics of the camera sensor.

[0017] In some embodiments, the influence of extended penumbra profiles on 3D scan noise may be leveraged for two primary objectives. First, to discriminate between different tissue types based on inferred optical penetration lengths. Second, to estimate the thickness of a layer with a high optical penetration length overlying a layer characterized by a shorter penetration length.

[0018] By way of example, one immediate clinical application of this technology is to estimate the thickness of cartilage over the femur during total knee arthroplasty (TKA). Accurate knowledge of cartilage thickness is critical for effectively localizing the bone that can lay several millimeters below the scanned exterior cartilage surface. Bone localization is vital to inform navigated surgical systems, such as robots, to key locations for TKA, such as planes that define where to cut the knee. For example, the statistical analysis of noise in reconstructed 3D scans may be an indicator of cartilage thickness.

[0019] Various embodiments of the present disclosure relate generally to computer vision, graphics, image scanning, and image processing as well as associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to form at least three-dimensional models or images of objects and to characterize the objects or portions of the objects through analysis of physical features, such as subsurface scattering, across one or more edges of luminosity. In some embodiments, systemand method disclosed herein relates generally to characterizing an object by projecting a shadow across it, measuring various physical features across the shadow edge, and generating a characterization model of the object from the information. In some embodiments, the system and method disclosed herein relates generally to characterizing objects using a shadow caster scanner, which projects one or more edges of luminosity across an object, measures various physical features across the one or more edges of luminosity, and generates a characterization model of the object from the information. In addition, a collection of characterization models may be gathered in a look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like), which is then used to identify materials by comparing characterization models of materials being interrogated to known characterization models in the look-up table and either matching the characterization models or identifying an unknown material, which lacks a matching characterization model in the look-up table. In some embodiments, the shadow caster scanner comprises one or more light sources and one or more shadow casters with a shape with at least one edge being contained within a common plane, which contains said one or more light sources. In some embodiments, the system disclosed herein may perform spectral analysis of the sub-surface light, for example by comparing red light subsurface scattering to blue light subsurface scattering. In some embodiments, other light that may be compared include infrared light, polarized light, narrow band light, laser light, or fluorescent light. In some embodiments, the system disclosed herein may analyze the optical penetration depth to characterize tissue. In some embodiments, the system of the present disclosure may measure or determine physical features which are useful for characterizing materials or tissue, including but not limited to (and by way of example only) scatter width, surface curvature of the tissue, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on the object from the light source(s) of the shadow caster scanner, the angle of observation of the image capturing device(s) of the shadow caster scanner, optical parameters, light intensity values, normalized light intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features including but not limited to:vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. In some embodiments, methods for determining these physical features using a shadow caster scanner are disclosed. In some embodiments, the system of the present disclosure may be configured for use in endoscopes or microscopes to characterize tissue or differentiate between various materials. In some embodiments, the system and methods of the present disclosure may be particularly useful in identifying subsurface tumors, such as astrocytoma, or melanoma, which may be highly vascularized.

[0020] In some embodiments, the system disclosed herein comprises a light source; a shadow caster comprising a shape with at least one edge being contained within a common plane, which contains said light source; an image capture device; wherein said light source illuminates said shadow caster to project sharp shadows of known geometry, which form edges of luminosity on an object, said edges of luminosity comprising a shadowed side and an illuminated side; wherein said shadow caster projects said edges of luminosity across said object; wherein said image capture device captures images of said edges of luminosity on said object and records said images; wherein a data representation of the physical features of said object are generated from said recorded images by analyzing light scattered from said illuminated side of said edges of luminosity into said shadowed side of said edges of luminosity; and wherein a characterization model of said object is generated using said data representation. According to some embodiments, the shadow caster is physically moved, such as by actuators, in order to sweep said edges of luminosity across said object, and in other embodiments, the shadow caster comprises a liquid crystal matrix, which creates said edges by generating opaque regions on the liquid crystal matrix and projects said edges of luminosity across said object by triggering different opaque regions in sequence. In addition, some embodiments comprise a memory stored in non-transitory computer readable medium and a processor comprising said computer-readable medium; where in the processor controls said apparatus, method or system, stores said images in said memory and generates said data representation and characterization model.

[0021] In some embodiments, the present disclosure relates broadly to apparatuses, which physically move one or more shadow casters in order to move one or more edges of luminosityrelative to the objects or areas with materials or tissues being characterized by the present disclosure. This embodiment relates generally to an apparatus configured for characterizing one or more materials in an object, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer- readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources; one or more actuators, said actuators being capable of moving said one or more shadow casters; wherein said one or more light sources illuminate said one or more shadow casters to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; wherein said one or more actuators move said one or more shadow casters in order to sweep said one or more edges of luminosity across said object; wherein said one or more image capture devices capture one or more images of said one or more edges of luminosity on said object and record said one or more images into said memory; wherein said processor forms a three-dimensional data representation of said object from said recorded one or more images; and wherein said processor extracts physical features of said object from said one or more images using said three-dimensional data representation; and wherein said processor uses said physical features to characterize said one or more materials of said object. In this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color filters or polarization filters. The physical features may be selected from a group comprising: surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelialtissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described below. A method of determining normalized pixels intensities is also described below. Once a material is identified, the apparatus of this preferred embodiment is capable of generating one or more false- color models of said object and displaying said object with said one or more materials characterized with false colors.

[0022] In some embodiments, the present disclosure relates broadly to apparatuses which use liquid crystal matrices to generate the edges of the shadow casters in the shadow caster scanners configured for material identification or tissue characterization and differentiation. This embodiment relates generally to an apparatus for characterizing one or more materials in an object, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor, said processor comprising: said computer-readable medium; a controller, said controller being capable of interacting with said processor; one or more shadow casters, said one or more shadow casters comprising: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller and being capable of generating opaque regions or patterns, said opaque regions or said patterns comprising: a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources; wherein said one or more light sources illuminate said one or more shadow casters to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; wherein said processor uses said controller to generate a series of said opaque regions or said patterns on said transparent liquid crystal matrix in order to project said one or more edges of luminosity across said object; wherein said one or more image capture devices capture one or more images of said one or more edges of luminosity on said object and record said one or more images into said memory; wherein said processor forms a three-dimensional data representation of said object from said recorded images; wherein said processor extracts physical features of said object from said one or more images using said three-dimensional data representation; and wherein said processor uses said physical features to characterize said one or more materials of said object. In this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color filters or polarization filters. The physical features are selected from a group comprising: surface curvature of the tissue, scatter width, reflectanceprofile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described below. A method of determining normalized pixels intensities is also described below. Once a material is identified, the apparatus of this preferred embodiment is capable of generating one or more false- color models of said object and displaying said object with said one or more materials characterized with false colors.

[0023] In some embodiments, the present disclosure relates broadly to methods which physically move one or more shadow casters to move one or more edges of luminosity relative to the objects or areas with materials or tissues being characterized by the present disclosure. By way of example, this embodiment relates generally to a method for characterizing one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources; one or more actuators, said actuators being capable of moving said one or more shadow casters; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object by using said one or more actuators to move said one or more shadow casters; capturing one or more images of said one or more edges ofluminosity on said object using said one or more image capture devices; recording said one or more images into said memory; forming a three-dimensional data representation of said object from recorded said one or more images; extracting physical features of said object from said one or more images using said three-dimensional data representation and said processor; and characterizing said one or more materials of said object using said physical features. In this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color filters or polarization filters. The physical features are selected from a group comprising: surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described below. A method of determining normalized pixels intensities is also described below. Once a material is identified, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0024] In some embodiments, the present disclosure relates broadly to methods which use liquid crystal matrices to generate the edges of the shadow casters in the shadow caster scanners configured for material identification or tissue characterization and differentiation. By way of example, this embodiment relates generally to a method of characterizing one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor, said processor comprising: said computer-readablemedium; a controller, said controller being capable of interacting with said processor; one or more shadow casters, said one or more shadow casters comprising: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller and being capable of generating opaque regions or patterns, said opaque regions or said patterns comprising: a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources; generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor, illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object by generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices; recording said one or more images into said memory; forming a three-dimensional data representation of said object from recorded said images; extracting physical features of said object from said one or more images using said three- dimensional data representation and said processor; and characterizing said one or more materials of said object using said physical features. By way of example, in this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color fdters or polarization fdters. The physical features are selected from a group comprising: surface curvature of the tissue, scatter width, reflectance profde length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue,organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described below. A method of determining normalized pixels intensities is also described below. Once a material is identified, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0025] In some embodiments, the present dislosure relates broadly to methods of identifying materials or tissue, which use look-up tables of known materials and shadow caster scanners configured for material identification or tissue characterization and differentiation, according to some examples. By way of example, this embodiment relates generally to a method for characterizing one or more materials in an object, said method comprising: providing a look-up table, said look up table comprising: known optical characteristics of known materials; proving an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitoiy computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters; capturing images of said one or more edges of luminosity on said object using said one or more image capture devices, each said one or more images comprising: pixels; a central vertex, said central vertex being the center of each said one or more images; lit regions, said lit regions being illuminated by said one or more light sources; and shadow regions, said shadow regions comprising: said sharp shadows; and recording said one or more images into said memory; determining the wavelength range that produces the largest signal-to-noise ratio between said shadow regions and said lit regions; constructing a three-dimensional representation of said surface of said object using said recorded one or more images and said wavelength range; identifying a shadow frame, said shadow frame comprising: said recorded said images in which said one or more edges of luminosity lie across the viewing area of each pixel; defining a region of interest for each said central vertex, said region of interest comprising:a rectangular projection on said surface of said object for determining the optical characteristics of said central vertex; determining intensity values for each said central vertex of said recorded said one or more images; determining the shadow edge vector, said shadow edge vector lying along said one or more edges of luminosity; for each said central vertex, integrating said intensity values of neighboring vertices within said region of interest along said shadow edge vector to determine the light intensity profde, said neighboring vertices being said central vertex of each adjacent one or more images; determining the optical characteristics of said one or more materials associated with each said central vertex; and comparing said optical characteristics of said one or more materials associated with each said central vertex to said known optical characteristics of said known materials in said look-up table, thereby identifying said one or more materials of said object. The central vertex is defined as the pixel that lies upon the shadow edge in the center of each region of interest. This central vertex is the pixel (or voxel in 3D) location to which an optical characteristic value, or physical feature value, is assigned for each region of interest. Each central vertex is rastered throughout the whole image space, keeping the size of the region of interest (or volume of interest) in physical parameters the same. In some versions of this embodiment, determining said wavelength range that produces said largest signal-to-noise ratio between said shadow regions and said lit regions is acquired with prior knowledge. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment use liquid crystal matrices to generate the edge(s) of said one or more shadow casters and comprises a controller, said controller being capable of interacting with said processor; wherein said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of saidopaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color filters or polarization filters. The optical characteristics are selected from a group comprising: surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described below. A method of determining normalized pixels intensities is also described below. For defining a region of interest, methods of determining the axes of bounding box volumes and methods of determining bounding box volume dimensions are described below. In addition, methods of building reference libraries, such as a look-up table, and methods of using look-up tables are described below. Moreover, once a material is identified, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0026] In some embodiments, the present disclosure relates broadly to methods of constructing phantoms for testing and calibrating a material-identifying a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method of building a phantom, said method comprising: measuring out a first amount of silicone base, such as polydimethylsiloxane (PDMS) or the like, in a mixing container; measuring out a second amount of aluminum oxide in a weighing dish; adding said second amount of said aluminum oxide to said first amount of said silicone base in said mixing container; mixing said secondamount of aluminum oxide and said first amount of silicone base in said mixing container for ten minutes to form a first mixture; measuring out a third amount of India ink in said weighing dish; adding said third amount of India ink to said first mixture of said aluminum oxide and said silicone base in said mixing container; mixing said third amount of said India ink and said first mixture of said aluminum oxide and said silicone base in said mixing container for a minimum of ten minutes, or until the solution is homogeneous, to form a second mixture; measuring out a fourth amount of curing agent in said weighing dish; adding said fourth amount of said curing agent to said second mixture of said India ink, said aluminum oxide, and said silicone base in said mixing container; mixing said fourth amount of curing agent and said second mixture of said India ink, said aluminum oxide, and said silicone base for a minimum of ten minutes, or until the solution is homogeneous, to form a third mixture; exposing said third mixture of said curing agent, said India ink, said aluminum oxide, and said silicone base, to a vacuum pressure below -25 inHG for at least 25 minutes or until there are not more visible air bubbles coming to the surface of the mixture, so that any air bubbles may be removed from the viscous mixture without causing the water in the mixture to boil and evaporate due to the decrease in pressure; pouring said third mixture of said curing agent, said India ink, said aluminum oxide, and said silicone base, into a mold; curing said third mixture of said curing agent, said India ink, said aluminum oxide, and said silicone base, in said mold for at least twenty-four hours at room temperature, thereby forming said phantom; and removing said phantom from said mold. The composition ratios of aluminum oxide comprise 0-15 grams of aluminum oxide per 100ml of silicone base, such as PDMS, provides a positive linear correlation with the optical scattering property. The concentrations of India ink comprise 0-156 micrograms of India ink per gram of water, which has a positively correlated linear relationship with the optical scattering property. These embodiments are exemplary of the scope and spirit of the present disclosure; however, the above-described embodiments and examples should not limit the present disclosure, and those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein.

[0027] In some embodiments, the present disclosure relates broadly to methods of determining physical features or optical characteristics, namely the scatter width, or reflectance profile length, of materials or tissue being characterized by a shadow caster scanner, according to some examples. This embodiment relates generally to a method of determining the scatteringwidth, or reflectance profile length, of one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer- readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; recording said one or more images into said memory; and forming a three- dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: points; calculating the shadow plane normal at each of said one or more shadow caster positions, said shadow plane normal comprising: the normal to said common plane; calculating the surface normal vector at each of said points; defining the shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining a linear approximation of the local shadow edge using the normal to the pixel of interest, or central vertex as described above, and said shadow edge vector; determining the sample size and bounding volume for fitting data; computing the point distance between each of said points within said bounding volume and said linear approximation of said local shadow edge; normalizing said pixel intensities; creating a plot of data by plotting said point distance as a function of normalized said pixel intensities; defining knots across said plot; applying a spline fit to said plot using said knots; defining the shadow edge location by determining the steepest slope of said spline fit between said knots; creating a fit curve by fitting the sum of two exponentials to said data starting at said shadow edge location and going into the unilluminated region; and determining the reflectance profile length using saidfit curve, said reflectance profile length being the distance between said shadow edge location and the position of said fit curve at half of normalized said pixel intensities, or half-max, at said shadow location. Determining the steepest slope of said spline fit between said knots is best done at a wavelength at which the scattering is high, to ensure that the shadow is quite sharp, thereby providing an edge location that conforms to the actual surface. Other wavelengths, which may scatter less and therefore have longer profile lengths, may cause the observed shadow edge to be shifted relative to the true shadow edge. Alternatively, instead of using the half-max in some versions of this embodiment other metrics, such as 1 / e or l / e2, or the like, may be specified to characterize the fit exponents. The surface normal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In this preferred embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color filters or polarization filters. A method of determining normalized pixels intensities is also described below. For the sample size and bounding volume for fitting data, methods of determining the axes of bounding box volumes and methods of determining bounding box volume dimensions are described below. Moreover, once a reflectance profile length is determined, the apparatus ofthis preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0028] In some embodiments, the present disclosure relates broadly to additional methods of determining physical features or optical characteristics, namely the scatter width, or reflectance profile length, of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method of determining the scattering width, or reflectance profile length, of one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer- readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; one or more positive frames, said one or more positive frames being said one or more images showing a positive transition from a shadow region to an illuminated region across said one or more edges of luminosity; and one or more negative frames, said one or more negative frames being said one or more images showing a negative transition from said illuminated region to said shadow region across said one or more edges of luminosity; recording said one or more images into said memory; and forming a three- dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: points; calculating the shadow plane normal at each of one or more shadow caster positions, said shadow plane normal comprising: the normal to said common plane; calculating the surface normal vector at each of said points; defining the shadow edge vector along saidthree-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining a linear approximation of the local shadow edge using the pixel of interest and said shadow edge vector; determining the sample size and bounding volume for fitting data; computing the point distance between each of said points within said bounding volume and said linear approximation of said local shadow edge; normalizing said pixel intensities; creating a plot of data by plotting said point distance as a function of normalized said pixel intensities; smoothing said data using a symmetric averaging filter; creating an absolute difference curve by taking the absolute value of the difference between said positive frame and said negative frame; defining the shadow edge location by determining the location of the minimum value of said absolute difference curve; creating a fit curve by fitting the sum of two exponentials to said data starting at said shadow edge location and going into said sharp shadow; and determining the reflectance profile length using said fit curve, said reflectance profile length being the distance between said shadow edge location and the position of said fit curve at half of normalized said pixel intensities at said shadow location. The surface normal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In this preferred embodiment,said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color fdters or polarization fdters. A method of determining normalized pixels intensities is also described below. For the sample size and bounding volume for fitting data, methods of determining the axes of bounding box volumes and methods of determining bounding box volume dimensions are described below. Moreover, once a reflectance profile length is determined, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0029] In some embodiments, the present disclosure relates broadly to additional methods of determining physical features or optical characteristics, namely the scatter width, or reflectance profile length, of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method of determining the scattering width, or reflectance profile length, of one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer- readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; recording said one or more images into said memory; and forming a three-dimensional data representation of said three- dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: points; calculating the shadow plane normal at each of said one or more shadow caster positions, said shadow plane normalcomprising: the normal to said common plane; calculating the surface normal vector at each of said points; defining the shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining a linear approximation of the local shadow edge using the pixel of interest and said shadow edge vector; determining the sample size and bounding volume for fitting data; computing the point distance between each of said points within said bounding volume and said linear approximation of said local shadow edge; normalizing said pixel intensities; creating a plot of data by plotting said point distance as a function of normalized said pixel intensities; applying a spline fit to said plot; defining the shadow edge location by determining the steepest slope of said spline fit; creating a fit curve by fitting the sum of two exponentials to said data starting at said shadow edge location and going into said sharp shadow; and determining the reflectance profile length using said fit curve, said reflectance profile length being the distance between said shadow edge location and the position of said fit curve at half of normalized said pixel intensities at said shadow location. In some embodiments, the surface normal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In this preferred embodiment, said one or more materials maycomprise tissue. In addition, said one or more light sources may also comprise color fdters or polarization fdters. A method of determining normalized pixels intensities is also described below. For the sample size and bounding volume for fitting data, methods of determining the axes of bounding box volumes and methods of determining bounding box volume dimensions are described below. Moreover, once a reflectance profile length is determined, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0030] In some embodiments, the present disclosure relates broadly to additional methods of determining physical features or optical characteristics, namely the scatter width, or reflectance profile length, of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method of determining the scattering width, or reflectance profile length, of one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer- readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three- dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; recording said one or more images into said memory; and forming a three-dimensional data representation of said three- dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: points; calculating the shadow plane normal at each of said one or more shadow caster positions, said shadow plane normal comprising: the normal to said common plane; calculating the surface normal vector at each ofsaid points; defining the shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining a linear approximation of the local shadow edge using the pixel of interest and said shadow edge vector; determining the sample size and bounding volume for fitting data; computing the point distance between each of said points within said bounding volume and said linear approximation of said local shadow edge; normalizing said pixel intensities; creating a plot of data by plotting said point distance as a function of normalized said pixel intensities; applying a spline fit to said plot; defining the shadow edge location by determining the steepest slope of said spline fit; and determining the reflectance profile length starting at said shadow edge location and going into said sharp shadow. In some embodiments, the surface normal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color filters or polarization filters. A method of determining normalized pixels intensities is also described below. For the sample size and bounding volume for fitting data, methods of determining the axes of bounding box volumes and methods of determining bounding box volume dimensions are described below.Moreover, once a reflectance profile length is determined, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0031] In some embodiments, the present disclosure relates broadly to additional methods of determining physical features or optical characteristics, namely the scatter width, or reflectance profile length, of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method of determining the scattering width, or reflectance profile length, of one or more materials in an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer- readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; one or more positive frames, said one or more positive frames being said one or more images showing a positive transition from a shadow region to an illuminated region across said one or more edges of luminosity; and one or more negative frames, said one or more negative frames being said one or more images showing a negative transition from said illuminated region to said shadow region across said one or more edges of luminosity; recording said one or more images into said memory; and forming a three- dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: points; calculating the shadow plane normal at each of one or more shadow caster positions, said shadow plane normal comprising: the normal to said common plane; calculatingthe surface normal vector at each of said points; defining the shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining a linear approximation of the local shadow edge using the pixel of interest and said shadow edge vector; determining the sample size and bounding volume for fitting data; computing the point distance between each of said points within said bounding volume and said linear approximation of said local shadow edge; normalizing said pixel intensities; creating a plot of data by plotting said point distance as a function of normalized said pixel intensities; smoothing said data using a symmetric averaging filter; creating an absolute difference curve by taking the absolute value of the difference between said positive frame and said negative frame; defining the shadow edge location by determining the location of the minimum value of said absolute difference curve; determining the reflectance profile length starting at said shadow edge location and going into said sharp shadow. The surface normal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In this embodiment, said one or more materials may comprise tissue. In addition, said one or more light sources may also comprise color fdters or polarization fdters. A method of determining normalized pixelsintensities is also described below. For the sample size and bounding volume for fitting data, methods of determining the axes of bounding box volumes and methods of determining bounding box volume dimensions are described below. Moreover, once a reflectance profile length is determined, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0032] In some embodiments, the present disclosure relates broadly to additional methods of determining physical features or optical characteristics, namely normalized pixel intensities, of materials or tissue being characterized by a shadow caster scanner, according to some examples. This embodiment relates generally to a method for normalizing pixel intensities of an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer- readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said object with said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said object and said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor; defining a maximum frame, said maximum frame being one of said one or more images showing a region without a shadow cast onto it; defining a minimum frame, or region of non-illumination, for each said shadow caster position, said minimum frame being one of said one or more images in which said sharp shadows cast by said one or more shadow casters cover least 5 mm on each side of said one ormore edges of luminosity, or a range or distance that is relatively long as compared to the profde length; and normalizing said pixel intensities in each of said one or more images using minimum-maximum feature scaling by scaling said minimum frame and said maximum frame or by scaling the difference between said minimum frame and said maximum frame. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. Said one or more light sources may also comprise color filters or polarization filters. A method of determining normalized pixels intensities is also described below. Additionally, once pixel intensities are normalized, the apparatus of this preferred embodiment is capable of generating one or more false-color models of said object and displaying said object with said one or more materials characterized with false colors.

[0033] In some embodiments, the present disclosure relates broadly to methods of determining axes of a bounding box volume used for identifying physical features or optical characteristics of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method for determining axes of a bounding box volume on an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capturedevices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising: one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; one or more positive frames, said one or more positive frames being said one or more images showing a positive transition from an unilluminated region to an illuminated region across said one or more edges of luminosity; and one or more negative frames, said one or more negative frames being said one or more images showing a negative transition from said illuminated region to said unilluminated region across said one or more edges of luminosity; recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: points, said points comprising: coordinate information, three-dimension coordinate information, color or wavelength information, associated normal information, associated profile length, or the like, or a combination of any of this information; calculating the shadow plane normal at each of one or more shadow caster positions, said shadow plane normal comprising: the normal to said common plane; calculating the surface normal vector at each of said points; defining the shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining the normal to the shadow edge along said three-dimensional surface by cross multiplying said surface normal vector with said shadow edge vector at each of said points; defining a local coordinate system of said bounding box volume at each point, said local coordinate system comprising: an X direction of said bounding box volume, said X direction comprising: said shadow edge vector; a Y direction ofsaid bounding volume, said Y direction comprising: said normal to said shadow edge; and a Z direction of said bounding volume, said Z direction comprising: said surface normal vector; and defining the dimensions of the bounding volume to have practical limits for each use case by balancing resolution and noise. The surface normal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. Said one or more light sources may also comprise color filters or polarization filters.

[0034] In another preferred embodiment, the present disclosure relates broadly to methods of determining bounding box dimensions used for identifying physical features or optical characteristics of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method for determining bounding box dimensions of an object, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at leastone edge, said edge being contained within a common plane, which contains said one or more light sources; aligning said apparatus to said object, said aligning said apparatus comprising: a scene; determining the maximum expected reflectance profile length, said maximum expected reflectance profile length comprising: the maximum distance light is scattered into shadow; setting the side lengths of a bounding box, said setting the side lengths of said bounding box comprising: setting a Z length, said Z length comprising: said maximum expected reflectance profile length in said scene in the Z direction described in the preceding paragraph; setting a Y length, said Y length comprising: 2.5 times said maximum expected reflectance profile length in said scene in said Y direction described in the preceding paragraph; setting an X length, said X length comprising: the minimum value that allows for at least 100 points per wavelength of light per distance in millimeters in said Y length in said X direction described in the preceding paragraph. In some versions of this embodiment, determining said maximum expected reflectance profile length comprises using prior knowledge. In other versions of this embodiment, determining said maximum expected reflectance profile length comprises: scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: scenes; recording said one or more images into said memory; and identifying said maximum expected reflectance profile length. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein saidscanning the surface of said object with said apparatus comprises: generating said opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. Said one or more light sources may also comprise color fdters or polarization fdters.

