Systems and methods for assessing topological characteristics of tissue - Patents.com

Abstract

The imaging system can include a plurality of image sensors configured to receive light in at least a first predetermined frequency band reflected by the tissue region, and one or more processors, the one or more processors configured to cause the image sensors to capture images of the tissue region and identify corresponding sets of pixels in the captured images, each set including pixels of a first image captured by the first image sensor and pixels of a second image captured by the second image sensor, the processor can determine distance or depth values ​​associated with each corresponding set of pixels based on pixel disparity for the corresponding sets of pixels and a baseline distance between the first image sensor and the second image sensor, and generate a 3D model of the tissue region based at least in part on the distance values.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 301,958, filed January 21, 2022, entitled "SYSTEMS AND METHODS FOR TOPOLOGICAL CHARACTERIZATION OF TISSUE," which is incorporated by reference in its entirety and for all purposes.

[0002] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT The inventions described in this disclosure were made in part with U.S. Government support awarded by the Biomedical Advanced Research and Development Authority (BARDA) in the Office of the Assistant Secretary for Preparedness and Response, U.S. Department of Health and Human Services under Contract No. HHSO100201300022C. The inventions described in this disclosure were also made in part with U.S. Government support awarded by the Defense Health Agency (DHA) under Contract No. W81XWH-17-C-0170 and / or Contract No. W81XWH-18-C-0114. The U.S. Government may own certain rights in this invention.

[0003] TECHNICAL FIELD The systems and methods disclosed herein relate to spectral imaging, and more particularly, to high-precision spectral imaging with multi-aperture imaging systems. [Background technology]

[0004] The electromagnetic spectrum is a band of wavelengths or frequencies of electromagnetic radiation (e.g., light). From longest to shortest wavelengths, the electromagnetic spectrum includes radio waves, microwaves, infrared light (IR), visible light (i.e., light detectable by structures in the human eye), ultraviolet light (UV), x-rays, and gamma rays. Spectral imaging also refers to a branch of spectroscopy and photography that collects partial or complete spectra of spectral information at various locations in an image plane. A multispectral imaging system can image multiple spectral bands (usually a dozen or less spectral bands, each in a different spectral region) and collect measurements in the spectral bands for each pixel, and can refer to a bandwidth of tens of nm per spectral channel. On the other hand, a hyperspectral imaging system can measure many more spectral bands, e.g., 200 or more spectral bands, and some types of hyperspectral imaging systems can continuously sample narrow bands (e.g., spectral bandwidth of a few nm or less) along a portion of the electromagnetic spectrum. Summary of the Invention [Means for solving the problem]

[0005] The multispectral imaging systems and techniques disclosed herein have several features, none of which can achieve the desired characteristics without the aid of others. Certain features of the spectral imaging disclosed herein are briefly described below, but the following description is not intended to limit the scope of the invention as set forth in the claims that follow. Those skilled in the art will appreciate how the features of the spectral imaging disclosed herein provide advantages over conventional systems and methods.

[0006] In a first aspect, an imaging system includes a plurality of image sensors configured to receive light of at least a first predetermined frequency band reflected by a tissue region; a memory having computer-executable instructions stored thereon; and one or more processors, the one or more processors being configured with instructions to at least capture images of the tissue region with each of at least two image sensors; identify corresponding sets of pixels in the captured images, each set including pixels of a first image captured by a first image sensor of the plurality of image sensors and pixels of a second image captured by a second image sensor of the plurality of image sensors; determine pixel disparities, preferably pixel disparities or individual pixel disparities, for pixels of the corresponding sets of pixels; determine distance values ​​associated with each corresponding set of pixels based at least in part on the pixel disparities and a baseline distance between the first image sensor and the second image sensor; and generate a three-dimensional (3D) model of the tissue region based at least in part on the distance values.

[0007] In some embodiments, the 3D model of the tissue region comprises at least one of a 3D point cloud, a 3D textured mesh, a 3D parametric surface, or a 3D voxel grid.

[0008] In some embodiments, the one or more processors are configured, in accordance with the instructions, to further determine one or more measurements of at least a portion of the tissue region based at least in part on the 3D model and a pre-calibration of the imaging system. In some embodiments, the one or more measurements include at least one of a length measurement, an area measurement, or a volume measurement of the portion of the tissue region. In some embodiments, the one or more measurements include a cross-sectional profile of the portion of the tissue region. In some embodiments, the pre-calibration is based on imaging a standardized calibration target with the imaging system. In some embodiments, the one or more measurements are not determined based on a marker included within a field of view of the imaging system. In some embodiments, the one or more processors are configured, in accordance with the instructions, to further perform one or more diagnostic analyses based at least in part on the one or more measurements. In some embodiments, the one or more processors are further configured, in accordance with the instructions, to further store medical billing codes identified based at least in part on the one or more measurements.