[0035] In some embodiments, the present disclosure relates broadly to methods of determining polarization in a scan of an object for use in identifying physical features or optical characteristics of materials or tissue being characterized by a shadow caster scanner, according to some examples. By way of example, this embodiment relates generally to a method for determining polarization in a scan of an object, said method comprising: scanning the three- dimensional surface of said object; calculating the surface normal vector at each 3D coordinate; determining local coordinate system at each three-dimension coordinate of said three- dimensional surface of said object, said local coordinate system comprising: a local XZ plane; projecting the polarization vector of light onto a projection on said local XZ plane; and extracting P-polarization and S-polarization components from said projection. In some versions of this embodiment, said scanning said three-dimension surface of said object comprises: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices; recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising: 3D coordinates. The surfacenormal vector may be calculated by using a covariance analysis algorithm, a similar method, or the like. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. Said one or more light sources may also comprise color filters or polarization filters. These embodiments are exemplary of the scope and spirit of the present disclosure; however, the above-described embodiments and examples should not limit the present disclosure, and those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein.

[0036] In some embodiments, the present disclosure relates broadly to methods of building and using a look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like) of known tissue types characterized by a shadow caster scanner of the present disclosure, according to some examples. By way of example, this embodiment relates generally to a method / apparatus of building and using a look-up table of known tissue types, said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one ormore shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning multiple tissue samples of the same tissue type with said apparatus, said scanning comprising: illuminating said tissue samples with said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said tissue samples; projecting said one or more edges of luminosity across said tissue samples using said one or more shadow casters; capturing one or more two-dimensional images of said tissue samples and said one or more edges of luminosity on said tissue samples using said one or more image capture device, said two-dimensional images comprising: said physical features of said tissue samples; and recording said one or more two-dimensional images into said memory; labeling known tissue types in said one or more two- dimensional images; creating a statistical characterization of said physical features of said tissue samples (such as a histogram, or the like); repeating said labeling and said scanning until said physical features of said tissue samples are well distributed in said statistical characterization or sampled with statistical significance within the relevant parameter space of physical features; analyzing said two-dimensional images for reflectance profile lengths; training a classification algorithm on said known tissue types using all relevant said physical features, thereby building said look-up table; using trained said classification algorithm to identify unknown tissue being scanned by said apparatus by comparing said physical features of said unknown tissue types to said physical features in said look-up table. In some versions of this embodiment, said one or more light sources may also comprise color filters or polarization filters. The physical features are selected from a group comprising: surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels,nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described above. A method of determining normalized pixels intensities is also described above. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said tissue samples using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said tissue samples with said apparatus comprises: generating said opaque regions on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said tissue samples using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In other versions of this embodiment, said classification algorithm may identify said unknown tissue as being unknown, wherein said physical features of said unknown tissue are not found in said look-up table.

[0037] In some embodiments, the present disclosure relates broadly to methods of building and using a patient-specific look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like) of known patient-specific tissue types characterized by a shadow caster scanner of the present disclosure, according to some examples. By way of example, this embodiment relates generally to a method of building a patient-specific look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like), said method comprising: providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing saidmaterials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the surface of patient-specific tissue with said apparatus, said scanning comprising: illuminating said patient-specific tissue with said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said patient-specific tissue; projecting said one or more edges of luminosity across said patient-specific tissue using said one or more shadow casters; capturing one or more two-dimensional images of said patient-specific tissue and said one or more edges of luminosity on said patient-specific tissue using said one or more image capture device, said two-dimensional images comprising: physical features of said patientspecific tissue; and recording said one or more two-dimensional images into said memory; labeling known tissue types in said one or more two-dimensional images; creating a statistical characterization of said physical features of said patient-specific tissue (such as a histogram, statistical analysis, or the like); repeating said labeling and said scanning until said physical features of said patient-specific tissue are well distributed in said statistical characterization; analyzing said two-dimensional images for reflectance profile lengths; training a classification algorithm on said known tissue types using all relevant said physical features, thereby building said look-up table; testing unlabeled portions of said two-dimensional images in order to identify unknown patient-specific tissue using trained said classification algorithm by comparing said physical features of said unknown patient-specific tissue to said physical features in said look-up table. In some versions of this embodiment, said one or more light sources may also comprise color filters or polarization filters. The physical features are selected from a group comprising: surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are alsouseful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. The reflectance profile length may be determined by one of several methods described above. A method of determining normalized pixels intensities is also described above. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said patient-specific tissue using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said tissue samples with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said patient-specific tissue using said one or more shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. In other versions of this embodiment, said classification algorithm may identify said unknown tissue as being unknown, wherein said physical features of said unknown tissue are not found in said look-up table. Similarly, the ability to identify patient-specific healthy tissue may be used to guide a surgeon or robotic surgery system away from patient-specific healthy tissue, thereby protecting healthy tissue, so that only patient-specific pathological tissue is subject to operation.

[0038] In some embodiments, the present disclosure relates broadly to methods of using a look-up table to identify materials in an object using a shadow caster scanner of the present disclosure, according to some examples. By way of example, this embodiment relates generally to a method of using a look-up table to identify materials in an object, said method comprising:providing a well -characterized optical phantom; building a look-up table of scatter widths using said well-characterized optical phantoms; if applicable, down-selecting from said look-up table to fit a specific application, such as different surgery types (for example, a lookup table may include bone, gray matter, and white matter; however, if gray matter is not expected in the scan, it may be removed from the relevant lookup table preventing bone, which resembles white matter for some wavelengths, from being confused for white matter); scanning the surface of said object; determining the sample size and region size for fitting data; determining the reflectance profile lengths; comparing said reflectance profile lengths to those in said look-up table; and identifying candidate materials by matching said reflectance profile lengths to those in said look-up table. Methods of constructing optical phantoms are described above. Methods of building and using reference libraries, or look-up tables, are also described above. Some versions of this embodiment use shadow caster scanners to scan the surface of an object, in which said method comprises providing an apparatus, said apparatus comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising: said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising: a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources. Some versions of this embodiment use physical shadow casters in which said apparatus of said method comprises: one or more actuators, said actuators being capable of moving said one or more shadow casters; and wherein said projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises: using said one or more actuators to move said one or more shadow casters. Other versions of this embodiment further comprise a controller, said controller being capable of interacting with said processor, and use liquid crystal matrices to generate the edge(s) of said one or more shadow casters wherein in said shadow casters of said apparatus of said method comprise: a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by said controller using said processor and being capable of generating opaque regions or patterns; wherein said scanning the surface of said object with said apparatus comprises: generating said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor; and wherein said projecting said one or more edges of luminosity across said object using said one ormore shadow casters comprises: generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor. Methods of determining the sample size and region size for fitting data are described above. The reflectance profile length may be determined by one of several methods described above. Said one or more light sources may also comprise color filters or polarization filters. In still other versions of this embodiment, said classification algorithm may identify said unknown tissue as being unknown, wherein said physical features of said unknown tissue are not found in said look-up table.

[0039] In some embodiments, the present disclosure relates broadly to methods of differentiating between materials forming an object, according to some examples. By way of example, this embodiment relates generally to a method for differentiating between materials forming an object, comprising: forming an initial three-dimensional data representation of an object from recorded images of edges of luminosity projected onto said object, the initial three- dimensional data representation comprising a plurality of data points; processing said initial three-dimensional data representation to generate a refined surface reconstruction of said object; performing statistical analysis of said plurality of data points of the initial three-dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction; using said average normal distances of said plurality of data points to infer optical penetration values of materials forming said object; and displaying, on a display device, a visual representation of said optical penetration values superimposed on at least one of a scan image of said object, the initial three-dimensional data representation of said object, and said refined surface reconstruction of said object, said visual representation comprising a first visual indicator corresponding to low optical penetration values and a second visual indicator corresponding to high optical penetration values; and identifying different materials forming said object based upon said visual representation. In some embodiments the step of forming an initial three-dimensional data representation of an object from recorded images of one or more edges of luminosity on an object comprises: projecting sharp shadows of known geometry to form said one or more edges of luminosity on said object using at least one light source and at least one shadow casting element, said at least one shadow casting element being capable of generating opaque regions, said opaque regions comprising a shape with at least one edge, said edge being contained within a plane, which contains said at least one light source;generating a series of said opaque regions on said shadow casting element to project a pattern of said one or more edges of luminosity across said object; capturing images of said one or more edges of luminosity on said object with one or more image capture devices; and forming said initial three-dimensional data representation from said recorded images. In some embodiments, the at least one light source is discrete or continuous. In some embodiments, the at least one light source is linear. In some embodiments, the at least one light source comprises one or more array of lights. In some embodiments, the shape of the at least one shadow casting element is based on the object. In some embodiments, the at least one shadow casting element further comprises color filters. In some embodiments, the one or more shadow casting elements comprises a liquid crystal matrix. In some embodiments, the three-dimensional data representation is displayed on said display device. In some embodiments, the step of capturing images of said one or more edges of luminosity on said object comprises capturing images of said one or more edges of luminosity on said object using one or more image capture devices. In some embodiments, the method comprises the step of generating a three-dimensional model of said object using said three-dimensional data representation. In some embodiments, the one or more shadow casters comprises at least one of a configurable opacity, a variable opacity, and a selectable opacity. In some embodiments, the plurality of data points includes a plurality of supra-surface data points and a plurality of subsurface data points. In some embodiments, the step of performing statistical analysis of said plurality of data points of the initial three- dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction comprises, for each point on the mesh: creating a cylinder aligned with the mesh normal; calculating a normal distance for each supra-surface point; and calculating a normal distance for each subsurface point.

[0040] In some embodiments, the present disclosure relates broadly to methods of determining thickness of one or more layers of material forming an object, according to some examples. By way of example, this embodiment relates generally to a method of determining thickness of a layer of material forming an object, comprising: forming an initial three- dimensional data representation of an object from recorded images of edges of luminosity projected onto said object, the initial three-dimensional data representation comprising a plurality of data points; processing said initial three-dimensional data representation to generate a refined surface reconstruction of said object; performing statistical analysis of said plurality of datapoints of the initial three-dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction; using said average normal distances of said plurality of data points to infer optical penetration values of material layers forming said object; and comparing said inferred optical penetration values of said material layer of said object with a collection of inferred optical penetration values that were previously correlated with measured thickness of layers of similar material derived from similar objects to determine the thickness of said material layer of said object. In some embodiments, the step of forming an initial three-dimensional data representation of an object from recorded images of one or more edges of luminosity on an object comprises: projecting sharp shadows of known geometry to form said one or more edges of luminosity on said object using at least one light source and at least one shadow casting element, said at least one shadow casting element being capable of generating opaque regions, said opaque regions comprising a shape with at least one edge, said edge being contained within a plane, which contains said at least one light source; generating a series of said opaque regions on said shadow casting element to project a pattern of said one or more edges of luminosity across said object; capturing images of said one or more edges of luminosity on said object with one or more image capture devices; and forming said initial three-dimensional data representation from said recorded images. In some embodiments, the at least one light source is discrete or continuous. In some embodiments, the at least one light source is linear. In some embodiments, the at least one light source comprises one or more array of lights. In some embodiments, the shape of the at least one shadow casting element is based on the object. In some embodiments, the at least one shadow casting element further comprises color filters. In some embodiments, the one or more shadow casting elements comprises a liquid crystal matrix. In some embodiments, the step of performing statistical analysis of said plurality of data points of the initial three-dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction comprises, for each point on the mesh: creating a cylinder aligned with the mesh normal calculating a normal distance for each supra-surface point; and calculating a normal distance for each subsurface point.

[0041] In sum, the system and related methods for the characterization of objects through subsurface scattering across one or more edges of luminosity disclosed herein provides a multitude of efficient and novel solutions for scanning objects and characterizing their physicalfeatures and optical characteristics in order to identify, characterize, distinguish between, and differentiate, materials and / or tissue in said objects, including identifying whether a material or tissue is unknown, by analyzing subsurface scattering of light measured across one or more edges of luminosity by a shadow caster scanner configured for material interrogation, according to various embodiments of the present disclosure.

[0042] In broad embodiment, the present disclosure relates generally to apparatuses, methods, and systems for characterizing objects using shadow caster scanners, which project one or more edges of luminosity across an object, measure subsurface scattering information across the one or more edges of luminosity to generate three-dimension representation of the object, to determine various physical features of the object, and generate a characterization model of the object from the information. These embodiments are not intended to limit the scope of the present disclosure.

[0043] Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described disclosure techniques. The disclosed examples are illustrative and not restrictive. These embodiments are not intended to limit the scope of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Many advantages of the present disclosure will be apparent to those skilled in the art with a reading of this specification in conjunction with the attached drawings, wherein like reference numerals are applied to like elements and wherein:

[0045] FIG. l is a depiction of a prior art scanner system;

[0046] FIG. 2 is a diagram depicting an example of a shadow caster, according to some embodiments;

[0047] FIG. 3 is a diagram depicting a shadow caster scanning system, according to some embodiments;

[0048] FIG. 4 is a diagram depicting another example of a shadow caster scanning system, according to some embodiments;

[0049] FIG. 5A is a diagram depicting a shadow caster scanning system of the present disclosure, which uses LCD shadow casters, according to some embodiments;

[0050] FIG. 5B is a perspective view of a physical shadow caster scanner of the present disclosure, according to some embodiments;

[0051] FIG. 6 is a diagram depicting the scattering of light in object, according to some embodiments;

[0052] FIG. 7 is a diagram depicting a side view of a shadow caster casting a sharp shadow on an object being scanned, according to some embodiments;

[0053] FIG. 8 is a diagram depicting light being imaged by a lens to form an edge of high contrast, according to some examples of the prior art;

[0054] FIG. 9 is a diagram depicting examples of sharp shadow edges cast by a shadow caster of the present disclosure, according to various embodiments, along with two graphs describing the geometric shadow edges and determined shadow edges;

[0055] FIG. 10 is a diagram and graph depicting the intensity of light across a shadow caster of the present disclosure, according to some embodiments;

[0056] FIG. 11 is a diagram and graph depicting light scattering within an asymmetric scattering region, according to some embodiments;

[0057] FIG. 12 is two sets of diagrams and graphs depicting the intensity of light across a shadow caster of the present disclosure for two different tissue types, according to some embodiments;

[0058] FIG. 13 is two diagrams depicting light scattering within a scattering region with a short reflectance profile and light scattering within a scattering region with a long reflectance profile, according to some embodiments;

[0059] FIG. 14 is two diagrams depicting examples of a sharp shadow edge, or edge of luminosity, cast by a shadow caster of the present disclosure on two different materials, tape and grey matter of a brain, along with two graphs describing the observed shadow edges for each material, according to some embodiments;

[0060] FIG. 15 is two diagrams depicting examples of a sharp shadow edge, or edge of luminosity, cast by a shadow caster of the present disclosure on two different types of tissue, white matter and grey matter of a brain, along with four graphs describing the observed shadow edges for each material, according to some embodiments;

[0061] FIG. 16 is a diagram depicting a shadow with sharp edges of luminosity moving across an object and describing relevant shadow and surface geometries of the present disclosure, according to some embodiments;

[0062] FIG. 17 is a diagram depicting light scattering in objects with different surface curvature, convex and concave, according to some embodiments;

[0063] FIG. 18 is a flow chart describing a process of identifying the material(s) of an object using a shadow caster scanner of the present disclsoure, according to some embodiments;

[0064] FIG. 19 is a flow chart describing a process of building a phantom for training and testing material identification using a shadow caster scanner of the present disclosure, according to some embodiments;

[0065] FIG. 20 is a flow chart describing a process of determining the scattering width, or reflectance profile length, of a material using a shadow caster scanner of the present disclosure, according to some embodiments;

[0066] FIG. 21 is a flow chart describing another process of determining the scattering width, or reflectance profile length, of a material using a shadow caster scanner of the present disclosure, according to some embodiments;

[0067] FIG. 22 is a flow chart describing another process of determining the scattering width, or reflectance profile length, of a material using a shadow caster scanner of the present disclosure, according to some embodiments;

[0068] FIG. 23 is a flow chart describing another process of determining the scattering width, or reflectance profile length, of a material using a shadow caster scanner of the present disclosure, according to some embodiments;

[0069] FIG. 24 is a flow chart describing a process of normalizing pixel intensities using a shadow caster scanner of the present disclosure, according to some embodiments;

[0070] FIG. 25 is a graph of raw data of the normalized intensity of light as a function of distance across a sharp shadow edge, or edge of luminosity, of a shadow caster scanner of the present disclosure, according to some embodiments;

[0071] FIG. 26 is a graph of raw data of the normalized intensity of light as a function of distance across a sharp shadow edge, or edge of luminosity, of a shadow caster scanner of the present disclosure, which shows a spline fit curve fit to spline knots, according to some embodiments;

[0072] FIG. 27 is a graph of raw data of the normalized intensity of light as a function of distance across a sharp shadow edge, or edge of luminosity, generated by a shadow caster scanner of the present disclosure, showing a fit curve starting at the shadow edge and going into the shadow, according to some embodiments;

[0073] FIG. 28 is a graph of the normalized intensity of light as a function of distance across a sharp shadow edge, or edge of luminosity, generated by a shadow caster scanner of the present disclosure, showing a positive frame in which the light goes from dark to light across the shadow edge, a negative frame in which the light goes from light to dark across the shadow edge, and the absolute difference between the frames, for measuring the reflectance profile length using a shadow caster scanner, according to some embodiments;

[0074] FIG. 29 is a graph of the normalized intensity of light as a function of distance across a sharp shadow edge, or edge of luminosity, generated by a shadow caster scanner of the present disclosure, showing a positive frame in which the light goes from dark to light across the shadow edge, a negative frame in which the light goes from light to dark across the shadow edge, and the absolute difference between the frames, and depicting anisotropic measurements of reflectanceprofile length using a shadow caster scanner, with the measurement of reflectance profile length differing depending on the type of frame, according to some embodiments;

[0075] FIG. 30 is a flow chart describing an example process for determining bounding box axes using a shadow caster scanner of the present disclosure, according to some embodiments;

[0076] FIG. 31 is a diagram describing directional definitions of a bounding box, or region of interest, used with a shadow caster scanner of the present disclosure, according to some embodiments;

[0077] FIG. 32 is a flow chart describing an example process for determining bounding box dimensions used with a shadow caster scanner of the present disclosure, according to some embodiments;

[0078] FIG. 33 is a flow chart describing an example process for determining polarization using a shadow caster scanner of the present disclosure, according to some embodiments;

[0079] FIG. 34 is a flow chart describing an example process for creating a look-up table for artificial intelligence training and testing, and identifying materials or tissue using a shadow caster scanner of the present disclosure, according to some embodiments;

[0080] FIG. 35 is a flow chart describing an example process for creating a look-up table for patient- specific artificial intelligence training and testing, and identifying patient-specific tissue using a shadow caster scanner of the present disclosure, according to some embodiments;

[0081] FIG. 36 is a flow chart describing an example process of using a look-up table to identify materials or tissue using a shadow caster scanner of the present disclosure, according to some embodiments;

[0082] FIG. 37 is an example diagram showing three graphs of the normalized intensity of red and blue light as a function of distance across a sharp shadow edge generated by a shadow caster scanner of the present disclosure for three different tissue types, according to some embodiments;

[0083] FIG. 38 is an example graph of reflectance profile length, which was measured by a shadow caster scanner of the present disclosure, as a function of oxygenation percentage, according to some embodiments;

[0084] FIG. 39 is an example diagram showing four graphs of reflectance profile length as a function of the angle of incidence of light, which illustrate how angle of measurement affects reflectance profile at bottom and top sharp shadow edges, or edges of luminosity, generated by a shadow caster scanner of the present disclosure, according to various embodiments;

[0085] FIG. 40 is an example diagram depicting the angle of observation relative to the angle of incidence of a light source for a shadow caster scanner of the present disclosure, according to some embodiments;

[0086] FIG. 41 is a series of graphs, which show the reflectance profile length as a function of the angular position of a shadow caster scanner’s camera for various angles of incidence of light across a bottom sharp shadow edge and top sharp shadow edge, according to some embodiments;

[0087] FIG. 42 is a series of images of a knee matrix showing statistics used to classify tissue, and a series of three graphs showing the average reflectance profile length as a function of the angle of incidence of light of the knee matrix for red, green, and blue light, according to some embodiments;

[0088] FIG. 43 is a series of images depicting the angle of incidence, intensity, reflectance profile length, and graphs showing the average reflectance profile length as a function of the angle of incidence of light of an injured finger and of a healthy finger, along with regions of interest comparing the injured finger to the healthy finger and identifying inflammation in the injured finger, according to some embodiments;

[0089] FIG. 44 is a diagram illustrating an example patient-non-specific process of training data and identifying pathological tissues, according to some embodiments;

[0090] FIG. 45 is a diagram illustrating an example patient-specific process of training data and identifying pathological tissues, according to various embodiments;

[0091] FIG. 46 is an example diagram with two graphs showing that reflectance profile lengths are sensitive to tissue type: a graph of the reflectance profile length as a function of optical parameter, and a graph of the predicted reflectance profile length as a function of optical wavelength for three tissue types, according to some embodiments;

[0092] FIG. 47 is a flowchart illustrating various steps of a method of analyzing scan noise to infer optical penetration lengths (or "noise analysis method"), according to some embodiments;

[0093] FIG. 48 is an example diagram demonstrating a noise pattern resulting from a 3D scan of a cadaver brain, including a a top-down view of a cadaver brain and a cross- sectional view of the generated point cloud which illustrates the generated point clouds in relation to the depth within the scanned section of cadaver brain, according to some embodiments;

[0094] FIG. 49A-C are graphic representations of sample density as a function of reconstructed depth in the point cloud for a given wavelength range, according to some embodiments;

[0095] FIG. 50 is a block diagram showing an example of how scattered light can be interpreted as vertex with subsurface material below a surface, according to some embodiments;

[0096] FIG. 51 is a block diagram showing an example of how, in the presence of scattering, signal associated with several shadow-casting edges can contribute to points along a single view direction, according to some embodiments;

[0097] FIG. 52 is an example diagram showing an example scan image of a surface of an object (e.g., a bone), and a sectional view of the object, showing a distribution of points around the surface, according to some embodiments;

[0098] FIG. 53 is an example diagram showing a reconstructed point cloud of a knee with an average cartilage thickness of 0.5 mm, and a reconstructed point cloud of a knee 5302b with average cartilage thickness of 1.5mm, according to some embodiments; and

[0099] FIG. 54 is an example diagram illustrating noise analysis of a near infrared scan of a knee (for example) that includes surrounding tissue, according to some embodiments.DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

[0100] Illustrative embodiments of the disclosure are described below. In the interest of clarity, not all features of an actual implementation are described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions must be made to achieve the developers’ specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure. The system and related methods for the characterization of objects through subsurface scattering across one or more edges of luminosity disclosed herein boasts a variety of inventive features and components that warrant patent protection, both individually and in combination.

[0101] For the purpose of illustration, the present disclosure is shown in the preferred embodiments of apparatuses, methods, and systems, for noninvasively scanning objects or regions in three-dimensions, measuring the properties of light scattered across extremely sharp shadows, or edges of luminosity, and characterizing the nature of the material(s) in objects or on the surface of objects, including differentiating between different types or tissue or identifying known and unknown tissue type. In some embodiments, the present disclosure relates generally to an apparatus, method and system for characterizing an object by projecting a shadow across it, measuring various physical features across the shadow edge, and generating a characterization model of the object from the information. In some embodiments, the present disclosure relates generally to apparatuses, methods, and systems for characterizing objects using shadow caster scanners, which project one or more edges of luminosity across an object, measure subsurface scattering information across the one or more edges of luminosity to generate three-dimension representation of the object, to determine various physical features of the object, and generate a characterization model of the object from the information. In some embodiments the present disclosure relates generally to apparatuses, systems, and methods which identify, characterize,differentiate, and distinguish materials in objects, including tissue of animals and plants, using the analysis of the physical features of light scattered out of the subsurface of objects or areas being scanned by shadow caster or structured light scanners. These embodiments are not intended to limit the scope of the present disclosure.

[0102] By way of example, some embodiments use physical shadow caster scanners, which move the shadow caster to pass edges of luminosity across an object being scanned for material interrogation, and other embodiments use LCD shadow casters, which generate a series of opaque regions in sequence to project edges of luminosity across an object being scanned for material interrogation. Other embodiments include methods of determining physical features of an object using a shadow caster scanner configured for material interrogation. Additional embodiments include methods of building phantoms and methods of training and using a look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like) for material identification and characterization. Various embodiments or examples may be implemented in numerous ways, including as a system, a process, a method, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. These embodiments are not intended to limit the scope of the present disclosure.

[0103] A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples but is not limited to any particular example. The scope is limited only by the claims, and numerous alternatives, modifications, and equivalents thereof. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description.

[0104] Referring now to the preferred embodiments of the present disclosure, FIG. 2 is a diagram 200 depicting an example of a basic shadow caster, according to some embodiments. Basic shadow caster diagram 200 depicts an example of a shadow caster 215 configured to form an edge of luminosity 250a and 250b at or upon a plane of projection or object (not shown) or environment (not shown) to facilitate three-dimensional representations of the shape and image of an object or environment, as well as to identify the material or to differentiate between types of materials or tissues in the object. In some examples, shadow caster 215 may be configured to receive photonic emission (e.g., as light) that may impinge on at least edge portions 211a and 211b of edge 213a of shadow caster 215, which, in turn, may cause projections 204a and 204b of light originating from edge portions 211a and 21 lb to form a sharp edge of luminosity 250a on plane of projection 210. Similarly, light may also impinge on edge portions 21 laa and 21 Ibb of edge 213b, which, in turn, may cause projections 204aa and 204bb of light originating from edge portions 21 laa and 21 Ibb to form another sharp edge of luminosity 250b. According to various examples, either edge of luminosity 250a or edge of luminosity 250b, or both, may be used to facilitate three-dimensional scanning and digital replication and material identification. In the example shown, shadow caster 215 may be opaque to form an umbra 220 based on the edges of luminosity 250a and 250b. Umbra 220 may be associated with relatively high degrees of darkness (e.g., low to negligible levels of illumination) relative to illuminated portions 299 of plane 210, including illuminated plane portion 228.

[0105] In view of the foregoing, shadow caster 215 may be implemented in accordance with various functions and / or structures described herein, to form edges of luminosity to facilitate three-dimensional scanning and digital replication of spatial characteristics associated with surfaces of objects and environments, as well as material interrogation. According to some examples, a shadow caster 215 includes a triangular cross-sectional area that provides a triangular profde, in projection, onto the plane Y-Z, which casts a sharp shadow with each edge maintaining parallelism to line 212 throughout a scan, where that sharp shadow is projected onto any plane parallel to line 212. That is, parallelism of one or both edges to line 212 may be maintained as projected onto plane 210 during a scan (e.g., when one or both edges of luminosity 250a and 250b move over an object, environment, and / or plane of projection 210). The geometries and dimensions of shadow caster 215, basic shadow caster light source 203, and an edge of luminosity 250a (or edge of luminosity 250b) facilitates maintenance of parallelism as,for example, one or more of the edges of luminosity move during a scanning process. As an angle of shadow caster 215 may be known a-priori, the parallelism may be maintained as one or more edges of luminosity used in scanning to facilitate accuracy in determination of a shadow plane, which, in turn, may improve accuracy of the coordinates of a 3D object and allow for different materials or tissues to be identified and characterized in said 3D object. In at least one example, shadow caster 215 may be implemented to form shadows planes that are parallel to line 212 traversing through basic shadow caster light source 203 at point L and apex 262 of shadow caster 215, for either edge 213a or 213b, or both. An example of a shadow plane is formed with points L, A, and B, which contains both basic shadow caster light source 203 and edge of luminosity 250a, and an example of a second shadow plane is formed with points L, C, and D, which contains both basic shadow caster light source 203 and edge of luminosity 250b. Thus, edge of luminosity 250a between points A and B may be maintained as being parallel to (or substantially parallel to) edge of luminosity 250b between points C and D, according to some examples. The Note that line 212 traversing through basic shadow caster light source 203 need not traverse through shadow planes in accordance with at least one example. In other examples, line 212 is parallel to the shadow plane, which is extendable to line 212. However, a shadow may not necessarily be cast along this line by a shadow caster.

[0106] Edge of luminosity 250a, for example, may be associated with a relatively sharp rate of change from an absence (or relatively low amounts) of reflected light or photonic emissions in umbra 220 (e.g., relatively low levels of brightness or luminosity) to relatively high levels of reflected light or photonic emissions at an illuminated plane portion 228 within a unit distance 226 at shadow caster distance 219. According to some examples, edge of luminosity 250a may be described as being associated with a gradient indicating unit distance 226. Characteristics of pixels may include, but are not limited to, pixel intensities, such as gray pixel intensities, values of brightness, luminosity, etc. In one example, a gradient may specify a distance at which one or more pixel characteristics of associated umbra 220 change from pixel value 000 (e.g., no illumination, or “black”) to a pixel value 255 (e.g., fully illuminated, or “white”). In at least some cases, a cross-sectional area associated with shadow caster 215 may produce sharper edges of luminosity and higher contrast than, for example, a cylindrical rod or pencil where such rod or pencil is disposed such that no shadow-casting edge lies entirely in a single plane containing the light source, according to at least some examples. In other words, any edge that lies entirely in asingle plane, which also contains the basic shadow caster light source 203, casts a shadow with sharp, high contrast edges. These high contrast, sharp edges are particularly well suited for allowing measurements of light scattered in the subsurface of an object across said edges, and this ability to measure subsurface scattered light is a particular advantageous in performing material identification and differentiation, as well as tissue characterization, in the embodiments of the present disclosure.

[0107] In some examples, an edge of luminosity may sufficiently provide for relatively sharp contrasts between illuminated surfaces and a generated shadow. As such, examples of edge of luminosity may facilitate capture of spatial characteristics of 3D surfaces as well as color associated with the surfaces where that color may be obtained from the illuminated surface closest to the shadow edge. Therefore, a color determination may be obtained relatively close to an edge of luminosity during a scan for accurately representing a color during a scan than otherwise might be the case. For example, determining a color need not rely on a co-registration of 3D data with separate color information, which may be obtained using a separate camera or at a different time than when data representing 3D information is scanned or otherwise captured. Additionally, further analysis of colors associated with the surface and analysis of subsurface scattering across the extremely sharp edge of luminosity allow for material (or tissue) characterization and distinction in the embodiments of the present disclosure.

[0108] Referring still to FIG. 2, basic shadow caster diagram 200 depicts a basic shadow caster light source 203 being disposed in a region associated with a negative X-plane (e.g., “-X”) portion of plane of projection 210, with shadow caster 215 (or a projection thereof) being disposed in a plane (e.g., Y-Z plane). A portion 260 of shadow caster 215 may be disposed at or adjacent to a line 212. Line 212 may also include basic shadow caster light source 203 positioned thereon. In at least one example, portion 260 may be coextensive with line 212. In one example, line 212 may coincide with one or more points of shadow caster 215, which may include a point at an apex 262 of a triangular-shaped shadow caster 215 shown in basic shadow caster diagram 200. Line 212 may be parallel to the X-Y plane and orthogonal to the Y-Z plane, at least in some cases. Another portion of shadow caster 215 may be disposed distally, such as at end portion 230. For example, end portion 230 may be disposed at or adjacent to plane of projection 210. In the examples of a linear light source, the light source is continual along line 212.

[0109] In some examples, the depiction of shadow caster 215 may represent a cross-sectional area, or a projection thereof, in association with a plane (e.g., Y-Z plane) that may form edges of luminosity 250a and 250b. Alternatively, shadow caster 215 (or a cross-sectional area thereof) may be positioned or oriented at an angle relative to a plane (e.g., at an angle 280 relative to a plane coextensive to an X-Y plane). Hence, structures and functions of shadow caster 215 need not be limited to that depicted and described in relation to FIG. 2. For example, a rectangular shadow caster may be implemented with one or more features, functions and / or structures described herein, such as one or more sources of light 203 (e.g., points of light), whereby the rectangular shadow caster may be rotated about a point on its edge (e.g., about a rotation axis parallel to line 212) to form at least one relatively sharp shadow edge (or edge of luminosity). Shadow caster 215 may be opaque, with the opacity being configurable or programmable, according to some examples. As another example, the shadow caster may be formed by opaque regions on a liquid crystal matrix, which projects shadows across plane of projection 210 by generating additional opaque regions, or patterns of opaque regions, in sequence at different locations relative to the basic shadow caster light source 203. Note that, in some examples, a penumbra may be implemented as umbra 220, whereby a partial amount of illumination from basic shadow caster light source 203 (or any other light source) may modify or limit a maximum of darkness (e.g., a partial amount of illumination may cause an increase in values of pixel intensities above 000, which may represent total darkness). Regardless, edge of luminosity 250a and 250b may be detected as a transition from a first range of one or more pixel values associated the umbra 220 to a second range of one or more pixel values associated with an illuminated portion 228 of plane of projection 210, according to some examples. According to some examples, a transition may be detected or determined in a single frame in which adjacent pixels may be compared. Or, a transition may be determined as a change in brightness of a pixel over time (e.g., over multiple frames). In at least one instance, an edge of luminosity (or shadow) may be resolved at dimensions finer than a pixel (e.g., during one or more frames in which a pixel value may change relatively slowly as a shadow edge moves across a pixel during a scan). Thus, an edge of luminosity may be determined at subpixel accuracy.

[0110] FIG. 3 is a diagram depicting a scanning system, according to some examples.Scanner diagram 300 depicts another example of a scanner shadow caster 315 as a constituent component of a scanning system also including an image capture device 301 and one source oflight 303 or multiple sources of light 303 (not shown) disposed on light line 312. Light line 312 may extend through scanner apex 362 of scanner shadow caster 315 and one or more sources of light 303. In some examples, scanner shadow caster 315 may be configured to receive photonic emission (e.g., as light) that may impinge on at least scanner edge portion 311a and 31 lb of scanner edge 313a of scanner shadow caster 315, which, in turn, may cause scanner projections 304a and 304b, respectively, of light originating from scanner edge portions 311a and 31 lb to form a scanner edge of luminosity 350a on scanner plane of projection 310. Similarly, light may also impinge on scanner edge portions 31 laa and 31 Ibb of scanner edge 313b, which, in turn, may cause scanner projections 304aa and 304bb of light originating from scanner edge portions 31 laa and 31 Ibb to form another scanner edge of luminosity 350b. The one or more scanner edges of luminosity 350a and 350b may be formed at or upon a scanner plane of projection 310 to facilitate generation of three-dimensional representations of a shape of a scanner object 370.

[0111] According to various functions and structures, a scanner edge of luminosity 350a and 350b may transit or move over a surface of scanner object 370 to determine three-dimensional spatial characteristics of the surface. Any number or type of motive force (not shown) may be generated by a device (not shown), such as an electromechanical motor, or by gravity, to move one of scanner shadow caster 315 and scanner object 370 relative to the other to effectuate movement of scanner edge of luminosity 350 relative to scanner object 370. For example, a motive force may cause angular displacement of scanner shadow caster 315 in a plane (e g., Y-Z plane) (e.g., rotation 384 that has at least some rotational component about an axis parallel to light line 312). In some examples, above-described parallelism may be maintained so as provide parallel edges of luminosity that move (e.g., in synchronicity) throughout a scan by rotating scanner shadow caster 315 about scanner apex 362 of FIG. 3. Similarly, shadow caster 215 of FIG. 2 may rotate about apex 262 to maintain parallelism. Note that width of bottom portion 331 (e.g., in the Y-axis direction) may be depicted as equivalent to a width of one or more squares of a checkerboard pattern depicted in scanner diagram 300. But here, or in any other example described herein, the width of bottom portion 331 may be smaller or larger than a width of any number of checkerboard squares. Thus, dimensions of scanner shadow caster 315 shown in scanner diagram 300 are exemplary. Any number of configurations and widths may be used to form any distance between parallel scanner edges of luminosity 350a and 350b, among various examples.

[0112] To implement a scan, an angular displacement of scanner shadow caster 315 in the Y- Z plane may cause scanner edge of luminosity 350 and scanner umbra 320 to move in a direction 380 parallel to, for example, a Y-axis and over scanner plane of projection 310. As another example, a motive force may cause scanner shadow caster 315 to translate (e.g., non- rotationally) in an orientation shown along the Y-axis to cause scanner edge of luminosity 350a and 350b and scanner umbra 320 to move in a direction 380. In yet another example, a motive force may cause scanner object 370 to rotate 382 or translate 383 (e.g., linear displacement parallel to Y-axis) relative to scanner shadow caster 315 to cause scanner edge of luminosity 350a and 350b to contact different portions of scanner object 370 at different points in time. In another example, a motive force may cause scanner object 370 to move relative to scanner shadow caster 315 to cause motion of an edge of luminosity.

[0113] In some examples, a motive force may cause one of sources of light 303, scanner shadow caster 315, and scanner object 370 to move relative to the others to effectuate movement of scanner edge of luminosity 350a and 350b. Note that the motive force on light source 303 or scanner shadow caster 315 may be any type of motive force, examples of which include, but are not limited to, mechanical, electromechanical, electrical, magnetic, electromagnetic, electronic (e.g., currents or voltages to activate elements of a LCD to effectuate motion of a simulated scanner shadow caster 315), or any other motive force. Further, a device that generates a motive force need not be limited to an electromechanical motor, but may be gravity or any known device to cause movement of scanner edge of luminosity 350 relative to a surface of scanner object 370.

[0114] Image capture device 301 may be configured to capture images of a scene or environment that includes scanner object 370 as scanner edge of luminosity 350a and 350b travels or moves over scanner plane of projection 310. Examples of image capture device 301 may include any type of camera, such as a digital video camera, a charge-coupled device (“CCD”)-based image sensor, etc., as well as analog cameras. In the example shown, image capture device 301 may capture one or more frames of images (e.g., video at a particular frame rate) in which a one or more pixels 373 may be associated scanner edge of luminosity 350a and 350b as the shadow (e.g., scanner umbra 320) is projected over scanner object 370. One or more pixels 373 may be pixels on a camera corresponding to a point on scanner object 370, which isdepicted as one or more pixels 373. In this example, image capture device 301 can capture for a given edge of luminosity a change in reflected luminosity from either darkness to brightness, or brightness to darkness. The surface of scanner object 370 may cause a portion of scanner edge of luminosity 350a and 350b (e.g., the portion casted upon scanner object 370) to deviate from other straighter line portions of scanner edge of luminosity 350a and 350b (e g., on the X-Y plane) as detected from a point of view of camera 301. The deviation or deformation of scanner edge of luminosity 350a and 350b may be due to surface dimensions (of scanner object 370) extending in positive values of the Z-axis. In at least one implementation, a single image capture device 301 (e.g., with a single lens) may be sufficient to implement at least some of the scanning functions described herein.

[0115] FIG. 4 is a diagram depicting another example of a shadow caster, according to some embodiments. System diagram 400 depicts a system of multiple system shadow casters 415a and 415b configured to form one or more common edges of luminosity 450 at or upon a system plane 410 of projection to facilitate three-dimensional object scanning and material interrogation. System diagram 400 also depicts an arrangement in which multiple system shadow casters 415a and 415b may be configured to cast system edges of luminosity 451 and 453 to be coincident with each other to form common edges of luminosity 450. System diagram 400 also depicts a system image capture device 401, a first subset 403a of one or more light sources, and a second subset 403b of one or more light sources. First subset 403a of one or more light sources are shown to be disposed in first region 430 (e.g., on one side of system shadow caster 415a), and second subset 403b of one or more light sources may be disposed in second region 434. First region 430, central region 432, and second region 434 may define two-dimensional or three- dimensional space. The light sources of first subset 403a and second subset 403b may be disposed axially on a light source line 412, and may be any type of light-generating source that may emit any amount of lumens (e.g., 200 lumens, or less, to 1300 lumens, or greater). Examples of light-generating sources may include, but are not limited to, LED, incandescent, halogen, laser, etc., as well as any type of light pipe, lens (e.g., Fresnel lens), or light guide, such as illuminated optical fibers (e.g., fiber optic, such as fiber optic cables). Each light source in first subset 403a and second subset 403b may emit photon emissions (e.g., light) at a same or different wavelength. For example, one or more light sources in each of first subset 403a and second subset 403b may generate light in the visible light spectrum, as well as other any range ofspectra (e.g., ultraviolet spectra, infrared spectra, etc.), and may emit at a relatively narrow spectral range. One or more ranges of wavelengths may be selectably implemented as a function of an application of multiple system shadow casters 415a and 415b. In some cases, light sources of first subset 403a and second subset 403b can be implemented to emit wavelengths of light that constitute “white light” or “broad bandwidth light,” which may reduce or negate effects of diffraction at edges of a shadow caster (e.g., one or more ranges of wavelengths, in combination, may reduce or negate artifacts associated with light diffracting due to an edge). Due to the speckle effects of coherent laser light adding to noise, there are disadvantages of using laser light sources; however, the signal-to-noise ratio afforded by using filters to block ambient light while recording the narrow-band light of the laser light source may be advantageous. Also, light sources of first subset 403a and second subset 403b can implement any number of ranges of wavelengths regardless of whether those ranges are in the visible spectra. Light sources of subsets 403a and 403b may be configured to emit light omnidirectionally, unidirectionally, or in any other pattern of light.

[0116] In some cases, light sources in first subset 403a and second subset 403b may be relatively narrow or approximate points of light, and / or may have a reduced (or relatively short) radial dimension (“r”) 499 about light source line 412 to, for example, effectuate a relatively sharp transition from “light” to “dark” along common edges of luminosity 450. As a number of sources (e.g., relatively narrow sources) of light increases along a length (“L”) 407 of a portion of light source line 412, system edge of luminosity 453 generated by system shadow caster 415b may sharpen (e.g., increase a rate of transition from umbra or shadowed area 420 in central region 432 to an illuminated portion of system plane 410 of projection.) In some examples, sources of light, such as second subset 403b, may be disposed at greater distances 490 from the system shadow caster 415b to sharpen system edges of luminosity 453. Similarly, any number of sources of light may be disposed in first subset 403a along a corresponding portion of light source line 412 to generate an enhanced system edge of luminosity 451 in association with system shadow caster 415a. In at least one example, a filament (e.g., in a halogen light bulb) may be used to function as a number point sources of light disposed in first subset 403a such that they form a continuous set. A radius of a halogen bulb or filament, or any other light source described herein, may be referred to as a subset of light sources describing a “narrow source” of light of radial dimension “r” 499, at least in some examples.

[0117] According to some examples, system shadow caster 415a may be configured to receive photonic emissions (e.g., from first subset 403a of one or more light sources) at edge portions to form at least two portions of system edges of luminosity 451. At least two portions of edges of luminosity 451 may be parallel or substantially parallel (e.g., non-intersecting on system plane 410) to each other as projected on system plane 410. System shadow caster 415b may be configured to receive photonic emissions (e.g., from subset 403b of one or more light sources) at edge portions to form at least two portions of system edges of luminosity 453. At least two portions of system edges of luminosity 453 may be parallel or substantially parallel to each other as projected onto system plane 410.

[0118] System edges of luminosity 453 may coincide coextensive (or substantially coextensive) with system edges of luminosity 451 to form common edges of luminosity 450 based on system shadow casters 415a and 415b. Thus, system shadow caster 415b may form system edges of luminosity 453 to bolster system edges of luminosity 451 (e.g., adjacent system shadow caster 415b), and similarly, system shadow caster 415a may form system edges of luminosity 451 to bolster system edges of luminosity 453 (e.g., adjacent system shadow caster 415a). A bolstered system edge of luminosity 453 may provide for a relatively sharp shadow for a parallel shadow, according to at least one example.

[0119] Common edges of luminosity 450 may translate, in synchronicity, over system plane 410 as system shadow casters 415a and 415b have a common component of rotation about light source line 412 as an axis, where light source line 412 may be maintained to extend along first subset 403a and second subset 403b of light sources, and to system apexes 462a and 462b of system shadow casters 415a and 415b, respectively. In other examples, system shadow casters 415a and 415b and first subset 403a and second subset 403b of light sources may translate together with some component along the Y axis, for example along first system line 431 and second system line 433, respectively. In other examples, system shadow casters 415a and 415b and first subset 403a and second subset 403b of light sources may rotate together while maintaining a common light source line 412. In such a case, common edges of illumination 450 need not lie along a single axis (e.g., such as an X-axis depicted in FIG. 4). In other examples, system shadow casters 415a and 415b and first subset 403a and second subset 403b may all translate and / or rotate in unison while maintaining a common light source line 412.

[0120] Referring now to FIG. 5A, FIG. 5A is a diagram depicting a shadow caster scanner configured to characterize the material of an object or to distinguish between different tissues, which generates shadow casters using a liquid crystal matrix or liquid crystal display (LCD) according to some embodiments of the present disclosure. LCD system diagram 500 depicts an LCD shadow caster scanner system 510, which comprises an LCD system light source 503; an LCD system shadow caster 502 comprising an LCD system panel 505 that is transparent and capable of generating opaque regions 515, which are usually stripes or patterns; an LCD system camera (or “image capture device”) 501; and a processor with a memory stored in non-transitory computer-readable medium (not shown). The LCD system light source 503 is linear and continuous, and the opaque regions 515 generated by the LCD system shadow caster 502 comprise at least one LCD system edge 507a, 507b that is contained within an LCD system shadow plane 517a, 517b, which contains the LCD system light source 503. During scanning, the LCD panel 505 generates opaque regions 515 by making transparent regions 519 opaque; the LCD system light source 503 illuminates the opaque regions 515 and transparent regions 519 of LCD panel 505 to project LCD system sharp shadows 520 upon an LCD system object 530 with LCD system edges of luminosity 550a, 550b within minimal LCD system scattering regions 526a, 526b; the LCD system shadow caster 502 sequences different opaque regions 515 of the LCD panel 505 by making the earlier opaque regions 515 transparent and making subsequent transparent regions 519 opaque in sequence in order to project the LCD system edges of luminosity 550a, 550b across an LCD system object 530; and the LCD system camera 501 captures images of the LCD system edges of luminosity 550a, 550b and the LCD system object 530 as the edges or patterns are projected across the object 530 and stores the images in the memory in order to facilitate three-dimensional scanning, and material (or tissue) characterization, distinction, and / or identification. In some versions of this embodiment, multiple LCD system light sources 503, multiple LCD system shadow casters 502, and / or multiple LCD system cameras 501 may also be used.

[0121] The construction details of the disclosure as shown in FIG. 5A, are as follows. The LCD system camera 501 comprises camera, a digital video camera, a complementary metal- oxi de- semi conductor (“CMOS”) device, a charge-coupled device (“CCD”), an image sensor, an analog camera, a microscope camera, an endoscope camera, or the like. The LCD system shadow caster 502 comprises a liquid crystal matrix, LCD display, a polarizer, or the like. LCD systemlight source 503 comprises an incandescent light, a halogen light, fluorescent light, a linear light, a slitted tube light, an LED, an array of LEDs, a linear array of LEDs, different colored light sources, colored LEDs, lasers, an X-ray source, a UV source, an infrared source, polarizers, or the like. The LCD panel 505 comprises a transparent liquid crystal matrix, liquid crystal display, or the like. The LCD shadow caster scanner system 510 comprises a camera, a video camera, a linear light source, a liquid crystal matrix, LCD display, a housing, and / or the like. The opaque regions 515 comprise areas in a liquid crystal matrix, which block light. The transparent regions 519 comprise areas in a liquid crystal matrix, which allow light to pass through. The LCD system object 530 comprise any object or region with a material or tissue to be interrogated, identified, characterized, distinguished, differentiated, identified as unknown, or the like. A processor and a memory may be configured to be used with any of the above construction details. These materials are exemplary of the scope and spirit of the present disclosure; however, the abovedescribed embodiments and examples should not limit the present disclosure, and those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein.

[0122] Referring now to FIG. 5B, FIG. 5B depicts a physical shadow caster scanner system 560 configured to characterize the material of an object or to distinguish between different tissues, which generates sharp shadows using a physical shadow caster 575 and sweeps edges of luminosity across an item being scanned by physically moving the physical shadow caster 575, according to some embodiments of the present disclosure. Physical shadow caster scanner system 560 comprises a housing 562, a physical light source 563, which is linear and continuous, a physical shadow caster 575 comprising physical edges 567a, 567b, which are contained in common planes that also contain the physical light source 563, an actuator 570, a physical camera or image capture device 561, and a processor with a memory stored in non-transitory computer-readable medium (not shown). During scanning, the physical light source 563 illuminates physical shadow caster 575 to project sharp shadows, or edges of luminosity, across an object being scanned, the actuator 570 causes the shadow caster 575 to move, thereby projecting edges of luminosity across the subject being scanned, and the physical camera 561 captures images of the object and edges of luminosity as the edges sweep across the object and stores the images in the memory in order to facilitate three-dimensional scanning, and material (or tissue) characterization, distinction, and / or identification. In some versions of thisembodiment, multiple physical light sources 563, multiple physical shadow casters 575, and / or multiple physical cameras 561 may also be used.

[0123] The construction details of the disclosure as shown in FIG. 5B, are as follows. In some embodiments, the physical shadow caster scanner system 560 comprises a housing 562, a linear light source 563, a camera 561, a moveable shadow caster 575, an actuator 570, or the like. The housing 562 comprises a strong rigid material, such as steel, copper cladding, plastic, high density plastic, silicone, PVC, fiber glass, carbon fiber, composite material, metal, galvanized steel, stainless steel, aluminum, brass, copper, wood, or other like material. The physical camera 561 comprises camera, a digital video camera, a charge-coupled device (“CCD”), an image sensor, an analog camera, a microscope camera, an endoscope camera, or any other suitable image capture device. The physical light source 563 comprises an incandescent light, a halogen light, fluorescent light, a linear light, a slitted tube light, an LED, an array of LEDs, a linear array of LEDs, different colored light sources, colored LEDs, lasers, an X-ray source, a UV source, an infrared source, polarizers, or the like. The actuator 570 comprises a motor, an engine, a linear stepper motor, an electric motor, a hydraulic system, or the like. The physical shadow caster 575 may be any object with at least one edge that falls in the common plane and that can create shadows in the source, and may comprise a strong rigid material, such as steel, copper cladding, plastic, high density plastic, silicone, PVC, fiberglass, carbon fiber, composite material, metal, galvanized steel, stainless steel, aluminum, brass, copper, wood, or other like material, and may further comprise configurable shapes, three-dimensionally-printed shapes, configurable opacity, such as liquid crystal, or the like, or various colored filters. These materials are exemplary of the scope and spirit of the present disclosure; however, the above-described embodiments and examples should not limit the present disclosure, and those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein.

[0124] Referring now to FIG. 6, as background for how the present disclosure characterizes a material, in particular tissue 645, using scattered light, FIG. 6 shows a scattering diagram 600, which depicts how light scatters in typical tissue 645. For the purposes of the present disclosure, scattering is the process in which light is deflected or diffused as it propagates through a material. Different tissues or materials scatter light differently. Further, white light has multiplewavelength and frequencies of light, which scatter differently within a given material or tissue, and the present disclosure takes advantage of these different scattering responses to aid in identifying or characterizing different materials or tissues. In addition, some versions of the present disclosures use relatively narrow bands for each individual color, which provides advantages for the signal -to-noise ratio in ambient light situations. Incidentally, because whitelight LEDs use a fluorescent layer (which broadens the light source through scattering), the narrow-band individual-color LEDs are geometrically smaller, and therefore cast a sharper shadow. In scattering diagram 600, incident light 610 propagates through the air 640 and strikes the typical surface 647 of a typical tissue 645. Some of the incident light 610 is reflected as specularly reflected light 615, and some of the incident light 610 passes through the typical surface 647 of a typical tissue 645 and propagates through the subsurface 649 of the typical tissue 645. Some of the light in the subsurface 649 becomes lost / absorbed light 630 and does not escape the subsurface 649. Some of the light in the subsurface 649, low scattering light 625, experiences multiple scattering and absorption within the typical tissue 645 and exits the subsurface 649 as first scattered light 620. Other light in the subsurface 649, high scattering light 635, experiences more instances of multiple scattering and absorption within the typical tissue 645 and exits the subsurface 649 as second scattered light 622. Note that in the scattering diagram 600, all scattered light 620, 622 and specularly reflected light 615 is shown going in the same direction because the figure only depicts light, which is collected by a lens. The present disclosure uses the different scattering responses of incident light 610 within the subsurface 649 of typical tissue 645 to identify and characterize tissue.

[0125] Referring now to FIG. 7 and FIG. 8, FIG. 7 shows a shadow caster diagram 700, which depicts a side view of a basic shadow caster 715 of the present disclosure casting sharp shadows 720 on the basic surface 710 of a basic object 745 being scanned and describes the basic angle of incidence 719 at an edge of luminosity 750 on the basic surface 710 of a basic object 745 for the purposes of three-dimensional scanning and material characterization, according to some embodiments. In shadow caster diagram 700 of FIG. 7, a light source 703 shines light that is interrupted by the basic shadow caster 715. In preferred embodiments, the light source 703 is linear, and the basic shadow caster 715 comprises at least one basic edge 707, which shares a common plane, the shadow edge plane 751, with the light source 703. The basic shadow caster 715 casts sharp shadows 720 of known geometry onto the basic surface 710 of thebasic object 745. The intersection of the shadow edge plane 7 1 and the basic surface 710 of the basic object 745 is a basic edge of luminosity 750. Generally, the basic geometric shadow edge 755 is defined as the geometric edge drawn from the basic edge 707 of the basic shadow caster 715 to the basic object 745, along the line that extends from the light source 703 to the basic edge 707 of the basic shadow caster 715. In the presence of light scatter, the measured basic edge of luminosity 750 may differ from the model geometric shadow edge 755. The basic angle of incidence 719 is defined as the angle between the shadow edge plane 751 and a perpendicular line 716, or normal line, extending from the basic surface 710 of the basic object 745 at the basic edge of luminosity 750. Because the position of the light source 703 and basic edge 707 of the basic shadow caster 715 are known quantities in a shadow caster scanner of the present disclosure, and because the three-dimensional scanning of the basic object 745 measures the position and shape of the basic surface 710 of the object, the basic angle of incidence 719 may be easily determined by the present disclosure. In contrast to the basic shadow caster 715 shown in FIG. 7, FIG. 8 shows a lens diagram 800 that describes how a high contrast edge 853 is typically achieved in the prior art using an imaging lens 840. In lens diagram 800 of FIG. 8, a light source (not shown) shines light through an imaging lens 840 in order to form imaged light 812 or pattern-generated LCD, which is focused on the surface 810 of an object 845 as a high contrast edge 853. However, problems arise when attempting to determine the lens angle of incidence 819a, 819b. When a lens perpendicular line 816, or normal line, is extended from the lens surface 810 of the lens object 845 at the high contrast edge 853, a range of lens angles of incidence 819a, 819b is found and an exact angle is difficult to determine, which is not well suited for three- dimensional scanning or material characterization. Further, high contrast edges 853 formed by imaging lens 840 are highly sensitive to focus shifts and are also not well suited for three- dimensional scanning or material characterization. The present disclosure addresses the problem of a large range of lens angles of incidence 819a, 819b found in imaged lens system in FIG. 8 by using a lensless shadow caster system as described in FIG. 7.

[0126] Referring now to FIG. 9, FIG. 19, FIG. 30, and FIG. 32, FIG. 9 shows a series of diagrams and graphs, which describe the ideal conditions of a light / shadow edge on an optical property phantom for the purposes of three-dimensional scanning and material characterization. (See FIG. 19 below for the process of building a phantom.) Dark-to-light diagram 900a shows a bounding box 940 across a dark-to-light edge of luminosity 950a, which is a sharp, high contrasttransition from a dark shadow 920 to an illuminated area 927 of an optical property phantom generated by a shadow caster scanner of the present disclosure. (See FIG. 30 and FIG. 32 for methods involving bounding boxes or volumes.) Dark-to-light graph 901a describes the brightness in the bounding box 940 of dark-to-light diagram 900a as a function of distance from the dark shadow 920 to the illuminated area 927. In dark-to-light graph 901a, the ideal horizontal axis 974 shows the distance in pixels from the top to the bottom of the bounding box 940, and the ideal vertical axis 973 shows the gray value measured in the bounding box 940 with 0 gray value being the darkest value (black) and 255 being the brightest value (white.) The dark-to-light brightness curve 930a shows the measured intensity of brightness in the bounding box 940 from near zero gray value to near maximum gray value with the dark-to-light basic observed shadow edge 952a falling within transition between minimum and maximum gray values. Generally, the ideal geometric shadow edge 951a, 951b is defined as the geometric edge drawn from the edge of a shadow caster to an object being scanned, along the line that extends from the light source to the edge of the shadow caster. In the presence of light scatter, the basic observed shadow edge 952a, 952b may differ from the ideal geometric shadow edge 951a, 951b. Even though the shadow caster system of the present disclosure provides extremely high contrast and sharp edges of luminosity, unless the penumbra is narrower than the imaging extent of a single pixel, there is still some gradual transition between the dark shadow 920 and the illuminated area 927 in the basic observed shadow edge 952a, 952b. For the purposes of material or tissue characterization and measuring scattered light, it is useful to use a dark-to-light basic determined shadow edge 975a, which is located at the peak slope of the dark-to-light brightness curve 930a. The dark-to- light ideal geometric shadow edge 951a is marked with a dotted line in dark-to-light graph 901a. Light-to-dark diagram 900b depicts bounding box 940 across a light-to-dark edge of luminosity 950b, which is a sharp, high contrast transition from an illuminated area 927 of an optical property phantom to a dark shadow 920 generated by a shadow caster scanner of the present disclosure. Light-to-dark graph 901b describes the brightness in the bounding box 940 of light- to-dark diagram 900b as a function of distance from the illuminated area 927 to the dark shadow 920. In light-to-dark graph 901a, the ideal horizontal axis 974 shows the distance in pixels from the top to the bottom of the bounding box 940, and the ideal vertical axis 973 shows the gray value measured in the bounding box 940 with 0 gray value being the darkest value (black) and 255 being the brightest value (white.) (These values are examples only, and other enumerationmay be used describe pixel illumination values or gray values.) The light-to-dark brightness curve 930b shows the measured intensity of brightness in the bounding box 940 from near maximum gray value to near zero gray value with the light-to-dark basic observed shadow edge 952b falling within transition between maximum and minimum gray values. Even though the shadow caster system of the present disclosure provides extremely high contrast and sharp edges of luminosity, unless the penumbra is narrower than the imaging extent of a single pixel, there is still some gradual transition between the illuminated area 927 and the dark shadow 920 in the light-to-dark basic observed shadow edge 952b. For the purposes of material or tissue characterization and measuring scattered light, it is useful to use a light-to-dark basic determined shadow edge 975b, which is located at the most negative slope of the negatively-sloped light-to- dark brightness curve 930b.

[0127] Referring now to FIG. 9 and FIG. 10, FIG. 10 shows a tissue illumination diagram 1000, which represents viewed light from tissue 1045 being scanned by a shadow caster scanner according to some embodiments of the present disclosure. In tissue illumination diagram 1000, scanner incident light 1012 illuminates the illuminated shadow caster 1015 and tissue 1045. Illuminated shadow caster 1015 blocks scanner incident light 1012 making shaded tissue 1049. Scanner incident light 1012 illuminating the surface 1010 of the tissue 1045 penetrates into illuminated tissue 1047 where it is scattered by the tissue 1045. Different tissues scatter light differently, and the present disclosure takes advantage of this fact to aid in characterizing tissue. As described above, the shadow caster edge 1007 of the illuminated shadow caster 1015 is configured to share a plane with the source (not shown) of scanner incident light 1012, and casts a sharp, high contrast observed shadow edge 1050 upon the tissue surface 1010 of the tissue 1045 and the sharp edge of luminosity penetrates into the tissue 1045. Generally, the geometric shadow edge 1051 is defined as the geometric edge drawn from the shadow caster edge 1007 of the illuminated shadow caster 1015 to tissue 1045 being scanned, along the line that extends from the light source (not shown) to the shadow caster edge 1007 of the illuminated shadow caster 1015. In the presence of light scatter, the observed shadow edge 1050 may differ from the geometric shadow edge 1051. Intensity graph 1070 depicts the intensity of light across the illuminated edge 1050, or edge of luminosity, as a function of position with the horizontal axis 1074 showing the position of intensity along the surface 1010 and the vertical axis 1073 showing the magnitude of intensity. The determined shadow edge 1052 within the observed shadow edge1050 is located at the peak slope of the intensity curve 1030, as described above in FIG. 9. Depending on the position of the shadow caster edge 1007 relative to the light source, scanner incident light 1012 illuminates the tissue 1045 at slightly different angles of incidence (not indicated); however, because the position of the shadow caster edge 1007 of the illuminated shadow caster 1015 and the source of scanner incident light 1012 are known quantities with the present disclosure, these angles of incidence are all known as well. The geometric shadow edge1051 and the determined shadow edge 1052 may be distinguishable with the determined shadow edge 1052 being useful for material identification or tissue characterization in the present disclosure. Due to light diffusion, the determined shadow edge 1052 is only an estimation of the geometric shadow edge 1051, and it may shift due to the angle of the scanner incident light 1012 or the observation angle.

[0128] Referring now to FIG. 6 and FIG. 11, as background for how the present disclosure characterizes different tissues or materials using scattered light, FIG. 11 shows an asymmetric scattering diagram 1100, which illustrates how light scatters in an asymmetric scattering region 1146 within regular tissue 1145. This asymmetry results from the propensity for light to scatter in a more forward direction, but also by the angle of incidence of the light on the surface as well as the angle of observation. In asymmetrical scattering diagram 1100, regular incident light 1110 propagates through regular air 1140 and strikes the asymmetric surface 1143 of an asymmetric scattering region 1 146 within regular tissue 1145. Some of the regular incident light 1 110 is reflected as asymmetric specularly reflected light 1115, and some of the regular incident light 1110 passes through the asymmetric surface 1143 of an asymmetric scattering region 1146 within regular tissue 1145 and propagates through the asymmetric subsurface 1149 of the asymmetric scattering region 1146 within regular tissue 1145. Some of the light in the asymmetric subsurface 1149 becomes asymmetric lost / absorbed light 1130 and does not escape the asymmetric subsurface 1149. Some of the light in the asymmetric subsurface 1149, asymmetric low scattering light 1125, experiences multiple scattering and absorption within the asymmetric scattering region 1146 within regular tissue 1145 and exits the asymmetric subsurface 1149 as first asymmetric scattered light 1105. Other light in the subsurface 1149, asymmetric high scattering light 1135, experiences more instances of multiple scattering and absorption within the asymmetric scattering region 1146 within regular tissue 1145 and exits the asymmetric subsurface 1149 as asymmetric second scattered light 1120. Note that in theasymmetric scattering diagram 1100, all scattered light and specularly reflected light is shown going in the same direction because the figure approximately depicts only light collected by a lens. The present disclosure uses the difference between scattering responses of regular incident light 1110 within the asymmetric subsurface 1149 of asymmetric scattering region 1146 within regular tissue 1145 and scattering responses of incident light 610 within the subsurface 649 of typical tissue 645, as described in Fig. 6, to identify and characterize tissue.

[0129] Referring now to FIG. 10 and FIG. 12, FIG. 12 shows two diagrams, which illustrate the differences between the viewed light of two different tissues, first tissue 1245a and second tissue 1245b, being scanned by a shadow caster scanner according to some embodiments of the present disclosure for the purposes of tissue characterization. In first tissue illumination diagram 1200a, scanner light source incident light 1212 illuminates typical shadow caster scanner 1215 and tissue 1245a. Typical shadow caster 1215 blocks scanner light source incident light 1212 making first shaded tissue 1249a. Scanner light source incident light 1212 illuminating first tissue surface 1210a of first tissue 1245a penetrates into first illuminated tissue 1247a where it is scattered by first tissue 1245a. As described above, the typical shadow caster edge 1207 of the typical shadow caster 1215 is configured to share a plane with the source (not shown) of scanner light source incident light 1212, and casts a sharp, high contrast first observed shadow edge 1250a upon first tissue surface 1210a of first tissue 1245a and penetrates into first tissue 1245a. Generally, the first geometric shadow edge 1251a is defined as the geometric edge drawn from the typical shadow caster edge 1207 of a typical shadow caster 1215 to a first tissue 1245a being scanned, along the line that extends from the light source (not shown) to the typical shadow caster edge 1207 of the typical shadow caster 1215. In the presence of light scatter, the first observed shadow edge 1250a may differ from the first geometric shadow edge 1251a. First intensity graph 1270a depicts the intensity of light across the first observed shadow edge 1250a as a function of position with the tissue comparison horizontal axis 1274 showing the position of intensity and the tissue comparison vertical axis 1273 showing the magnitude of intensity. The first determined shadow edge 1252a within the first observed shadow edge 1250a is located at the peak slope of the first intensity curve 1230a, as described above. In second tissue illumination diagram 1200b, scanner light source incident light 1212 illuminates shadow caster scanner 1215 and tissue 1245b. Typical shadow caster 1215 blocks scanner light source incident light 1212 making second shaded tissue 1249b. Scanner light source incident light 1212illuminating second tissue surface 1210b of second tissue 1245b penetrates into second illuminated tissue 1247b where it is scattered by second tissue 1245b. Again, the typical shadow caster edge 1207 of the typical shadow caster 1215 is configured to share a plane with the source (not shown) of scanner light source incident light 1212, and casts a sharp, high contrast second observed shadow edge 1250b upon second tissue surface 1210b of second tissue 1245b and penetrates into second tissue 1245a. Generally, the second geometric shadow edge 1251b is defined as the geometric edge drawn from the typical shadow caster edge 1207 of a typical shadow caster 1215 to a second tissue 1245b being scanned, along the line that extends from the light source (not shown) to the typical shadow caster edge 1207 of the typical shadow caster 1215. In the presence of light scatter, the second observed shadow edge 1250b may differ from the second geometric shadow edge 1251b. Second intensity graph 1270b depicts the intensity of light across the second observed shadow edge 1250b as a function of position with the tissue comparison horizontal axis 1274 showing the position of intensity and the tissue comparison vertical axis 1273 showing the magnitude of intensity. The second determined shadow edge 1252b within the second observed shadow edge 1250b is located at the peak slope of the second intensity curve 1230b, as described above. Note that the profile of the first intensity curve 1230a and the second intensity curve 1230b have different shapes due to the different tissue types. As with FIG. 10, depending on the position of the typical shadow edge 1207 relative to the typical shadow caster 1215, scanner light source incident light 1212 illuminates the first tissue 1245a and / or second tissue 1245b at slightly different angles of incidence; however, because the position of the edge 1207 of the typical shadow caster 1215 and the source of scanner light source incident light 1212 are known quantities with the present disclosure, these angles of incidence are all known as well. The first geometric shadow edge 1251a and the first determined shadow edge 1252a are distinguishable, as are the second geometric shadow edge 1251b and the second determined shadow edge 1252b, with the first determined shadow edge 1252a and the second determined shadow edge 1252b being useful for material identification or tissue characterization in the present disclosure. Due to light diffusion, the first determined shadow edge 1252a is only an estimation of the first geometric shadow edge 1251a, and the second determined shadow edge 1252b is only an estimation of the second geometric shadow edge 1251b, and the first determined shadow edge 1252a and second determined shadow edge 1250bmay shift due to the angle of the scanner light source incident light 1212 or the observation angle.

[0130] Referring now to FIG. 13, as background for how the present disclosure characterizes different tissues or materials using scattered light, FIG. 13 shows two diagrams, which illustrate how light scatters differently in different tissue types, one with a short reflectance profile and one with a long reflectance profile. In short reflectance tissue scattering diagram 1300a, incident light 1310 propagates through the atmosphere 1340 and strikes short reflectance tissue surface 1343a of short reflectance tissue 1346a within background tissue 1345. Some of the incident light 1310 is reflected as short reflectance specularly reflected light 1315a, and some of the incident light 1310 passes through short reflectance tissue surface 1343a of short reflectance tissue 1346a within background tissue 1345 and propagates through the short reflectance subsurface 1349a of short reflectance tissue 1346a within background tissue 1345. Some of the light in the short reflectance subsurface 1349a becomes short reflectance lost / absorbed light 1330a and does not escape the short reflectance subsurface 1349a. Some of the light in the short reflectance subsurface 1349a, short reflectance low scattering light 1325a, experiences multiple scattering and absorption within the short reflectance tissue 1346a within background tissue 1345 and exits the short reflectance subsurface 1349a as short reflectance first scattered light 1305a. Other light in the short reflectance subsurface 1349a, short reflectance high scattering light 1335a, experiences more instances of multiple scattering and absorption within the short reflectance tissue 1346a within background tissue 1345 and exits the short reflectance subsurface 1349a as short reflectance second scattered light 1320a. In long reflectance tissue scattering diagram 1300b, incident light 1310 propagates through the air 1340 and strikes long reflectance tissue surface 1343b of long reflectance tissue 1346b within background tissue 1345. Some of the incident light 1310 is reflected as long reflectance specularly reflected light 1315b, and some of the incident light 1310 passes through long reflectance tissue surface 1343b of long reflectance tissue 1346b within background tissue 1345 and propagates through the long reflectance subsurface 1349b of long reflectance tissue 1346b within background tissue 1345. Some of the light in the long reflectance subsurface 1349b becomes first long reflectance lost / absorbed light 1330b and does not escape the long reflectance subsurface 1349b. Because long reflectance tissue 1346b, absorbs more light than short reflectance tissue 1346a, some of the light in the long reflectance subsurface 1349b becomes second long reflectance lost / absorbed light 1336b anddoes not escape the long reflectance subsurface 1349b. Some of the light in the long reflectance subsurface 1349b, long reflectance low scattering light 1325b, experiences multiple scattering and absorption within the long reflectance tissue 1346b within background tissue 1345 and exits the long reflectance subsurface 1349b as long reflectance first scattered light 1305b. Other light in the long reflectance subsurface 1349b, long reflectance high scattering light 1335b, experiences more instances of multiple scattering and absorption within the long reflectance tissue 1346b within background tissue 1345 and exits the long reflectance subsurface 1349b as long reflectance second scattered light 1320b. Note that in short reflectance tissue scattering diagram 1300a and long reflectance tissue scattering diagram 1300b, all scattered light 1305a, 1305b, 1320a, 1320b, and specularly reflected light 1315a, 1315b go in the same direction, where they are collected by a lens. The present disclosure uses the difference between scattering responses of incident light 1310 within the short reflectance subsurface 1349a of short reflectance tissue 1346a within background tissue 1345 and scattering responses of incident light 1310 within the long reflectance subsurface 1349b of background tissue 1345 to identify and characterize the different tissues.

[0131] Referring now to FIG. 14, FIG. 30, and FIG. 32, FIG. 14 shows two sets of diagrams and graphs, which compare the light / shadow edge of tape 1437 and a phantom modeling grey brain matter 1438 of a brain in the process of being scanned by a shadow caster scanner of the present disclosure for the purposes of three-dimensional scanning and material / tissue characterization. Tape diagram 1400a shows a tape / phantom-modeled grey matter bounding box 1440 across a visually-observed tape shadow edge 1450a, which is a sharp, high contrast transition from a dark shadow 1420 to a bright illuminated area 1427 on tape 1437 generated by a shadow caster scanner of the present disclosure. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Tape graph 1470a describes the gray value in the tape / phantom-modeled grey matter bounding box 1440 of tape diagram 1400a as a function of distance from the dark shadow 1420 to the bright illuminated area 1427. In tape graph 1470a, the tape / phantom-modeled grey matter horizontal axis 1474 shows the distance in pixels from the top to the bottom of the tape / phantom-modeled grey matter bounding box 1440, and the tape / phantom-modeled grey matter vertical axis 1473 shows the gray value measured in the tape / phantom-modeled grey matter bounding box 1440 with 0 gray value being the darkest value (black) and 255 being the brightest value (white.) The tape brightness curve 1430a shows themeasured intensity of brightness in the tape / phantom-modeled grey matter bounding box 1440 from near minimum gray value to near maximum gray value with the observed tape shadow edge 1452a falling within transition between minimum and maximum gray values. Even though the shadow caster system of the present disclosure provides extremely high contrast, sharp edges of luminosity, there is still some gradual transition between the dark shadow 1420 and the bright illuminated area 1427 in the observed tape shadow edge 1452a. The observed tape shadow edge 1452a is marked with a dotted line in tape graph 1470a. The observed tape shadow edge intensity 1455a is marked by a horizontal line at the point where the observed tape shadow edge 1452a and the tape brightness curve 1430a intersect. The observed tape shadow edge intensity half 1456a is marked by a horizontal line at the midpoint between the maximum and minimum of the observed tape shadow edge intensity 1455a. The tape reflectance profde length 1461a is defined as the distance between the tape brightness curve 1430a and the observed tape shadow edge 1452a at the observed tape shadow edge intensity midpoint 1456a, and is useful for tissue characterization in the present disclosure. Phantom-modeled grey brain matter diagram 1400b shows a tape / phantom-modeled grey matter bounding box 1440 across a visually-observed phantom-modeled grey brain matter shadow edge 1450b, which is a sharp, high contrast transition from a dark shadow 1420 to a bright illuminated area 1427 on a phantom modeling grey matter 1437 generated by a shadow caster scanner of the present disclosure. Phantom- modeled grey brain matter graph 1470b describes the gray value in the tape / phantom-modeled grey matter bounding box 1440 of phantom-modeled grey brain matter diagram 1400b as a function of distance from the dark shadow 1420 to the bright illuminated area 1427. In phantom- modeled grey brain matter graph 1470b, the tape / phantom-modeled grey matter horizontal axis 1474 shows the distance in pixels from the top to the bottom of the tape / phantom-modeled grey matter bounding box 1440, and the tape / phantom-modeled grey matter vertical axis 1473 shows the gray value measured in the tape / phantom-modeled grey matter bounding box 1440 with 0 gray value being the darkest value (black) and 255 being the brightest value (white.) The phantom-modeled grey matter intensity curve 1430b shows the measured intensity of brightness in the tape / grey matter bounding box 1440 from near zero gray value to near maximum gray value with the observed grey brain matter shadow edge 1452b falling within transition between minimum and maximum gray values. Even though the shadow caster system of the present disclosure provides extremely high contrast, sharp edges of luminosity, there is still somegradual transition between the dark shadow 1420 and the bright illuminated area 1427 in the observed phantom-modeled grey matter shadow edge 1452b. The observed phantom-modeled grey brain matter shadow edge 1452b is marked with a dotted line in phantom-modeled grey brain matter graph 1470b. The observed phantom-modeled grey brain matter shadow edge intensity 1455b is marked by a horizontal line at the point where the observed phantom-modeled grey brain matter shadow edge 1452b and the phantom -modeled grey matter intensity curve 1430b intersect. The observed phantom-modeled grey brain matter shadow edge intensity half 1456b is marked by a horizontal line at half the intensity of the observed phantom-modeled grey brain matter shadow edge intensity 1455b. The phantom-modeled grey brain matter reflectance profile length 1461b is defined as the distance between the phantom-modeled grey matter intensity curve 1430b and the observed phantom-modeled grey brain matter shadow edge 1452b at the observed phantom-modeled grey brain matter shadow edge intensity half 1456b. Note that the tape reflectance profile length 1461a and the phantom-modeled grey brain matter reflectance profile length 1461b differ significantly in FIG.14 and this difference aids the present disclosure in identifying scanned materials or characterizing different tissues.

[0132] Referring now to FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 30, and FIG. 32, FIG. 15 shows two sets of diagrams and graphs, which compare the light / shadow edge of phantom-modeled white matter 1537 and phantom-modeled grey matter 1538 of a brain in the process of being scanned by a shadow caster scanner of the present disclosure for the purposes of three-dimensional scanning and material / tissue characterization. Phantom-modeled white matter diagram 1500a shows a white / grey bounding box 1540 across a visually-observed phantom- modeled white matter shadow edge 1550a, which is a sharp, high contrast transition from a dark shadow region 1520 to an illuminated area 1527 on phantom-modeled white matter 1537 generated by a shadow caster scanner of the present disclosure. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Phantom-modeled white matter graph 1570a describes the gray value in the white / grey bounding box 1540 of phantom-modeled white matter diagram 1500a as a function of distance from the dark shadow region 1520 to the illuminated area 1527. In phantom-modeled white matter graph 1570a, the first horizontal axis 1574 shows the distance in pixels from the top to the bottom of the white / grey bounding box 1540, and the first vertical axis 1573 shows the gray value measured in the white / grey bounding box 1540 with 0 gray value being the darkest value (black) and 255 being the brightest value (white.) In thephantom-modeled white matter graph 1570a, the phantom-modeled white matter brightness curve 1530a shows the measured gray value in the white / grey bounding box 1540 from near minimum gray value to near maximum gray value. Even though the shadow caster system of the present disclosure provides extremely high contrast, sharp edges of luminosity, there is still some gradual transition between the dark shadow region 1520 and the illuminated area 1527. The phantom-modeled white matter slope graph 1580a describes the slope (or derivative) of the phantom-modeled white matter brightness curve 1530a of the phantom-modeled white matter graph 1570a as a function of distance. In phantom-modeled white matter slope graph 1580a, the second horizontal axis 1584 shows the distance in pixels from the top to the bottom of the white / grey bounding box 1540, and the second vertical axis 1583 shows the slope of the phantom-modeled white matter brightness curve 1530a of the phantom-modeled white matter graph 1570a. In the phantom-modeled white matter slope graph 1580a, the phantom-modeled white matter slope curve 1531a describes the slope of the phantom-modeled white matter brightness curve 1530a of the phantom-modeled white matter graph 1570a as function of distance in pixels across the white / grey bounding box 1540. In the phantom -modeled white matter slope graph 1580a, the observed phantom-modeled white matter shadow edge 1552a is defined as the distance at which the phantom-modeled white matter peak slope 1553a occurs, which is the maximum value of the phantom-modeled white matter slope curve 1531a, and is marked with a dotted line. In phantom-modeled white matter graph 1570a, the observed phantom-modeled white matter shadow edge intensity 1555a is marked by a horizontal line at the distance where the observed phantom-modeled white matter shadow edge 1552a from the phantom-modeled white matter slope graph 1580a and the phantom-modeled white matter brightness curve 1530a intersect. In phantom-modeled white matter graph 1570a, the observed phantom-modeled white matter shadow edge intensity half 1556a is marked by a horizontal line at half the intensity of the observed phantom-modeled white matter shadow edge intensity 1555a. The phantom-modeled white matter reflectance profile length 1561a is defined as the distance between the phantom-modeled white matter brightness curve 1530a and the distance of the observed phantom-modeled white matter shadow edge 1552a at the observed phantom-modeled white matter shadow edge intensity half 1556a, and is useful for tissue characterization in the present disclosure. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 below for other methods of determining reflectance profile length.) Phantom-modeled grey matter diagram 1500b shows awhite / grey bounding box 1540 across a visually-observed phantom-modeled grey matter shadow edge 1550b, which is a sharp, high contrast transition from a dark shadow region 1520 to an illuminated area 1527 on phantom-modeled grey matter 1538 generated by a shadow caster scanner of the present disclosure. Phantom-modeled grey matter graph 1570b describes the gray value in the white / grey bounding box 1540 of phantom-modeled grey matter diagram 1500b as a function of distance from the dark shadow region 1520 to the illuminated area 1527. In phantom- modeled grey matter graph 1570b, the first horizontal axis 1574 shows the distance in pixels from the top to the bottom of the white / grey bounding box 1540, and the first vertical axis 1573 shows the gray value measured in the white / grey bounding box 1540 with 0 gray value being the darkest value (black) and 255 being the brightest value (white.) In the phantom-modeled grey matter graph 1570b, the phantom-modeled grey matter brightness curve 1530b shows the measured gray value in the white / grey bounding box 1540 from near minimum gray value to near maximum gray value. Even though the shadow caster system of the present disclosure provides extremely high contrast, sharp edges of luminosity, there is still some gradual transition between the dark shadow region 1520 and the illuminated area 1527. The phantom-modeled grey matter slope graph 1580b describes the slope (or derivative) of the phantom -modeled grey matter brightness curve 1530b of the phantom-modeled grey matter graph 1570b as a function of distance. In phantom-modeled grey matter slope graph 1580b, the second horizontal axis 1584 shows the distance in pixels from the top to the bottom of the white / grey bounding box 1540, and the second vertical axis 1583 shows the slope of the phantom-modeled grey matter brightness curve 1530b of the phantom-modeled grey matter graph 1570b. In the phantom-modeled grey matter slope graph 1580b, the phantom-modeled grey matter slope curve 1531b describes the slope of the phantom-modeled grey matter brightness curve 1530b of the phantom-modeled grey matter graph 1570b as function of distance in pixels across the white / grey bounding box 1540. In the phantom-modeled grey matter slope graph 1580b, the observed phantom-modeled grey matter shadow edge 1552b is defined as the distance at which the phantom-modeled grey matter peak slope 1553b occurs, which is the maximum value of the phantom-modeled grey matter slope curve 153 lb, and is marked with a dotted line. In phantom-modeled grey matter graph 1570b, the observed phantom-modeled grey matter shadow edge intensity 1555b is marked by a horizontal line at the distance where the observed phantom-modeled grey matter shadow edge 1552b from the phantom-modeled grey matter slope graph 1580b and the phantom-modeled greymatter brightness curve 1530b intersect. In phantom-modeled grey matter graph 1570b, the observed phantom-modeled grey matter shadow edge intensity half 1556b is marked by a horizontal line at half the intensity of the observed phantom-modeled grey matter shadow edge intensity 1555b. The phantom-modeled grey matter reflectance profde length 1561b is defined as the distance between the phantom-modeled grey matter brightness curve 1530b and the distance of the observed phantom-modeled grey matter shadow edge 1552b at the observed phantom- modeled grey matter shadow edge intensity half 1556b, and is useful for tissue characterization in the present disclosure. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 for other methods of determining reflectance profile length.) Note the stark differences between the shapes of the phantom-modeled white matter brightness curve 1530a and the phantom-modeled grey matter brightness curve 1530b and between shapes of the phantom-modeled white matter slope curve 1531a and the phantom-modeled grey matter slope curve 1531b. These differences aid in allowing the present disclosure to characterize different tissues.

[0133] Referring now to FIG. 16, FIG. 16 shows two diagrams describing relevant shadow and surface geometries for material identification and / or tissue characterization and distinction, according to some embodiments of the present disclosure. The shadow caster scanner of the present disclosure rigorously determines the three-dimensional structure of a three-dimensional object 1670 and is well suited for three-dimensional modeling, and this three-dimensional modeling is useful in identifying materials or characterizing different tissue in a three- dimensional object 1670. In object diagram 1600a, a three-dimensional object 1670 is shown on a surface 1643 with a sharp shadow 1620 cast by a shadow caster scanner of the present disclosure. In object diagram 1600a, a leading sharp edge of luminosity 1650a and a trailing sharp edge of luminosity 1650b are shown projected across the three-dimensional object 1670 and the surface 1643. Zoom diagram 1600b depicts an enlarged view of the zoom box 1640 in object diagram 1600a. In zoom diagram 1600b, relevant shadow and surface geometries are shown and defined as follows. Light plane 1617 is a plane, which contains the light source (not shown) and also contains the edge (not shown) of the shadow caster of a shadow caster scanner of the present disclosure, thereby generating the trailing sharp edge of luminosity 1650b. A similar plane generates the leading sharp edge of luminosity 1650a. Light direction vector 1619 depicts the direction of the light source of the shadow caster scanner of the present disclosure, which is parallel to light plane 1617. The light plane normal vector 1637 is perpendicular to thelight plane 1617. Local surface normal vector 1630 is perpendicular to the surface of the three- dimensional object 1670 at surface point 1635, which is located along the trailing sharp edge of luminosity 1650b. The angle of incidence 1633 is the angle between the light direction vector 1619 and the local surface normal 1630. The shadow edge vector 1631 runs along the trailing sharp edge of luminosity 1650b at surface point 1635 and is perpendicular to local surface normal vector 1630. The shadow direction vector 1632 runs along the surface of the three- dimensional object 1670 and is perpendicular to shadow edge vector 1631 at surface point 1635 and is perpendicular to local surface normal vector 1630. The shadow direction vector 1632 also follows the direction of the sharp shadow 1620 as it is projected across the three-dimensional object 1670 during a scan by the shadow caster scanner of the present disclosure.

[0134] Referring now to FIG. 17, FIG. 17 shows two diagrams that demonstrate how a curved surface affects scattering distance. In convex scattering diagram 1700a, convex / concave incident light 1710 strikes the convex surface 1743a of a convex object 1746a. Some of the convex / concave incident light 1710 passes through the convex surface 1743 a of the convex object 1746a and propagates through the convex subsurface 1749 of convex object 1746a. Some of the light in the convex subsurface 1749a, convex internal scattered light 1735a, experiences multiple scattering and absorption within the convex object 1746a and exits the convex subsurface 1749a as convex scattered light 1715a. The convex scattering distance 1751a is the distance between where the convex / concave incident light 1710 enters the convex object 1746a and where convex scattered light 1715a exits. In concave scattering diagram 1700b, convex / concave incident light 1710 strikes the concave surface 1743b of a concave object 1746b. Some of the convex / concave incident light 1710 passes through the concave surface 1743b of the concave object 1746b and propagates through the concave subsurface 1749 of concave object 1746b. Some of the light in the concave subsurface 1749b, concave internal scattered light 1735b, experiences multiple scattering and absorption within the concave object 1746b and exits the concave subsurface 1749b as concave scattered light 1715b. The concave scattering distance 1751b is the distance between where the convex / concave incident light 1710 enters the concave object 1746b and where concave scattered light 1715b exits. The scattering distances differ depending on whether the surface of an object is convex or concave. For the purposes of illustration, the convex scattering distance 1751a and the concave scattering distance 1751b are shown as straight-line distances. (The concave scattering distance 1751b does not take intoconsideration light that escapes one edge of the concave portion, travels through air, and enters the other side.) The actual internal scattering distance is not indicated by these straight-line distances, but rather by the scattering path-lengths indicated within the medium, convex internal scattered light 1735a and concave internal scattered light 1735b. Note that the straight-line distances 1751a 1751b are essentially the same; however, there are more short-path scattering lengths for the convex case. For the convex case many more short-path scattering events are possible than in the concave case. This fact is true even in the purely diffusive case, where there is completely isotropic scattering and the angle of incidence of the light does not matter. Moreover, although there may be more short-path scattering events in the convex case, the overall length of the concave internal scattered light path 1751b is greater than the overall length of the convex internal scattered light path 1751a. This difference in path lengths may imply that absorption will have a greater effect for the concave surfaces. Determining the effective path lengths from observations outside the material relies, then, on knowledge of the shape of the surface. Simulations of the appearance of scattered light through such concave and convex surfaces would enable more reliable determination of material(s).

[0135] Referring now to FIG. 9, FIG. 10, FIG. 11, FIG. 12, FIG. 14, FIG. 15, FIG. 17, FIG. 18, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 30, FIG. 32, FIG. 33, FIG. 34, FIG. 35, FIG. 36, FIG. 37, FIG. 39, FIG. 40, FIG. 41, FIG. 42, FIG. 43 and FIG. 46, FIG. 18 illustrates a high-level flow chart 1800, which describes a high- level process of using a shadow caster scanner of the present disclosure to identify material, according to the some embodiments of the present disclosure. The first step in the high-level process of identifying materials in an object using a shadow caster scanner is the scan surface step 1810, in which the surface of an object is scanned by a shadow caster scanner of the present disclosure by projecting one or more edges of luminosity across the object. Next, in the prior knowledge decision step 1820, whether there is prior knowledge of the wavelength range with the lowest noise in the material is determined. If there is not prior knowledge of the wavelength range with the lowest noise in the material, then determine the wavelength range that produces the largest signal to noise ratio between the shadowed and lit regions and the sharpest shadow in the determine wavelength range step 1830 and continue to the construct 3D representation step 1840. Sharper shadows require shorter penetration depths without too much absorption so that reflected light is observed. If there is prior knowledge of the wavelength range with the lowestnoise in the material, then construct a three-dimensional representation of the surface using the wavelength range with the lowest noise in the construct 3D representation step 1840. Next, in the identify shadow frame step 1850, identify the shadow frame of the video captured by the shadow caster scanner where the shadow edge lies across the viewing area of each pixel. Next, define a rectangular projection, or bounding box, onto the surface of the object for determining the optical characteristics of the central vertex in the define rectangular projection step 1860. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Next, in the determine light intensity profile step 1870, for each central vertex, integrate the intensity values of neighboring vertices within the region of interest along the shadow edge vector to determine the light intensity profile. Next, determine the optical characteristics of the material associated with each vertex in the determine optical characteristics step 1880. Relevant optical characteristics, or physical features, may include, but are not limited to: scatter width (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), reflectance profile length (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), shape of the reflectance profile (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), combined reflectance profile length of the tissue (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG. 29), forward reflectance profile length from dark to light across the shadow edge or edges of luminosity (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG. 29), backward reflectance profile length from light to dark across the shadow edge or edges of luminosity (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG. 29), asymmetry (FIG. 11), anisotropy (FIG. 29, FIG. 42), polarizationdependent optical properties (FIG. 33), the angle of incidence on said object from one or more light sources (FIG. 39, FIG. 40, FIG. 41, FIG. 42, FIG. 43), the angle of observation from said one or more image capturing devices (FIG. 40, FIG. 41, and FIG. 42), pixel intensity (FIG. 9, FIG. 10, FIG. 12, FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 42, and FIG. 43), normalized pixel intensity (FIG. 20, FIG. 21 FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG.26, FIG. 27, FIG. 28, FIG. 29, and FIG. 37), intensity values (FIG. 9, FIG. 10, FIG. 12, FIG. FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 42, and FIG. 43), normalized intensity values (FIG. 20, FIG. 21 FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG.27, FIG. 28, FIG. 29, and FIG. 37), color intensity values (FIG. 37, FIG. 42, and FIG. 46), colorintensity ratios (FIG. 37, FIG. 42, and FIG. 46), the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. Finally, in the identify material step 1890, use a look up table to look up the optical characteristics of different materials to identify the material(s). (See FIG. 34, FIG. 35, and FIG. 36 for methods of building and using look-up tables or reference libraries.)

[0136] Referring now to FIG. 19, FIG. 19 depicts a phantom building recipe flow chart 1900, which describes the process of building phantoms for testing and calibrating a materialidentifying a shadow caster scanner of the present disclosure, according to some embodiments. The first step in the process of building phantoms is the silicone base measuring step 1910, in which a silicone base is measured out into a mixing container. Next, in the aluminum oxide measuring step 1915, aluminum oxide (A1O2) is measured out in a weighing dish. Next, in the mix base step 1920, the aluminum oxide (A1O2) is added to the silicone base and mixed by hand for ten (10) minutes. Next, India ink is measured out in a weighing dish in the India ink measuring step 1925. Next, in the mix India ink step 1930, India ink is added to the aluminum oxide (A1O2) and silicone base mixture and hand mixed for ten (10) minutes. Next, a curing agent is measured out in a weighing dish in the measure curing agent step 1935. Next, in the curing agent mix step 1940, the curing agent is added to the aluminum oxide (A1O2), silicone base, and India ink mixture and hand mixed for ten (10) minutes. Next, the aluminum oxide (A1O2), silicone base, India ink, and curing agent mixture is placed in a vacuum chamber for thirty (30) minutes under twenty -five (25) inHg pressure in the vacuum step 1945. Next, in the pour mold step 1950, the aluminum oxide (A1O2), silicone base, India ink, and curing agent mixture is carefully poured into a mold and is cured for 24 hours at room temperature. Finally, the phantom is removed from the mold in the remove phantom step 1955. The absorption of the dye is then determined by conducting laser-transmission tests. The phantom is then ready to be used to test and calibrate a material-identifying a shadow caster scanner of the present disclosure.

[0137] The construction details of the disclosure as shown in FIG. 19, are as follows. The silicone base comprises silicone, polydimethylsiloxane (PDMS), or the like. Aluminum oxide (A1O2) comprises Aluminum oxide (A1O2) or the like. India ink comprises ink, India ink, dye, color fdler, or the like. The curing agent comprises a curing agent or the like. The composition ratios of aluminum oxide to silicone base comprise 0-15 grams of aluminum oxide per 100ml of silicone base. The concentrations of India ink comprise 0-156 micrograms of India ink per gram of water. These materials are exemplary of the scope and spirit of the present disclosure; however, the above-described embodiments and examples should not limit the present disclosure, and those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein.

[0138] Referring now to FIG. 9, FIG. 10, FIG. 12, FIG. 14, FIG. 15, FIG. 16, FIG. 18, FIG. 20, FIG. 24, FIG. 26, FIG. 27, FIG. 30, FIG. 32, FIG. 34, FIG. 35, and FIG. 36, FIG. 20 shows a first scattering width determination flow chart 2000, which describes the first process of determining the scatter width using a shadow caster scanner of the present disclosure, according to some embodiments. According to other embodiments, any device that can project a pattern can be used. The first step in the first process of determining the scattering width is the scan 3D surface step 2010 in which an object’s three-dimensional surface is scanned by a shadow caster scanner of the present disclosure. Next, in the calculate shadow plane normal step 2015, the normal of the plane (light plane 1617 in FIG. 16) defined by the light source and the shadow caster at each shadow caster position is calculated. Next, the surface normal vector (local surface normal vector 1630 in FIG. 16) is calculated at each point by using a covariance analysis algorithm, or the like, in the calculate normal vector step 2020. Next, in the cross multiply step 2025, the shadow plane normal calculated above is cross multiplied with the surface normal vector calculated above at each point to define the shadow edge vector (shadow edge vector 1631 in FIG. 16) along the surface. Next, a linear approximation of the local shadow edge is defined using the pixel of interest and the shadow edge vector defined above in the shadow edge approximation step 2030. Next, in the determine sample size step 2035, the sample size and bounding volume for fitting data, or bounding box, is determined. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Next, the distance between each point within the bounding volume and the local shadow edge approximation determined above is computed in the compute distance step 2040. Next, in the normalize pixel intensities step 2045,pixel intensities are normalized. (See FIG. 24 below for methods of normalizing pixel intensities.) Next, in the plot step 2050, the distance is analyzed as a function of pixel intensity. (See FIG. 9, FIG. 10, FIG. 12, FIG. 14, and FIG. 15 for examples of plots of distance versus pixel intensity.) In some cases, plotting the data is unnecessary and fitting a curve to the data is sufficient. Next, knots (anchor points) are defined across the plot from above in the define knots step 2055 as shown in FIG. 26. Next, in the spline fit step 2060, a spline fit is applied to the plot using the knots identified above as shown in FIG. 26. Next, the steepest slope of the fit spline between the knots is determined in the determine steepest slope step 2065 (See FIG. 15), and this steepest slope defines the shadow edge location. Next, in the fit sum step 2070, the sum of two exponentials is fit to the data, starting at the shadow edge location and looking into the shadow (See FIG. 27). In some versions of this embodiment other techniques than the sum of two exponentials may be used to characterize the data, such as by taking the absolute difference profile of the positive and negative frame reflectance profile length and counting pixels above the full width half max of the resulting curve, which is faster but noisier (See Fig. 29). Finally, the fit curve is used to determine the reflectance profile length in the determine reflectance profile step 2075 (See FIG. 27). For the purposes of illustration, the first scattering width determination flow chart 2000 describes data analysis in terms of plotting data; however, the data can be fit without using a plot but by using other data analysis techniques. Reflectance profile length may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, FIG. 35, and FIG. 36.

[0139] It should be noted that, in some embodiments, the fit curve may be used determine not only the reflectance profile length but also the reflectance profile shape, which may be useful in determine the thickness of the material. By way of example, the curve fit of the shadow edge in general may be an indication of thicknesses, especially if the material types are known but their thicknesses are not. Conversely, if one or more material thicknesses are known, characteristics of the material may be determined using the shape of the reflectance profile shape. Thus, the fit curve may be used to determine reflectance profile length or shape in the determine reflectance profile step 2075 and, and the reflectance profile shape may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, and FIG. 35.

[0140] Referring now to FIG. 9, FIG. 10, FIG. 12, FIG. 14, FIG. 15, FIG. 16, FIG. 18, FIG 21, FIG. 24, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 30, FIG. 32, FIG. 34, FIG. 35, and FIG.36, FIG. 21 shows a second scattering width determination flow chart 2100, which describes the second process of determining the scatter width using a shadow caster scanner of the present disclosure, according to another embodiment. The first step in the second process of determining the scattering width is the second scan 3D surface step 2110 in which an object’s three- dimensional surface is scanned by a shadow caster scanner of the present disclosure. Next, in the second calculate shadow plane normal step 2115, the normal of the plane (light plane 1617 in FIG. 16) defined by the light source and the shadow caster at each shadow caster position is calculated. Next, the surface normal vector (local surface normal vector 1630 in FIG. 16) is calculated at each point by using a covariance analysis algorithm, or the like, in the second calculate normal vector step 2120. This step is performed by analyzing a point’s neighboring points and choosing the radius of the neighborhood. A larger neighborhood smooths features and causes an averaging effect. Typically, the neighborhood radius is not larger than the bounding box, or too small such that calculated shadow direction becomes noisy. Next, in the second cross multiply step 2125, the shadow plane normal calculated above is cross multiplied with the surface normal vector calculated above at each point to define the shadow edge vector (shadow edge vector 1631 in FIG. 16) along the surface. Next, a linear approximation of the local shadow edge is defined using the pixel of interest and the shadow edge vector defined above in the second shadow edge approximation step 2130. If the bounding box is too large, this linear approximation may not be correct at the pixel of interest, and this fact is a consideration in determining bounding box sizes. Next, in the second determine sample size step 2135, the sample size and bounding volume for fitting data, or bounding box, is determined. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Next, the distance between each point within the bounding volume and the local shadow edge approximation determined above is computed in the second compute distance step 2140. Next, in the second normalize pixel intensities step 2145, pixel intensities are normalized. (See FIG. 24 below for methods of normalizing pixel intensities.) Next, in the second plot step 2150, the distance is plotted as a function of pixel intensity. (See FIG. 9, FIG. 10, FIG. 12, FIG. 14, and FIG. 15 for examples of plots of distance versus pixel intensity.) Next, whether an LCD or a physical / mechanical shadow caster was used is decided in the shadow caster type step 2155. If aphysical / mechanical shadow caster was used in the shadow caster type step 2155, then knots (anchor points) are defined across the plot from above in the second define knots step 2160 as shown in FIG. 26. Then, in the second spline fit step 2165, a spline fit is applied to the plot using the knots identified above as shown in FIG. 26. Then, the steepest slope of the fit spline between the knots is determined in the second determine steepest slope step 2167 (See FIG. 15), and this steepest slope defines the shadow edge location. If an LCD shadow caster was used in the shadow caster type step 2155, then the data sets are smoothed using a symmetric averaging filter in the data smoothing step 2170. Then, in the absolute value step 2175, the absolute value of the difference between the two inverse frames is taken. (See FIG. 28 and 29 below.) Then, the location of the minimum value is determined in the determine minimum value step 2177 (See FIG. 28 and 29 below), and this minimum value is the shadow edge location. Next, whether or not an LCD or a physical / mechanical shadow caster was used in the shadow caster type step 2155, in the second fit sum step 2180, the sum of two exponentials is fit to the data, starting at the shadow edge location and looking into the shadow (See FIG. 27). Finally, the fit curve is used to determine the reflectance profile length in the second determine reflectance profile step 2190 (See FIG. 27). Reflectance profile length may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, FIG. 35, and FIG. 36.

[0141] Referring now to FIG. 9, FIG. 10, FIG. 12, FIG. 14, FIG. 15, FIG. 16, FIG. 18, FIG. 22, FIG. 24, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 30, FIG. 32, FIG. 34, FIG. 35, and FIG. 36, FIG. 22 shows a third scattering width determination flow chart 2200, which describes the third process of determining the scatter width using a shadow caster scanner of the present disclosure, according to another embodiment. The first step in the third process of determining the scattering width is the third scan 3D surface step 2210 in which an object’s three-dimensional surface is scanned by a shadow caster scanner of the present disclosure. Next, in the third calculate shadow plane normal step 2215, the normal of the plane (light plane 1617 in FIG. 16) defined by the light source and the shadow caster at each shadow caster position is calculated. Next, the surface normal vector (local surface normal vector 1630 in FIG. 16) is calculated at each point by using a covariance analysis algorithm, or the like, in the third calculate normal vector step 2220. Next, in the third cross multiply step 2225, the shadow plane normal calculated above is cross multiplied with the surface normal vector calculated above at each point to define the shadow edge vector (shadow edge vector 1631 in FIG. 16) along the surface. Next, a linearapproximation of the local shadow edge is defined using the pixel of interest and the shadow edge vector defined above in the third shadow edge approximation step 2230. Next, in the third determine sample size step 2235, the sample size and bounding volume for fitting data, or bounding box, is determined. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Next, the distance between each point within the bounding volume and the local shadow edge approximation determined above is computed in the third compute distance step 2240. Next, in the third normalize pixel intensities step 2245, pixel intensities are normalized. (See FIG. 24 below for methods of normalizing pixel intensities.) Next, in the third plot step 2250, the distance is plotted as a function of pixel intensity. (See FIG. 9, FIG. 10, FIG. 12, FIG. 14, and FIG. 15 for examples of plots of distance versus pixel intensity.) Next, whether an LCD or a physical / mechanical shadow caster was used is decided in the second shadow caster type step 2255. If a physical / mechanical shadow caster was used in the second shadow caster type step 2255, then in the third spline fit step 2260, a spline fit is applied to the plot. Then, the steepest slope of the fit spline is determined in the third determine steepest slope step 2265 (See FIG. 15), and this steepest slope defines the shadow edge location. If an LCD shadow caster was used in the second shadow caster type step 2255, then the data sets are smoothed using a symmetric averaging filter in the second data smoothing step 2270. Then, in the second absolute value step 2275, the absolute value of the difference between the two inverse frames is taken. (See FIG. 28 and 29 below.) Then, the location of the minimum value is determined in the second determine minimum value step 2277 (See FIG. 28 and 29 below), and this minimum value is the shadow edge location. Next, whether or not an LCD or a physical / mechanical shadow caster was used in the second shadow caster type step 2255, in the third fit sum step 2280, the sum of two exponentials is fit to the data, starting at the shadow edge location and looking into the shadow (See FIG. 27). Finally, the fit curve is used to determine the reflectance profile length in the third determine reflectance profile step 2290 (See FIG. 27). Reflectance profile length may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, FIG. 35, and FIG. 36.

[0142] Referring now to FIG. 9, FIG. 10, FIG. 12, FIG. 14, FIG. 15, FIG. 16, FIG. 18, FIG. 23, FIG. 24, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 30, FIG. 32, FIG. 34, FIG. 35, and FIG. 36, FIG. 23 shows a fourth scattering width determination flow chart 2300, which describes the fourth process of determining the scatter width using a shadow caster scanner of the presentdisclosure, according to another embodiment. The first step in the fourth process of determining the scattering width is the fourth scan 3D surface step 2310 in which an object’s three- dimensional surface is scanned by a shadow caster scanner of the present disclosure. Next, in the fourth calculate shadow plane normal step 2315, the normal of the plane (light plane 1617 in FIG. 16) defined by the light source and the shadow caster at each shadow caster position is calculated. Next, the surface normal vector (local surface normal vector 1630 in FIG. 16) is calculated at each point by using a covariance analysis algorithm, or the like, in the fourth calculate normal vector step 2320. Next, in the fourth cross multiply step 2325, the shadow plane normal calculated above is cross multiplied with the surface normal vector calculated above at each point to define the shadow edge vector (shadow edge vector 1631 in FIG. 16) along the surface. Next, a linear approximation of the local shadow edge is defined using the pixel of interest and the shadow edge vector defined above in the fourth shadow edge approximation step 2330. Next, in the fourth determine sample size step 2335, the sample size and bounding volume for fitting data, or bounding box, is determined. (See FIG. 30 and FIG. 32 below for methods involving bounding boxes or volumes.) Next, the distance between each point within the bounding volume and the local shadow edge approximation determined above is computed in the fourth compute distance step 2340. Next, in the fourth normalize pixel intensities step 2345, pixel intensities are normalized. Next, in the fourth plot step 2350, the distance is plotted as a function of pixel intensity. (See FIG. 9, FIG. 10, FIG. 12, FIG. 14, and FIG. 15 for examples of plots of distance versus pixel intensity.) Next, whether an LCD or a physical / mechanical shadow caster was used is decided in the third shadow caster type step 2355. If a physical / mechanical shadow caster was used in the third shadow caster type step 2355, then in the fourth spline fit step 2360, a spline fit is applied to the plot. Then, the steepest slope of the fit spline is determined in the fourth determine steepest slope step 2365 (See FIG. 15), and this steepest slope defines the shadow edge location. If an LCD shadow caster was used in the third shadow caster type step 2355, then the data sets are smoothed using a symmetric averaging filter in the third data smoothing step 2370. Then, in the third absolute value step 2375, the absolute value of the difference between the two inverse frames is taken. (See FIG. 28 and 29 below.) Then, the location of the minimum value is determined in the third determine minimum value step 2377 (See FIG. 28 and 29 below), and this minimum value is the shadow edge location. Next, whether or not an LCD or a physical / mechanical shadow caster was used in the third shadow caster typestep 2355, starting at the shadow edge location and looking into the shadow, the reflectance profile length is determined in the fourth determine reflectance profile step 2380. (See Fig. 27.) Reflectance profile length may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, FIG. 35, and FIG. 36.

[0143] Referring now to FIG. 18, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 34, and FIG. 35, FIG. 24 illustrates a normalization flowchart 2400, which describes the process of normalizing pixel intensities, which is used in the flowcharts of FIG. 20, FIG. 21, FIG. 22, and FIG. 23, above, and shown in the graphs of FIG. 25, FIG. 26, FIG. 27, FIG. 28, and FIG. 29. The first step of the normalization flowchart 2400 is the fifth scan 3D surface step 2410 in which an object’s three-dimensional surface is scanned by a shadow caster scanner of the present disclosure. Next, a maximum frame is defined as an image of the scene without a shadow cast onto it in the define maximum frame step 2420. Next, in the define minimum frame step 2430, for each shadow caster position, a minimum frame is defined as a future or past frame in the scan in which the shadow cast by the shadow caster scanner of the present disclosure is covering at least 5 mm on each side of the shadow edge or other reasonable length such that the bounding box captures the observable scattering profile length. Finally, in the pixel intensities are normalized in each frame using the minimum-maximum feature scaling in the frame normalization step 2440. Normalized pixel intensities may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, and FIG. 35.

[0144] Referring now to FIG. 24 and FIG. 25, FIG. 25 shows an example of a full lineout from raw data from a scan by a shadow caster scanner for identifying materials of the present disclosure, according to some embodiments. In FIG. 25, a normalization intensity graph 2500 is depicted, which presents a plot of raw data 2531 of normalized intensity of light as a function of distance across a sharp shadow edge generated by a shadow caster of the present disclosure. The raw data horizontal axis 2574 shows the distance in millimeters across an edge of luminosity, and the raw data vertical axis 2573 shows the normalized intensity of light as determined by the process disclosed in normalization flowchart 2400 in FIG. 24, as described above. The vertical dotted line 2551 indicates the position of the shadow edge as shown in the legend 2571. First horizontal line 2536 denotes the normalized intensity at the shadow edge. Second horizontal line 2534 specifies half the normalized intensity at the shadow edge. Third horizontal line 2532shows the minimum average normalized intensity. The solid vertical line 2552 indicates the reflectance profile length.

[0145] Referring now to FIG. 20, FIG. 21, FIG. 24 and FIG. 26, FIG. 26 shows an example of spline fit raw data 2631 from a scan by a shadow caster scanner for identifying materials, according to some embodiments of the present disclosure. In FIG. 26, a spline fit normalization intensity graph 2600 is illustrated, which presents a plot of spline fit raw data 2631 of normalized intensity as a function of distance across a sharp shadow generated by a shadow caster of the present disclosure with a spline fit curve 2630 fit to spline knots 2635, which are used in the flowcharts of FIG. 20 and FIG. 21 and shown in the spline fit legend 2671. The spline fit horizontal axis 2674 shows the distance in millimeters across an edge of luminosity, and the spline fit vertical axis 2673 shows the normalized intensity of light as determined by the process disclosed in normalization flowchart 2400 in FIG. 24, as described above. The spline fit vertical dotted line 2651 indicates the position of the shadow edge as shown in the spline fit legend 2671. Spline fit first horizontal line 2636 denotes the normalized intensity at the shadow edge where the spline fit curve 2630 intersects the shadow edge shown by the spline fit vertical dotted line 2651. Spline fit second horizontal line 2634 specifies half the normalized intensity at the shadow edge. Spline fit third horizontal line 2632 shows the minimum average normalized intensity. The spline fit solid vertical line 2652 indicates the reflectance profile length.

[0146] Referring now to FIG. 22, FIG. 23, FIG. 24 and FIG. 27, FIG. 27 shows an example of fit curve raw data 2731 from a scan by a shadow caster scanner for identifying materials, according to some embodiments of the present disclosure. In FIG. 27, a fit curve graph 2700 is illustrated, which presents a plot of fit curve raw data 2731 of normalized intensity as a function of distance across a sharp shadow generated by a shadow caster of the present disclosure with a fit curve 2730 fit to the raw data 2731, which is used in the flowcharts of FIG. 22 and FIG. 23 and shown in the fit curve legend 2771. The fit curve horizontal axis 2774 shows the distance in millimeters across an edge of luminosity, and the fit curve vertical axis 2773 shows the normalized intensity of light as determined by the process disclosed in normalization flowchart 2400 in FIG. 24, as described above. The fit curve vertical dotted line 2751 indicates the position of the shadow edge as shown in the legend 2771. Fit curve first horizontal line 2736 denotes the normalized intensity at the shadow edge where the spline fit curve 2730 intersects the shadowedge shown by the vertical dotted line 2751 . Fit curve second horizontal line 2734 specifies half the normalized intensity at the shadow edge. Fit curve third horizontal line 2732 shows the minimum average normalized intensity. The fit curve vertical solid line 2752 indicates the reflectance profile length.

[0147] Referring now to FIG. 19, FIG. 21, FIG. 22, FIG. 23, FIG. 24, and FIG. 28, FIG. 28 shows an example of positive frame raw data 2831 and negative frame raw data 2833 from a scan of a grey matter phantom, which was prepared by the method described in FIG. 19, by a shadow caster scanner for identifying materials, according to some embodiments of the present disclosure. In FIG. 28, for a given point on a grey matter phantom, the positive frame is defined as a frame from a scan by a shadow caster scanner of the present disclosure in which the transition across the sharp shadow edge goes from dark exposure or minimum normalized light intensity to bright exposure or maximum normalized light intensity. For a given point on an object, a negative frame is defined as a frame from a scan by a shadow caster scanner of the present disclosure in which the transition across the sharp shadow edge goes from bright exposure or maximum normalized light intensity to dark exposure or minimum normalized light intensity. FIG. 28 shows a plot of the measurement of green light from a grey matter phantom, which has been scanned by a shadow caster scanner of the present disclosure. In FIG. 28, an absolute difference graph 2800 is depicted, which presents a plot of positive frame raw data 2831 of normalized intensity as a function of distance across a sharp shadow generated by a shadow caster of the present disclosure with a positive frame profile 2842 of the positive frame raw data 2831, a plot of negative frame raw data 2833 of normalized intensity as a function of distance across a sharp shadow generated by a shadow caster of the present disclosure with a negative frame profile 2844 of the negative frame raw data 2833, and an absolute difference profile 2846, which are shown in the legend 2871. The absolute difference profile 2846 represents the absolute value of the difference between the positive frame profile 2842 and the negative frame profile 2844 and is used for LCD shadow casters in the flowcharts of FIG. 21, FIG. 22 and FIG. 23. The absolute difference horizontal axis 2874 shows the distance in millimeters across an edge of luminosity, and the absolute difference vertical axis 2873 shows the normalized intensity of light as determined by the process disclosed in normalization flowchart 2400 in FIG. 24, as described above. The absolute difference vertical dotted line 2851 indicates the position of the shadow edge, which is set at the absolute difference minimum value 2853 of absolute difference profile2846 and set at 0 mm distance and shown in the absolute distance legend 2871 . Absolute difference first horizontal line 2836 denotes the normalized intensity at the shadow edge. Absolute difference second horizontal line 2834 specifies half the normalized intensity at the shadow edge. Third horizontal line 2832 shows the minimum average normalized intensity. The distance between the negative frame point 2857, where the negative frame profile 2844 intersect the second horizontal line 2834 that is half the normalized intensity at the shadow edge, and the absolute difference vertical dotted line 2851, which represents the shadow edge, is the absolute difference negative frame reflectance profile length 2849. The distance between the positive frame point 2855, where the positive frame profile 2842 intersect the second horizontal line 2834 that is half the normalized intensity at the shadow edge, is the absolute difference positive frame reflectance profile length 2848. For the measurement of green light from a given point of a grey matter phantom, which was scanned by a shadow caster scanner of the present disclosure, note that the absolute difference negative frame reflectance profile length 2849 and the absolute difference positive frame reflectance profile length 2848 both have substantially the same length and are isotropic with homogeneous length regardless of whether the measurement is from a negative frame or a positive frame, basically indicating that the amount of subsurface scattered light penetrating across the shadow edge, into the shadow, and detected by the camera, is the same between a positive frame and a negative frame. The comparison of reflectance length between a positive frame and a negative frame is useful in allowing the present disclosure to characterize or distinguish different materials or tissues, and is considered a physical feature, or optical characteristic.

[0148] Referring now to FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 28, and FIG. 29, FIG. 29 shows an example of porcine positive frame raw data 2931 and porcine negative frame raw data 2933 from a scan of porcine skin by a shadow caster scanner for identifying materials, according to some embodiments of the present disclosure. As with FIG. 28, the positive frame is defined as a frame from a scan by a shadow caster scanner of the present disclosure in which the transition across the sharp shadow edge goes from dark exposure or minimum normalized light intensity to bright exposure or maximum normalized light intensity, and a negative frame is defined as a frame from a scan by a shadow caster scanner of the present disclosure in which the transition across the sharp shadow edge goes from bright exposure or maximum normalized light intensity to dark exposure or minimum normalized light intensity. FIG. 29 shows a plot showing ananisotropy measurement of green light from porcine skin, which has been scanned by a shadow caster scanner of the present disclosure. In FIG. 29, a porcine absolute difference graph 2900 is depicted, which presents a plot of porcine positive frame raw data 2931 of normalized intensity as a function of distance across a sharp shadow generated by a shadow caster of the present disclosure with a porcine positive frame profde 2942 of the porcine positive frame raw data 2931, a plot of porcine negative frame raw data 2933 of normalized intensity as a function of distance across a sharp shadow generated by a shadow caster of the present disclosure with a porcine negative frame profile 2944 of the porcine negative frame raw data 2933, and a porcine absolute difference profile 2946, which are shown in the porcine legend 2971. The porcine absolute difference profile 2946 represents the absolute value of the difference between the porcine positive frame profile 2942 and the porcine negative frame profile 2944 and is used for LCD shadow casters in the flowcharts of FIG. 21, FIG. 22 and FIG. 23. The porcine horizontal axis 2974 shows the distance in millimeters across an edge of luminosity, and the porcine vertical axis 2973 shows the normalized intensity of light as determined by the process disclosed in normalization flowchart 2400 in FIG. 24, as described above. The porcine vertical dotted line 2951 indicates the position of the shadow edge, which is set at the porcine absolute difference minimum value 2953 of porcine absolute difference profile 2946 and set at 0 mm distance, and shown in the porcine legend 2971. First porcine horizontal line 2936 denotes the normalized intensity at the shadow edge. Second porcine horizontal line 2934 specifies half the normalized intensity at the shadow edge. Third porcine horizontal line 2932 shows the minimum average normalized intensity. The distance between the porcine negative frame point 2957, where the porcine negative frame profile 2944 intersect the second horizontal line 2934 that is half the normalized intensity at the shadow edge, and the porcine vertical dotted line 2951, which represents the shadow edge, is the porcine negative frame reflectance profile length 2949. The distance between the porcine positive frame point 2955, where the porcine positive frame profile 2942 intersect the second porcine horizontal line 2934 that is half the normalized intensity at the shadow edge, is the porcine positive frame reflectance profile length 2948. For the measurement of green light from a given point of porcine skin, which was scanned by a shadow caster scanner of the present disclosure, note that the porcine negative frame reflectance profile length 2949 and the porcine positive frame reflectance profile length 2948 have different lengths with the porcine negative frame reflectance profile length 2949 being shorter than the porcine positive framereflectance profile length 2948 and are anisotropic with reflectance length depending on whether the measurement is from a negative frame or a positive frame, basically indicating that the amount of scattered light penetrating into the shadow differs between a negative and a positive frame. These anisotropic differences of reflectance length are useful in allowing the present disclosure to characterize or distinguish different materials or tissues, and are considered physical features, or optical characteristics.

[0149] Referring now to FIG. 9, FIG. 14, FIG. 15, FIG. 16, FIG.18, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 30, FIG. 31, FIG. 43, and FIG. 45, FIG. 30 illustrates a bounding box axes determination flowchart 3000, which describes the process of determining bounding box axes by a shadow caster scanner configured for identifying materials or distinguishing between different tissues, according to some embodiments of the present disclosure. These bounding boxes are used in the flowcharts of FIG. 18, FIG. 20, FIG. 21, FIG. 22, and FIG. 23, as described above. Examples of bounding boxes are shown in FIG. 9, FIG. 14, FIG. 15, FIG.18, FIG. 31, FIG. 43, and FIG. 45. The first step of bounding box axes determination flowchart 3000 is the bounding boxes scan 3D surface step 3010 in which an object’s three-dimensional surface is scanned by a shadow caster scanner of the present disclosure. Next, in the bounding box calculate shadow plane normal step 3020, the normal of the plane (light plane 1617 in FIG. 16) defined by the light source and the shadow caster at each shadow caster position is calculated. Next, the surface normal vector (local surface normal vector 1630 in FIG. 16) is calculated at each point by using a covariance analysis algorithm, or the like, in the bounding box calculate normal vector step 3030. Next, in the bounding box define shadow edge vector step 3040, the shadow plane normal calculated above is cross multiplied with the surface normal vector calculated above at each point to define the shadow edge vector (in FIG. 16) along the surface. Next, in the bounding box define shadow edge normal step 3050, the shadow normal vector calculated above is cross multiplied with the surface edge vector calculated above at each point to define the normal to the shadow edge vector (shadow direction vector 1632 in FIG. 16) along the surface. Next, in the define local coordinate system step 3060, the local coordinate system of the bounding box volume is defined at each point with the “X” direction of the bounding volume, or bounding box, being the shadow edge vector calculated above, with the “Y” direction of the bounding volume being the normal to the shadow edge calculated above, and with the “Z” direction being the surface normal vector calculated above. (See Fig. 16 with shadow edge vector 1631 being the“X” axis, shadow direction vector 1632 being the “Y” axis, and local surface normal vector 1630 being the “Z” axis.) Finally, in the define bounding volume dimensions step 3070, the dimensions of the bounding volume are defined to have practical limits for each use case by balancing resolution and noise.

[0150] Referring now to FIG. 5 A, FIG. 5B, FIG. 16, FIG. 19, FIG. 30, FIG. 31, and FIG. 32, for the purposes of directional definitions for a shadow caster scanner configured for identifying materials and differentiating tissues, according to some embodiments of the present disclosure, FIG. 31 shows a diagram of phantom 3170 in the process being scanned by a shadow caster scanner of the present disclosure along with a top-down closeup diagram 3100b. (See FIG. 19 for methods of building a phantom.) Phantom diagram 3100a displays a perspective view of a three- dimensional phantom 3170 with the shadow edge plane 3150 cast at an angle through the center of the phantom 3170 dividing it into a light side 3121 and a dark side 3120. The shadow edge plane 3150 is the common plane that contains both the light source and edge of the shadow caster of the present disclosure. (See FIG. 5A and FIG. 5B.) In phantom diagram 3100a, region of interest 3111 is illustrated on the local surface 3115 of the phantom 3170. Top-down closeup diagram 3100b depicts a detailed view of the local surface 3115 in region of interest 3111 as seen from the perspective of the normal of the local surface 3115 at pixel of interest 3137, which is located at the center of region of interest 3111. In top-down closeup diagram 3100b, the light side 3121 and the dark side 3120 are divided by the shadow edge plane 3150. The X-axis 3161 and Y-axis 3162 of the bounding box 3140 intersect at the pixel of interest 3137. Local X 3132 is parallel to the X-axis 3161, and local Y 3131 is parallel to the Y-axis 3162. Bounding box 3140 comprises local X 3132 and local Y 3131. (See FIG. 30 and FIG. 32 for methods involving bounding boxes or volumes.) Shadow edge vector 3135 starts at the pixel of interest 3137 and runs along the X-axis 3161. Local X 3132 and local Y 3131 are defined by the angle of the shadow edge vector 3135 with local X 3132 being parallel to shadow edge vector 3135 and local Y 3131 being perpendicular to shadow edge vector 3135. Local Z 3133 is defined by the surface normal perpendicular to the phantom’s 3170 local surface 3115 at the pixel of interest 3137. (See FIG. 16.)

[0151] Referring now to FIG. 9, FIG. 14, FIG. 15, FIG. 16, FIG.18, FIG. 20, FIG. 21, FIG.22, FIG. 23, and FIG. 24, FIG. 31, FIG. 32, FIG. 43, and FIG. 45, FIG. 32 demonstrates abounding box dimension determination flowchart 3200, which describes the process of defining bounding box dimensioned used by a shadow caster scanner configured for identifying materials or distinguishing between different tissues, according to some embodiments of the present disclosure. These bounding boxes are used in the flowcharts of FIG. 18, FIG. 20, FIG. 21, FIG. 22, and FIG. 23, as described above. Examples of bounding boxes are shown in FIG. 9, FIG. 14, FIG. 15, FIG. 18, FIG. 31, FIG. 43, and FIG. 45. The first step of bounding box dimension determination flowchart 3200 is the align scanner step 3210 in which a shadow caster scanner of the present disclosure is aligned with an object subject to material investigation or tissue differentiation. Next, in the bounding box prior knowledge decision step 3220, whether there is prior knowledge of the expected reflectance profile lengths within the scene to which the shadow caster scanner of the present disclosure is aligned is determined. If there is not prior knowledge of the expected reflectance profile lengths within the scene, then an initial scan of the scene is made by the shadow caster scanner of the present disclosure to identify the maximum expected reflectance profile length in the initial scan step 3240. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 above for methods of determining reflectance profile length.) If there is prior knowledge of expected reflectance profile lengths within the scene to which the shadow caster scanner of the present disclosure is aligned, then use the prior knowledge of the materials in the scene to set the maximum expected reflectance profile length in the use prior knowledge step 3230. Next, in the define Z dimension step 3250, the Z dimension is set to the maximum expected reflectance profile length in the scene. Next, in the define Y dimension step 3260, the Y dimension is set to 2.5 times the maximum expected reflectance profile length in the scene. Finally, in the define X dimension step 3270, the X dimension is set to the minimum value that allows for at least 100 points per wavelength of light per distance in millimeters in the Y dimension. In this define X dimension step 3270, dynamic range is taken into account for defining the size of the x dimension. When the dynamic range is small, more points are needed to reduce noise. When the dynamic range is fully utilized, the amount of noise is reduced and the statistics are improved as the box size is increased; however, resolution is lost if the bounding box is too large. In that case the linear approximation of the shadow is no longer valid, and physical features finer than the bounding box may therefore be uncaptured, as the bounding box has an averaging effect.

[0152] Referring now to FIG. 16, FIG.18, FIG. 30, FIG. 32, FIG. 33, FIG 34, and FIG. 35, FIG. 33 illustrates a polarization determination flowchart 3300, which describes the process ofdetermining polarization with a shadow caster scanner configured for identifying materials or distinguishing between different tissues, according to some embodiments of the present disclosure. The first step of polarization determination flowchart 3300 is the polarization scan surface step 3310 in which a shadow caster scanner of the present disclosure scans the surface of an object subject to material investigation or tissue differentiation. Next, in the calculate surface normal vector step 3320, the surface normal vector is calculated at each point by using a covariance analysis algorithm, or the like. (See FIG. 16) Next, determine the local coordinated system at each three-dimensional coordinate in the determine local coordinate step 3330. (See FIG. 30 and FIG. 32 above for methods involving bounding boxes or volumes.) Next, in the project polarization vector step 3340, the polarization vector of the light from passing through non-opaque regions of the LCD shadow caster of the present disclosure is projected onto the local XZ plane. Finally, in the extract components step 3350, the P-polarization and S- polarization components are extracted from the projection of the polarization vector onto the local surface, which means that the polarization direction of the light as projected on the surface is known, thus allowing the profile length for a given S or P polarization ratio to be characterized and used in histograms, statistical analysis or by artificial intelligence. Variations detected in profile lengths as polarization is varied may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, and FIG. 35.

[0153] Referring now to FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 33, FIG. 34, and FIG. 44, FIG. 34 shows a look-up table creation flowchart 3400, which describes the process of building a look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like) for tissue types using a shadow caster scanner configured for identifying materials or distinguishing between different tissues according to some embodiments of the present disclosure, including training and testing an artificial intelligence algorithm, or the like, to build the look-up table, according to some examples. In FIG. 34, the first step of the look-up table creation flowchart 3400 is the scan multiple tissues step 3410 in which multiple tissue samples of the same tissue type are scanned using a shadow caster scanner configured for identifying different tissues. Next, in the label tissue type step 3420, known tissue types are labeled in the two-dimensional images captures by the camera of the shadow caster scanner of the present disclosure. Next, a statistical characterization of the physical features of the tissue (such as a histogram, or the like), which were captured in the scan is created in the createstatistical characterization step 3430. Relevant physical features of the tissue captured at each pixel, point, or three-dimension location by the shadow caster scanner of the present disclosure may include, but are not limited to: scatter width (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG.29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), reflectance profde length (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG.26, FIG. 27, FIG. 28, FIG.29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), shape of the reflectance profde (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG.29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), combined reflectance profile length of the tissue (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG.29), forward reflectance profile length from dark to light across the shadow edge or edges of luminosity (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG.29), backward reflectance profile length from light to dark across the shadow edge or edges of luminosity (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG.29), asymmetry (FIG. 11), anisotropy (FIG. 29, FIG. 42), polarization-dependent optical properties (FIG. 33), the angle of incidence on said object from one or more light sources (FIG. 39, FIG. 40, FIG. 41, FIG. 42, FIG. 43), the angle of observation from said one or more image capturing devices (FIG. 40, FIG. 41, and FIG. 42), optical parameters, pixel intensity (FIG. 9, FIG. 10, FIG. 12, FIG. FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 42, and FIG. 43), normalized pixel intensity (FIG. 20, FIG. 21 FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, and FIG. 37), intensity values (FIG. 9, FIG. 10, FIG. 12, FIG. FIG. 14, FIG. 15, FIG. 20, FIG. 21 , FIG. 22, FIG. 23, FIG. 24, FIG. 42, and FIG. 43), normalized intensity values (FIG. 20, FIG. 21 FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG.27, FIG. 28, FIG. 29, and FIG. 37), color intensity values (FIG. 37, FIG.42, and FIG.46), color intensity ratios (FIG. 37, FIG.42, and FIG.46), the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. Next, whether the scanned physical features in the statistical characterization are well determined is decided in the physical feature distribution decision step 3440. If the physical features are not well distributed, then additional scans of the tissue are taken to fill in physical feature parameters that do not have robust samples in the first statisticalcharacterization in the additional scans step 3450. Then, the label tissue type step 3420 and its following steps are repeated. If the physical features are well distributed in the statistical characterization, then the scans are analyzed for reflectance profde lengths in the analyze scans step 3460. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 above for methods of determining reflectance profde length.) Next, in the train algorithm step 3470, a classification algorithm is trained on known tissue types using all relevant physical features in order to build a look-up table. Finally, the trained classification algorithm is used to identify tissues in a new scene being scanned by a shadow caster scanner of the present disclosure in the new scene identification step 3480. See FIG. 44 below for a diagram illustrating a patient-non-specific process of training data and identifying pathological tissues.

[0154] Referring now to FIG. 9, FIG. 10, FIG. 11, FIG. 12, FIG. 14, FIG. 15, FIG. 17, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 33, FIG. 35, FIG. 37, FIG. 39, FIG. 40, FIG. 41, FIG. 42, FIG. 43 and FIG. 46, FIG. 35, and FIG.45, FIG. 35 shows a patient-specific look-up table creation flowchart 3500, which describes the process of building a look-up table (such as a reference library, machine learning algorithms, artificial intelligence algorithms, or the like) for tissue types of a specific patient using a shadow caster scanner configured for differentiating between different tissue types according to some embodiments of the present disclosure, including training and testing an artificial intelligence algorithm, or the like, to build the look-up table for a specific patient, according to some examples. In FIG. 35, the first step of the specific patient look-up table creation flowchart 3500 is the scan patient-specific tissues step 3510 in which multiple tissue samples from a specific patient are scanned using a shadow caster scanner configured for identifying different tissues. Next, in the label patient-specific tissue type step 3520, known tissue types of the specific patient are labeled in the two-dimensional images captures by the camera of the shadow caster scanner of the present disclosure. Next, a statistical characterization of the physical features of the specific patient’s tissue (such as a histogram, or the like), which were captured in the scan, is created in the create patient-specific statistical characterization step 3530. As above, relevant physical features of the tissue captured at each pixel, point, or three-dimension location by the shadow caster scanner of the present disclosure may include, but are not limited to: scatter width (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), reflectance profile length (FIG. 14, FIG. 15, FIG.20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), shape of the reflectance profile (FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, FIG. 39, FIG. 41, FIG. 42, and FIG. 43), combined reflectance profile length of the tissue (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG. 29), forward reflectance profile length from dark to light across the shadow edge or edges of luminosity (FIG. 21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG. 29), backward reflectance profile length from light to dark across the shadow edge or edges of luminosity (FIG.21, FIG. 22, FIG. 23, FIG. 25, FIG. 28, and FIG. 29), asymmetry (FIG. 11), anisotropy (FIG. 29,FIG. 42), polarization-dependent optical properties (FIG. 33), the angle of incidence on said object from one or more light sources (FIG. 39, FIG. 40, FIG. 41, FIG. 42, FIG. 43), the angle of observation from said one or more image capturing devices (FIG. 40, FIG. 41, and FIG. 42), optical parameters, pixel intensity (FIG. 9, FIG. 10, FIG. 12, FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 42, and FIG. 43), normalized pixel intensity (FIG. 20, FIG. 21FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, and FIG. 37), intensity values (FIG. 9, FIG. 10, FIG. 12, FIG. FIG. 14, FIG. 15, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 24, FIG. 42, and FIG. 43), normalized intensity values (FIG. 20, FIG. 21 FIG. 22, FIG. 23, FIG. 24, FIG. 25, FIG. 26, FIG. 27, FIG. 28, FIG. 29, and FIG. 37), color intensity values (FIG.37, FIG. 42, and FIG. 46), color intensity ratios (FIG. 37, FIG. 42, and FIG. 46), the angular extent of linear illumination, layering of the scattering medium, or the like. These physical features are also useful for the identification and characterization of anatomical features, including, but are not limited to: vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, adjacent tissue, or the like. Next, whether the scanned patient-specific physical features in the statistical characterization are well determined is decided in the patient-specific physical feature distribution decision step 3540. If the physical features are not well distributed, then additional scans of the specific patient’s tissue are taken to fill in physical feature parameters that do not have robust samples in the first statistical characterization in the additional patient-specific scans step 3550. Then, the label patient-specific tissue type step 3520 and its following steps are repeated. If the physical features are well distributed in the statistical characterization, then the scans are analyzed for reflectance profile lengths in the analyze patient-specific scans step 3560. (See FIG. 20, FIG. 21, FIG. 22,and FIG. 23 above for methods of determining reflectance profde length.) Next, in the patientspecific train algorithm step 3570, a classification algorithm is trained on known patient-specific tissue types using all relevant physical features in order to build a patient-specific look-up table, patient-specific reference library, patient-specific machine learning algorithm, patient-specific artificial intelligence algorithms, patient-specific machine learning component, or the like. Finally, the trained patient-specific classification algorithm is used to test unlabeled portions of a scan by a shadow caster scanner of the present disclosure in order to identify unknown patientspecific tissues in the patient-specific unknown tissue identification step 3580. See FIG. 45 below for a diagram of a patient-specific process of training data and identifying pathological tissues.

[0155] Referring now to FIG.19, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 30, and FIG. 32, and FIG. 36, FIG. 36 depicts look-up table material identification flowchart 3600, which describes the process of using a look-up table to identify materials in a three-dimensional object using a shadow caster scanner configured for identifying materials, according to some embodiments of the present disclosure. The first step of the look-up table material identification flowchart 3600 is the build look-up table step 3610 in which a look-up table of scatter widths is built using well characterized optical phantoms, which were prepared by the method described in FIG. 19. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 above for methods of determining scatter widths or reflectance profile length.) Next, if applicable, down-select from the look-up table to fit the specific application for the material identification in the down-select step 3620. Next, in the look-up table scan surface step 3630, the surface of a material being identified is scanned using a shadow caster scanner configured for identifying materials. Next, in the sample size determination step 3640, the sample size and region size for fitting data is determined. (See FIG. 30 and FIG. 32 above for methods involving bounding boxes or volumes.) Next, in the reflectance profile length determination step 3650, a curve is fit to the data from the scan within the determined sample size and region in order to determine the reflectance profile lengths. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 above for methods of determining reflectance profile length.) Next, the reflectance profile lengths are compared to the values in the built look-up table in the compare reflectance profile lengths step 3660. Finally, in the identify candidate materials step 3670, the candidate materials in the scanned scene are identified by matching values in the look-up table. Note, that the present disclosure, a shadow caster scanner configured foridentifying materials, scans in three-dimensions and also measures light scattered into the three- dimensional subsurface of the object. The present disclosure has the advantage of being able to identify materials not only on the surface of the object, but also the ability to locate different materials within three-dimensional space, including different materials within the subsurface of the three-dimensional object.

[0156] Referring now to FIG. 18, FIG. 19, and FIG. 37, FIG. 37 shows three sets of graphs, which compare the normalized intensity of different colors or wavelengths of light of optical phantoms as a function of distance across a shadow edge for three different types of tissue found in brains, which were scanned by a shadow caster scanner of the present disclosure for the purposes of three-dimensional tissue characterization. (See FIG. 19 for methods of building optical phantoms.) The light source of shadow caster scanner of the present disclosure, which is configured to characterize tissue, may use different colored lights, use color filters, may be polarized, or may comprise a white light source, according to some examples. For a white light source, the responses of different wavelengths may be extracted from the raw images captured by the camera of the shadow caster scanner using the processor configured accordingly. The graphs in FIG. 37 effectively describe the different responses of different colored light scattering into the subsurface of different tissue types. Grey matter color comparison graph 3700a depicts the relative normalized intensity of red and blue light as a function of distance in millimeters across a sharp shadow edge and into the shadow for an optical phantom mimicking grey matter of a brain. Color comparison horizontal axis 3774 shows the distance in pixels into the shadow, and the color comparison vertical axis 3773 shows the relative normalized intensity. In grey matter color comparison graph 3700a, as shown in the color comparison legend 3771, the grey matter red curve 3743 a shows the normalized intensity of red light as a function of distance into the shadow, and grey matter blue curve 3745a shows the normalized intensity of blue light as a function of distance into the shadow. The grey matter error bars 3741a show the root mean squared (RMS) error, which is the square root of squared error amounts of the grey matter measurements. White matter color comparison graph 3700b depicts the relative normalized intensity of red and blue light as a function of distance in millimeters across a sharp shadow edge and into the shadow for an optical phantom mimicking white matter of a brain. Again, color comparison horizontal axis 3774 shows the distance in pixels into the shadow, and the color comparison vertical axis 3773 shows the relative normalized intensity. In white matter colorcomparison graph 3700b, as shown in the color comparison legend 3771, the white matter red curve 3743b shows the normalized intensity of red light as a function of distance into the shadow, and white matter blue curve 3745b shows the normalized intensity of blue light as a function of distance into the shadow. The white matter error bars 3741b show the root mean squared (RMS) error, which is the square root of squared error amounts of the white matter measurements. Astrocytoma color comparison graph 3700c depicts the relative normalized intensity of red and blue light as a function of distance in millimeters across a sharp shadow edge and into the shadow for an optical phantom mimicking astrocytoma grade-II tumors of a brain. Again, color comparison horizontal axis 3774 shows the distance in pixels into the shadow, and the color comparison vertical axis 3773 shows the relative normalized intensity. In astrocytoma color comparison graph 3700c, as shown in the color comparison legend 3771, the astrocytoma red curve 3743c shows the normalized intensity of red light as a function of distance into the shadow, and astrocytoma blue curve 3745c shows the normalized intensity of blue light as a function of distance into the shadow. The astrocytoma error bars 3741c show the root mean squared (RMS) error, which is the square root of squared error amounts of the astrocytoma measurements. Note the stark differences between the relative shapes of the grey matter red curve 3743a and grey matter blue curve 3745a as compared to the white matter red curve 3743b and white matter blue curve 3745b, as compared to the astrocytoma red curve 3743c and the astrocytoma blue curve 3745c. These differences aid in allowing the system of the present disclosure to characterize different tissue types, and may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, and FIG. 35. Also, it should be noted that the astrocytoma red curve 3743c is not distinct from grey matter red curve 3743a, so by itself, red color would not distinguish. Similarly, the astrocytoma blue curve 3745c would not clearly distinguish from the white matter blue curve 3745b and thus, by itself, blue color would not distinguish. It is only in combination of red and blue that astrocytoma can be identified.

[0157] Referring now to FIG. 20, FIG. 21, FIG. 22, FIG. 23, and FIG. 38, FIG. 38 shows a graph, which describes the reflectance profile length in millimeters as a function of percentage of blood oxygen in a human hand, which was deprived of oxygen for a short time while being scanned by a shadow caster scanner of the present disclosure for the purposes of three- dimensional tissue characterization. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 above for methods of determining reflectance profile length.) The graph in FIG. 38 effectively describeschanges in healthy tissue, which can be identified by the present disclosure. Oxygen level comparison graph 3800 depicts the reflectance profile length in millimeters measured across a sharp shadow edge of the present disclosure as a function of measured blood oxygen percentage for a human hand that was deprived oxygen for 270 seconds. Oxygen level comparison vertical axis 3873 shows the reflectance profile length in millimeters as measured by the present disclosure, and oxygen level comparison horizontal axis 3874 shows the oxygenation percentage of blood measured by a blood oxygen meter. The 0-second reflectance profile length 3843 a indicates the reflectance profile length of a hand scanned by the present disclosure at the moment that oxygen is deprived to the hand, or zero (0) seconds, and the width of the 0-second oxygen percentage standard deviation box 3841a represents the standard deviation of the blood oxygen percentage measurement at the moment that oxygen is deprived to the hand. The 90-second reflectance profile length 3843b indicates the reflectance profile length of a hand scanned by the present disclosure after ninety (90) seconds of being deprived of oxygen, and the width of the 90-second oxygen percentage standard deviation box 3841b represents the standard deviation of the blood oxygen percentage measurement of the hand after ninety (90) seconds of being deprived of oxygen. The 180-second reflectance profile length 3843c indicates the reflectance profile length of a hand scanned by the present disclosure after one hundred and eighty (180) seconds of being deprived of oxygen, and the width of the 180-second oxygen percentage standard deviation box 3841c represents the standard deviation of the blood oxygen percentage measurement of the hand after one hundred and eighty (180) seconds of being deprived of oxygen. The 270-second reflectance profile length 3843d indicates the reflectance profile length of a hand scanned by the present disclosure after two hundred and seventy (270) seconds of being deprived of oxygen, and the width of the 270-second oxygen percentage standard deviation box 384 Id represents the standard deviation of the blood oxygen percentage measurement of the hand after two hundred and seventy (270) seconds of being deprived of oxygen. This oxygen level comparison graph 3800 clearly demonstrates that the present disclosure can distinguish between healthy tissue and damaged tissue even when the damage, in this case oxygen deprivation, occurs in a relatively brief amount of time and to the same tissue, which was originally healthy.

[0158] Referring now to FIG. 18, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 34, FIG. 35, FIG.39, and FIG. 40, FIG. 39 shows a diagram with four graphs, which describe how the angle ofmeasurement affects the reflectance profile length for a bottom and top shadow edge when scanning a phantom’s surface with a tissue-characterizing shadow caster scanner of the present disclosure, according to some embodiments. (See FIG. 20, FIG. 21, FIG. 22, and FIG. 23 above for methods of determining reflectance profile length.) In all four graphs, angle of measurement vertical axis 3973 shows the reflectance profile length in millimeters, and angle of measurement horizontal axis 3974 shows the angle of incidence of light in degrees. Bottom experimental angle of measurement graph 3900a charts bottom experimental data 3931a, which are experimental results for reflectance profile length as a function of light angle of incidence as measured by the present disclosure at the bottom edge of the shadow, where the angle of measurement 3947 between the light source direction 3910 and camera direction 3917 at a central point 3945 on the phantom surface 3943 is thirty (30) degrees. (See FIG. 40 for a diagram describing the angle of incidence and the angle of observation of the shadow caster camera.) Bottom simulated angle of measurement graph 3900b displays bottom simulated data 3931b, which are experimental results for reflectance profile length as a function of light angle of incidence as measured by the present disclosure at the bottom edge of the shadow, where the angle of measurement 3947 between the light source direction 3910 and camera direction 3917 at a central point 3945 on the phantom surface 3943 is thirty (30) degrees. Note that the bottom simulated data 393 lb does not match the bottom experimental data 3931a. However, the behavior of the experimental reflectance profile length does match the behavior of the simulated reflectance profile length (e.g., especially for light angles of incidence of < -30 degrees), but is offset by approximately 0.1 mm. Top experimental angle of measurement graph 3900c plots top experimental data 3931c, which are experimental results for reflectance profile length as a function of light angle of incidence as measured by the present disclosure at the top edge of the shadow, where the angle of measurement 3947 between the light source direction 3910 and camera direction 3917 at a central point 3945 on the phantom surface 3943 is thirty (30) degrees. Top simulated angle of measurement graph 3900d displays top simulated data 393 Id, which are experimental results for reflectance profile length as a function of light angle of incidence as measured by the present disclosure at the bottom edge of the shadow, where the angle of measurement 3947 between the light source direction 3910 and camera direction 3917 at a central point 3945 on the phantom surface 3943 is thirty (30) degrees. Note that the top simulated data 393 Id matches the top experimental data 3931c quite well. These four graphs demonstrate how back scattering acrossthe edge of the sharp shadow into the shadow region is dependent on the angle of observation.Angle of incidence and angle of observation may be used as an optical characteristic, or physical feature, in the methods of FIG. 18, FIG. 34, and FIG. 35.

[0159] Referring now to FIG. 40, FIG. 40 shows diagram, which explains the terminology of various angles used with a shadow caster scanner of the present disclosure, according to some embodiments. In FIG. 40, observation reference system diagram 4000 demonstrates an arrangement of an observation shadow caster 4015, an observation light source 4003, and observation camera 4001 of the present disclosure for a given point 4049 on an observation object 4045 being scanned, according to some examples. In FIG. 40, in the example shown, the observation light source 4003 has an angle of incidence of twenty -five (25) degrees from the normal to the observation surface 4047 of the observation object 4045, which is zero (0) degrees in the diagram. Observation shadow caster 4015 has an edge 4017, which shares a common plane 4019 with the observation light source 4003 and casts a sharp shadow onto the observation object 4045 creating a lit region 4025 and a shadow region 4020 on the observation surface 4047 with an observation shadow edge 4050 between the two regions. An observation camera 4001 of the present disclosure, which is shown at an angle between thirty degrees (30°) and forty-five degrees (45°) from the normal to the observation surface 4047 of the observation object 4045, which is zero (0) degrees in the diagram. The angle of the observation camera 4001 may be set at a wide range of angles independent from the observation light source 4003 and observation shadow caster 4015, which has an edge 4017 that shares a common plane 4019 with the observation light source 4003.

[0160] Referring now to FIG. 18, FIG. 20, FIG. 21, FIG. 22, FIG. 23, FIG. 34, FIG. 35, and FIG. 41, FIG. 41 shows a series of graphs, which demonstrate the reflectance profile length and relative intensity as functions of the angular position of the camera of a shadow caster scanner of the present disclosure for various angles of incidence of light across a bottom sharp shadow edge and top sharp shadow edge, according to some embodiments. (See FIG. 20, FIG. 2...

Claims

What is claimed is:

1. A shadow caster scanner system for characterizing one or more materials in an object, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters; wherein said one or more light sources are configured to illuminate said one or more shadow casters to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; wherein said shadow caster scanner system is configured to project said one or more edges of luminosity across said object; wherein said one or more image capture devices captures one or more images of said one or more edges of luminosity on said object and records said one or more images into said memory; wherein said processor forms a three-dimensional data representation of said object from said recorded one or more images; and wherein said processor extracts physical features of said object from said one or more images using said three-dimensional data representation; and wherein said processor uses said physical features to characterize said one or more materials of said object.

2. The shadow caster scanner system of claim 1, wherein said one or more shadow casters comprises a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources.

3. The shadow caster scanner system of claim 2, further comprising one or more actuators configured to move said one or more shadow casters.

4. The shadow caster scanner system of claim 3, wherein said shadow caster scanner system is configured to project said one or more edges of luminosity across said object by using said one or more actuators to move said one or more shadow casters to sweep said one or more edges of luminosity across said object.

5. The shadow caster scanner system of claim 1, further comprising a controller configured to interact with said processor, and wherein said one or more shadow casters comprises a transparent liquid crystal matrix controllable by said controller and configured to generate opaque regions or patterns, said opaque regions or patterns comprising a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources.

6. The shadow caster system of claim 5, wherein said shadow caster scanner system is configured to project said one or more edges of luminosity across said object by using said controller to generate a series of said opaque regions or said patterns on said transparent liquid crystal matrix in order to project said one or more edges of luminosity across said object.

7. The shadow caster scanner system of claim 1, wherein said one or more materials comprises tissue.

8. The shadow caster scanner system of claim 1, wherein the one or more light sources comprises one or more linear light sources.

9. The shadow caster scanner system of claim 1, wherein said one or more light sources comprise color filters or polarization filters.

10. The shadow caster scanner system of claim 1, wherein the physical features comprise one or more of surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity,normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, and layering of the scattering medium.

11. The shadow caster scanner system of claim 1, wherein said processor is configured to characterize said one or more materials as anatomical features.

12. The shadow caster scanner system of claim 11, wherein the anatomic features comprise one or more of vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, and adjacent tissue.

13. The shadow caster scanner system of claim 1, wherein the processor is configured to generate one or more false-color models of said object and display said object with said one or more materials characterized with false colors.

14. A method for characterizing one or more materials in an object, said method comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters; scanning a surface of said object with said shadow caster scanner system, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices;recording said one or more images into said memory; forming a three-dimensional data representation of said object from recorded said one or more images; determining physical features or optical characteristics of said object from said one or more images; and characterizing said one or more materials of said object using said physical features or optical characteristics.

15. The method of claim 14, wherein said one or more shadow casters comprises a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources.

16. The method of claim 15, wherein said shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters.

17. The method of claim 16, wherein projecting said one or more edges of luminosity across said object comprises using said one or more actuators to move said one or more shadow casters to sweep said one or more edges of luminosity across said object.

18. The method of claim 14, wherein said shadow caster scanner system further comprises a controller configured to interact with said processor, and wherein said one or more shadow casters comprises a transparent liquid crystal matrix controllable by said controller and configured to generate opaque regions or patterns, said opaque regions or patterns comprising a shape with at least one edge, said edge being contained within a plane, which contains said one or more light sources.

19. The method of claim 18, wherein projecting said one or more edges of luminosity across said object comprises using said controller to generate a series of said opaque regions or patterns on said transparent liquid crystal matrix in order to project said one or more edges of luminosity across said object.

20. The method of claim 14, wherein said one or more materials comprises tissue.21 . The method of claim 14, wherein the one or more light sources comprises one or more linear light sources.

22. The method of claim 14, wherein said one or more light sources comprises color fdters or polarization filters.

23. The method of claim 14, wherein the physical features comprise at least one of surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, and layering of the scattering medium.

24. The method of claim 14, wherein the processor is configured to characterize said one or more materials as anatomical features.

25. The method of claim 14, wherein the anatomical features comprise one or more of vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, and adjacent tissue.

26. The method of claim 14 further comprising: generating one or more false-color models of said object; and displaying said object with said one or more materials characterized with false colors.

27. The method of claim 14, wherein the step of determining physical features or optical characteristics of said object comprises determining physical features from said one or more images using said three-dimensional data representation and said processor.

28. The method of claim 14, further comprising: providing a look-up table, said look up table comprising known optical characteristics of known materials;29. The method of claim 28, wherein each said one or more images comprises pixels, a central vertex comprising the center of each said one or more images, lit regions illuminated by said one or more light sources, and shadow regions comprising said sharp shadows.

30. The method of claim 29, further comprising the step of: determining a wavelength range that produces the largest signal-to-noise ratio between said shadow regions and said lit regions.

31. The method of claim 30, wherein the step of constructing a three-dimensional representation of said surface of said object comprises constructing a three-dimensional representation of said surface of said object using said recorded one or more images and said wavelength range;32. The method of claim 31, further comprising the steps of: identifying a shadow frame, said shadow frame comprising said recorded said images in which said one or more edges of luminosity lie across the viewing area of each pixel; defining a region of interest for each said central vertex, said region of interest comprising a rectangular projection on said surface of said object for determining the optical characteristics of said central vertex; determining intensity values for each said central vertex of said recorded said one or more images; determining a shadow edge vector, said shadow edge vector lying along said one or more edges of luminosity; for each said central vertex, integrating said intensity values of neighboring vertices within said region of interest along said shadow edge vector to determine the light intensity profile, said neighboring vertices being said central vertex of each adjacent one or more images; anddetermining the optical characteristics of said one or more materials associated with each said central vertex.

33. The method of claim 32, wherein the step of characterizing said one or more materials of said object using said physical features or optical characteristics comprises: comparing said optical characteristics of said one or more materials associated with each said central vertex to said known optical characteristics of said known materials in said look-up table, thereby identifying said one or more materials of said object.

34. The method of claim 29, wherein the central vertex comprises a pixel that lies upon the shadow edge vector in the center of each region of interest.

35. The method of claim 29, further comprising: assigning an optical characteristic value or physical feature value to the central vertex of each region of interest.

36. The method of claim 29, further comprising: rastering each central vertex throughout the whole image space to keep the size of the region of interest in physical parameters the same.

37. A method of determining the scattering width, or reflectance profile length, of one or more materials in an object to characterize said one or more materials in the object, said method comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium;one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said shadow caster scanner system, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising pixel intensities; recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising data points; analyzing said data points; determining a shadow edge location; and determining reflectance profile length extending into the shadow.

38. The method of claim 37, wherein said step of analyzing said data points comprises: calculating a shadow plane normal at each of said one or more shadow caster positions, said shadow plane normal comprising the normal to said common plane; calculating a surface normal vector at each of said points; defining a shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector;defining a linear approximation of a local shadow edge using a normal to the pixel of interest and said shadow edge vector; determining a sample size and bounding volume for fitting data; computing a point distance between each of said points within said bounding volume and said linear approximation of said local shadow edge; normalizing said pixel intensities; and creating a plot of data by plotting said point distance as a function of normalized said pixel intensities.

39. The method of claim 38, wherein said step of determining a shadow edge location comprises: defining knots across said plot; applying a spline fit to said plot using said knots; defining the shadow edge location by determining the steepest slope of said spline fit between said knots; and creating a fit curve by fitting the sum of two exponentials to said data starting at said shadow edge location and going into the shadow region,40. The method of claim 39, wherein said reflectance profile length is the distance between said shadow edge location and the position of said fit curve at a characteristic length at said shadow edge location.

41. The method of claim 40, wherein said characteristic length is half of normalized said pixel intensities.

42. The method of claim 40, wherein said characteristic length is 1 / e or 1 / e2.

43. The method of claim 40, wherein the steepest slope of said spline fit between said knots is determined at a wavelength at which the scattering is high to ensure that the shadow is sharp, thereby providing an edge location that conforms to the actual surface.

44. The method of claim 37, wherein said step of analyzing data points comprises: creating a plot of data using said points; applying a spline fit to said plot;defining the shadow edge location by determining the steepest slope of said spline fit; and creating a fit curve by fitting the sum of two exponentials to said data starting at said shadow edge location and going into the shadow region.

45. The method of claim 44, wherein said reflectance profile length is the distance between said shadow edge location and the position of said fit curve at a characteristic length at said shadow edge location.

46. The method of claim 44, wherein said characteristic length is half of normalized said pixel intensities.

47. The method of claim 44, wherein said characteristic length is 1 / e or 1 / e2.

48. The method of claim 39, wherein said step of analyzing said data points comprises: creating a plot of data using said points; applying a spline fit to said plot; and defining the shadow edge location by determining the steepest slope of said spline fit.

49. The method of claim 48, wherein the reflectance profile length is determined starting at said shadow edge location and going into said sharp shadow.

50. The method of claim 38, wherein the surface normal vector is calculated using a covariance analysis algorithm.

51. The method of claim 37, wherein said one or more images further comprises: one or more positive frames, said one or more positive frames being said one or more images showing a positive transition from a shadow region to an illuminated region across said one or more edges of luminosity; and one or more negative frames, said one or more negative frames being said one or more images showing a negative transition from said illuminated region to said shadow region across said one or more edges of luminosity.

52. The method of claim 51, wherein said step of analyzing said data points comprises: smoothing said data using a symmetric averaging filter; creating an absolute difference curve by taking the absolute value of the difference between said positive frame and said negative frame; defining the shadow edge location by determining the location of the minimum value of said absolute difference curve; and creating a fit curve by fitting the sum of two exponentials to said data starting at said shadow edge location and going into said sharp shadow.

53. The method of claim 52, wherein said reflectance profile length is the distance between said shadow edge location and the position of said fit curve at a characteristic length at said shadow edge location.

54. The method of claim 53, wherein said characteristic length is half of normalized said pixel intensities.

55. The method of claim 53, wherein said characteristic length is 1 / e or 1 / e2.

56. The method of claim 51, wherein said step of analyzing said data points comprises: smoothing said data using a symmetric averaging filter; creating an absolute difference curve by taking the absolute value of the difference between said positive frame and said negative frame; and defining the shadow edge location by determining the location of the minimum value of said absolute difference curve.

57. The method of claim 56, wherein the reflectance profile length is determined starting at said shadow edge location and going into said sharp shadow.

58. The method of claim 37, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

59. The method of claim 37, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

60. The method of claim 52, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

61. The method of claim 53, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

62. The method of claim 37, wherein said one or more materials comprises tissue.

63. The method of claim 37, wherein said one or more light sources comprises one or more linear light sources.

64. The method of claim 37, wherein said one or more light sources comprises color filters or polarization filters.

65. The method of claim 37, further comprising: generating one or more false-color models of said object; and displaying said object with said one or more materials characterized with false colors.

66. A method for normalizing pixel intensities of an object, said method comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium;a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said shadow caster scanner system, said scanning comprising: illuminating said object with said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said object and said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising pixel intensities; recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor; defining a maximum frame, said maximum frame being one of said one or more images showing a region without a shadow cast onto it; defining a minimum frame, or region of non-illumination, for each said shadow caster position, said minimum frame being one of said one or more images in which said sharp shadows cast by said one or more shadow casters cover least 5 mm on each side of said one or more edges of luminosity, or a range or distance that is relatively long as compared to the profile length; and normalizing said pixel intensities in each of said one or more images using minimum-maximum feature scaling by scaling said minimum frame and said maximumframe or by scaling the difference between said minimum frame and said maximum frame.

67. The method of claim 66, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

68. The method of claim 66, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

69. The method of claim 68, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

70. The method of claim 69, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

71. The method of claim 66, wherein said one or more light sources comprises color filters or polarization filters.

72. The method of claim 66, further comprising: generating one or more false-color models of said object; and displaying said object with said one or more materials characterized with false colors.

73. A method for determining axes of a bounding box volume on an object, said method comprising:providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the three-dimensional surface of said object with said shadow caster scanner system, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters, said projecting comprising one or more shadow caster positions, said one or more shadow caster positions being a position of said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: pixel intensities; one or more positive frames, said one or more positive frames being said one or more images showing a positive transition from an unilluminated region to an illuminated region across said one or more edges of luminosity; and one or more negative frames, said one or more negative frames being said one or more images showing a negative transition from said illuminated region to said unilluminated region across said one or more edges of luminosity;recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three-dimensional data representation comprising points, said points comprising one or more of coordinate information, three-dimension coordinate information, color or wavelength information, associated normal information, and associated profde length; calculating the shadow plane normal at each of one or more shadow caster positions, said shadow plane normal comprising the normal to said common plane; calculating the surface normal vector at each of said points; defining the shadow edge vector along said three-dimensional surface at each of said points by cross multiplying said shadow plane normal with said surface normal vector; defining the normal to the shadow edge along said three-dimensional surface by cross multiplying said surface normal vector with said shadow edge vector at each of said points; defining a local coordinate system of said bounding box volume at each point, said local coordinate system comprising: an X direction of said bounding box volume, said X direction comprising said shadow edge vector; a Y direction of said bounding volume, said Y direction comprising said normal to said shadow edge; and a Z direction of said bounding volume, said Z direction comprising said surface normal vector; and defining the dimensions of the bounding volume to have practical limits for each use case by balancing resolution and noise.

74. The method of claim 73, wherein the surface normal vector is calculated using a covariance analysis algorithm.

75. The method of claim 73, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projectingsaid one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

76. The method of claim 73, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

77. The method of claim 76, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

78. The method of claim 77, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

79. The method of claim 73, wherein said one or more light sources comprises color filters or polarization filters.

80. A method for determining bounding box dimensions of an object, said method comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources;aligning said shadow caster scanner system to said object, said aligning said shadow caster scanner system comprising a scene; determining the maximum expected reflectance profile length, said maximum expected reflectance profile length comprising the maximum distance light is scattered into shadow; setting the side lengths of a bounding box, said setting the side lengths of said bounding box comprising: setting a Z length, said Z length comprising said maximum expected reflectance profile length in said scene in the Z direction, said Z direction comprising said surface normal vector; setting a Y length, said Y length comprising: 2.5 times said maximum expected reflectance profile length in said scene in said Y direction, said Y direction comprising said normal to said shadow edge; setting an X length, said X length comprising the minimum value that allows for at least 100 points per wavelength of light per distance in millimeters in said Y length in said X direction.

81. The method of claim 80, wherein determining said maximum expected reflectance profile length comprises scanning the three-dimensional surface of said object with said apparatus, said scanning comprising: illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices, said one or more images comprising: scenes; recording said one or more images into said memory; and identifying said maximum expected reflectance profile length.

82. The method of claim 80, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

83. The method of claim 80, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

84. The method of claim 83, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

85. The method of claim 84, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

86. The method of claim 80, wherein said one or more light sources comprises color filters or polarization filters.

87. A method for determining polarization in a scan of an object, said method comprising: scanning the three-dimensional surface of said object; calculating the surface normal vector at each 3D coordinate; determining local coordinate system at each three-dimension coordinate of said three-dimensional surface of said object, said local coordinate system comprising a local XZ plane; projecting the polarization vector of light onto a projection on said local XZ plane; and extracting P-polarization and S-polarization components from said projection.

88. The method of claim 87, wherein scanning said three-dimension surface of said object comprises: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said object; projecting said one or more edges of luminosity across said object using said one or more shadow casters; capturing one or more images of said one or more edges of luminosity on said object using said one or more image capture devices; recording said one or more images into said memory; and forming a three-dimensional data representation of said three-dimensional surface of said object from recorded said one or more images using said processor, said three- dimensional data representation comprising 3D coordinates.

89. The method of claim 87, wherein the surface normal vector is calculated using a covariance analysis algorithm.

90. The method of claim 87, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.91 . The method of claim 87, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

92. The method of claim 91, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

93. The method of claim 92, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

94. The method of claim 87, wherein said one or more light sources comprises color filters or polarization filters.

95. A method of building and using a look-up table of known tissue types, said method comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning multiple tissue samples of the same tissue type with said shadow caster scanner system, said scanning comprising: illuminating said tissue samples with said one or more light sources;illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said tissue samples; projecting said one or more edges of luminosity across said tissue samples using said one or more shadow casters; capturing one or more two-dimensional images of said tissue samples and said one or more edges of luminosity on said tissue samples using said one or more image capture device, said two-dimensional images comprising said physical features of said tissue samples; and recording said one or more two-dimensional images into said memory; labeling known tissue types in said one or more two-dimensional images; creating a statistical characterization of said physical features of said tissue samples; repeating said labeling and said scanning until said physical features of said tissue samples are well distributed in said statistical characterization or sampled with statistical significance within the relevant parameter space of physical features; analyzing said two-dimensional images for reflectance profile lengths; training a classification algorithm on said known tissue types using all relevant said physical features, thereby building said look-up table; using trained said classification algorithm to identify unknown tissue being scanned by said apparatus by comparing said physical features of said unknown tissue types to said physical features in said look-up table.

96. The method of claim 95, wherein said one or more light sources comprises color filters or polarization filters.

97. The method of claim 95, wherein the physical features comprises at least one of surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one ormore image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like.

98. The method of claim 95, wherein said processor is configured to characterize said materials as anatomical features.

99. The method of claim 98, wherein said anatomical features comprise one or more of vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, and adjacent tissue.

100. The method of claim 95, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

101. The method of claim 95, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

102. The method of claim 101, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

103. The method of claim 102, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

104. The method of claim 95, wherein said statistical characterization of said physical features of said tissue samples comprises a histogram.

105. The method of claim 95, wherein said classification algorithm identifies said unknown tissue as being unknown, wherein said physical features of said unknown tissue are not found in said look-up table.

106. A method of building a patient-specific look-up table, said method comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources; scanning the surface of patient-specific tissue with said shadow caster scanner system, said scanning comprising: illuminating said patient-specific tissue with said one or more light sources; illuminating said one or more shadow casters with said one or more light sources to project sharp shadows of known geometry, which form one or more edges of luminosity on said patient-specific tissue; projecting said one or more edges of luminosity across said patientspecific tissue using said one or more shadow casters; capturing one or more two-dimensional images of said patient-specific tissue and said one or more edges of luminosity on said patient-specific tissue using said one or more image capture device, said two-dimensional images comprising physical features of said patient-specific tissue; and recording said one or more two-dimensional images into said memory;labeling known tissue types in said one or more two-dimensional images; creating a statistical characterization of said physical features of said patientspecific tissue; repeating said labeling and said scanning until said physical features of said patient-specific tissue are well distributed in said statistical characterization; analyzing said two-dimensional images for reflectance profile lengths; training a classification algorithm on said known tissue types using all relevant said physical features, thereby building said look-up table; testing unlabeled portions of said two-dimensional images in order to identify unknown patient-specific tissue using trained said classification algorithm by comparing said physical features of said unknown patient-specific tissue to said physical features in said look-up table.

107. The method of claim 106, wherein said patient-specific lookup table comprises one or more of a reference library, machine learning algorithm, and artificial intelligence algorithm.

108. The method of claim 106, wherein said one or more light sources comprises color filters or polarization filters.

109. The method of claim 106, wherein the physical features comprises at least one of surface curvature of the tissue, scatter width, reflectance profile length, shape of the reflectance profile, combined reflectance profile length of the tissue, reflectance profile length from dark to light or light to dark across the edges of luminosity or shadow edges, asymmetry, anisotropy, polarization-dependent optical properties, the angle of incidence on said object from said one or more light sources, the angle of observation from said one or more image capturing devices, optical parameters, pixel intensity, normalized pixel intensity, intensity values, normalized intensity values, color intensity values, color intensity ratios, the angular extent of linear illumination, layering of the scattering medium, or the like.

110. The method of claim 106, wherein said processor is configured to characterize said materials as anatomical features.

111. The method of claim 110, wherein said anatomical features comprise one or more of vascularization of the underlying tissue, blood vessels, nervous tissue, cancerous tissue, fat tissue, tumors, cartilage, bone, connective tissue, epithelial tissue, muscle tissue, organs, heathy tissue, diseased tissue, and adjacent tissue.

112. The method of claim 106, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

113. The method of claim 106, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllable by a controller using said processor and being configured to generate opaque regions or patterns.

114. The method of claim 113, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

115. The method of claim 114, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

116. The method of claim 106, wherein said statistical characterization of said physical features of said tissue samples comprises a histogram.

117. The method of claim 106, wherein said classification algorithm identifies said unknown tissue as being unknown, wherein said physical features of said unknown tissue are not found in said look-up table.

118. A method of using a look-up table to identify materials in an object, said method comprising:providing a well-characterized optical phantom; building a look-up table of scatter widths using said well-characterized optical phantoms; if applicable, down-selecting from said look-up table to fit a specific application or surgery type; scanning the surface of said object; determining the sample size and region size for fitting data; determining the reflectance profile lengths; comparing said reflectance profile lengths to those in said look-up table; and i dentifying candidate materials by matching said reflectance profile lengths to those in said look-up table.

119. The method of claim 118, further comprising: providing a shadow caster scanner system, said shadow caster scanner system comprising: one or more light sources; one or more image capture devices; a memory stored in non-transitory computer-readable medium; a processor configured for characterizing said materials, said processor comprising said computer-readable medium; one or more shadow casters, said one or more shadow casters comprising a shape with at least one edge, said edge being contained within a common plane, which contains said one or more light sources;120. The method of claim 119, wherein the shadow caster scanner system further comprises one or more actuators configured to move said one or more shadow casters, and projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises using said one or more actuators to move said one or more shadow casters.

121. The method of claim 119, wherein said one or more shadow casters comprises a transparent liquid crystal matrix, said transparent liquid crystal matrix being controllableby a controller using said processor and being configured to generate opaque regions or patterns.

122. The method of claim 120, wherein scanning the surface of said object with said apparatus comprises generating opaque regions or patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

123. The method of claim 121, wherein projecting said one or more edges of luminosity across said object using said one or more shadow casters comprises generating a series of said opaque regions or said patterns on said transparent liquid crystal matrix of said one or more shadow casters using said controller controlled by said processor.

124. The method of claim 119, wherein said one or more light sources comprises color filters or polarization filters.

125. A method of differentiating between materials forming an object, comprising: forming an initial three-dimensional data representation of an object from recorded images of edges of luminosity projected onto said object, the initial three- dimensional data representation comprising a plurality of data points; processing said initial three-dimensional data representation to generate a refined surface reconstruction of said object; performing statistical analysis of said plurality of data points of the initial three- dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction; using said average normal distances of said plurality of data points to infer optical penetration values of materials forming said object; and displaying, on a display device, a visual representation of said optical penetration values superimposed on at least one of a scan image of said object, the initial three- dimensional data representation of said object, and said refined surface reconstruction of said object, said visual representation comprising a first visual indicator corresponding to low optical penetration values and a second visual indicator corresponding to high optical penetration values; andidentifying different materials forming said object based upon said visual representation.

126. The method of claim 126, wherein the step of forming an initial three-dimensional data representation of an object from recorded images of one or more edges of luminosity on an object comprises: projecting sharp shadows of known geometry to form said one or more edges of luminosity on said object using at least one light source and at least one shadow casting element, said at least one shadow casting element being capable of generating opaque regions, said opaque regions comprising a shape with at least one edge, said edge being contained within a plane, which contains said at least one light source; generating a series of said opaque regions on said shadow casting element to project a pattern of said one or more edges of luminosity across said object; capturing images of said one or more edges of luminosity on said object with one or more image capture devices; and forming said initial three-dimensional data representation from said recorded images.

127. The method of claim 126, wherein said at least one light source is discrete or continuous.

128. The method of claim 126, wherein said at least one light source is linear.

129. The method of claim 126, wherein said at least one light source comprises one or more array of lights.

130. The method of claim 126, wherein the shape of the at least one shadow casting element is based on the object.

131. The method of claim 126, wherein said at least one shadow casting element further comprises color filters.

132. The method of claim 126, wherein said one or more shadow casting elements comprises a liquid crystal matrix.

133. The method of claim 126, wherein said three-dimensional data representation is displayed on said display device.

134. The method of claim 126, wherein the step of capturing images of said one or more edges of luminosity on said object comprises capturing images of said one or more edges of luminosity on said object using one or more image capture devices.

135. The method of claim 134, further comprising the step of: generating a three-dimensional model of said object using said three-dimensional data representation.

136. The method of claim 134, wherein said one or more shadow casters comprises at least one of a configurable opacity, a variable opacity, and a selectable opacity.

137. The method of claim 125, wherein the plurality of data points includes a plurality of supra-surface data points and a plurality of subsurface data points.

138. The method of claim 125, wherein the step of performing statistical analysis of said plurality of data points of the initial three-dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction comprises, for each point on the mesh: creating a cylinder aligned with the mesh normal; calculating a normal distance for each supra-surface point; and calculating a normal distance for each subsurface point.

139. A method of determining thickness of a layer of material forming an object, comprising: forming an initial three-dimensional data representation of an object from recorded images of edges of luminosity projected onto said object, the initial three- dimensional data representation comprising a plurality of data points; processing said initial three-dimensional data representation to generate a refined surface reconstruction of said object; performing statistical analysis of said plurality of data points of the initial three- dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction;using said average normal distances of said plurality of data points to infer optical penetration values of material layers forming said object; and comparing said inferred optical penetration values of said material layer of said object with a collection of inferred optical penetration values that were previously correlated with measured thickness of layers of similar material derived from similar objects to determine the thickness of said material layer of said object.

140. The method of claim 138, wherein the step of forming an initial three-dimensional data representation of an object from recorded images of one or more edges of luminosity on an object comprises: projecting sharp shadows of known geometry to form said one or more edges of luminosity on said object using at least one light source and at least one shadow casting element, said at least one shadow casting element being capable of generating opaque regions, said opaque regions comprising a shape with at least one edge, said edge being contained within a plane, which contains said at least one light source; generating a series of said opaque regions on said shadow casting element to project a pattern of said one or more edges of luminosity across said object; capturing images of said one or more edges of luminosity on said object with one or more image capture devices; and forming said initial three-dimensional data representation from said recorded images.

141. The method of claim 139, wherein said at least one light source is discrete or continuous.

142. The method of claim 139, wherein said at least one light source is linear.

143. The method of claim 139, wherein said at least one light source comprises one or more array of lights.

144. The method of claim 139, wherein the shape of the at least one shadow casting element is based on the object.

145. The method of claim 139, wherein said at least one shadow casting element further comprises color filters.

146. The method of claim 139, wherein said one or more shadow casting elements comprises a liquid crystal matrix.

147. The method of claim 139, wherein the step of performing statistical analysis of said plurality of data points of the initial three-dimensional data representation to determine average normal distances of said plurality of data points to said refined surface reconstruction comprises, for each point on the mesh: creating a cylinder aligned with the mesh normal; calculating a normal distance for each supra-surface point; and calculating a normal distance for each subsurface point.

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