[0009] In some embodiments, the one or more processors are further configured to segment the captured image prior to identifying the set of corresponding pixels in accordance with the instructions. In some embodiments, the segmentation includes identifying at least background pixels and tissue region pixels. In some embodiments, the background pixels are excluded from identifying the set of corresponding pixels.

[0010] In some embodiments, the imaging system further includes at least one light source configured to illuminate the tissue region with light in at least a first predetermined frequency band.

[0011] In some embodiments, each image sensor of the plurality of image sensors is configured to image the tissue region in at least a first predetermined frequency band and a second frequency band different from a second frequency band of at least one other image sensor of the plurality of image sensors. In some embodiments, the one or more processors are further configured, according to the instructions, to obtain multispectral image data related to the tissue region based on the captured images. In some embodiments, the one or more processors are further configured, according to the instructions, to align the multispectral image data based on the image data corresponding to the first predetermined frequency band.

[0012] In some embodiments, the tissue region comprises at least a portion of a wound, an uneven tissue surface, pigmentation, a lesion, and the lesion comprises a cancer lesion, including, but not limited to, squamous cell carcinoma, basal cell carcinoma, Merkel cell carcinoma, malignant melanoma, and actinic keratosis.

[0013] In some embodiments, the one or more processors are configured, in accordance with the instructions, to further detect at least a portion of the wound edge, uneven tissue surface, pigmentation, or lesion based on the 3D model. In some embodiments, the one or more processors are configured, in accordance with the instructions, to further calculate one or more gradients of the wound edge, uneven tissue surface, pigmentation, or lesion relative to the wound surface, uneven tissue surface, pigmentation, or lesion. In some embodiments, the one or more processors are configured, in accordance with the instructions, to further determine a condition of the wound, uneven tissue surface, pigmentation, or lesion based on a comparison of the one or more gradients to a predefined gradient threshold. In some embodiments, the one or more processors are configured, in accordance with the instructions, to further evaluate a change in the condition of the wound, uneven tissue surface, pigmentation, or lesion based on multiple 3D models generated by the imaging system at different times. In some embodiments, the one or more processors are configured, according to the instructions, to further determine characteristics based at least in part on the 3D model and the detected wound edge, uneven tissue surface, pigmentation, or lesion, including at least one of an area, rim height, volume, and skin configuration of the wound, uneven tissue surface, pigmentation, or lesion. In some embodiments, the one or more processors are configured, according to the instructions, to further evaluate changes in the characteristics based on multiple 3D models generated by the imaging system at different times. In some embodiments, the wound includes at least one of a burn wound, a surgical wound, a pressure ulcer, and a diabetic foot ulcer.

[0014] In some embodiments, the one or more processors are configured in accordance with the instructions to further perform an image modification or transformation prior to identifying the set of corresponding pixels, hi some embodiments, the image modification or transformation includes correcting at least one of a size, a distortion, a skew, a perspective, or a rotation of the captured image.

[0015] In some embodiments, the one or more processors are configured in accordance with the instructions to output a visual representation of the tissue region further based on a 3D model of the tissue region. In some embodiments, the visual representation includes two-dimensional image data from at least one of the captured images fitted to the 3D model. In some embodiments, the visual representation includes a heat map. In some embodiments, the visual representation includes a two-dimensional cross-sectional profile of at least a portion of the tissue region.

[0016] In some embodiments, the plurality of image sensors includes at least three image sensors, each set further including pixels of a third image captured by a third image sensor of the plurality of image sensors. In some embodiments, the one or more processors are further configured, in accordance with the computer executable instructions, to determine a confidence value corresponding to the distance value associated with each corresponding set of pixels. In some embodiments, for each corresponding set of pixels, the distance value is determined based on pixels of the first image and the second image, and the confidence value is determined based at least in part on pixels of the third image. In some embodiments, the confidence value includes an uncertainty value expressed in units of a dimension, such as length. In some embodiments, the one or more processors are further configured, in accordance with the computer executable instructions, to generate a confidence map of the tissue region based on the 3D model and the determined confidence value. In some embodiments, the one or more processors are further configured, in accordance with the computer executable instructions, to exclude pixels from the 3D model having a confidence value that meets a predetermined threshold. In some embodiments, identifying the corresponding sets of pixels in the captured images includes evaluating a cost function that identifies matching areas or pixels of the first image and the second image. In some embodiments, identifying matching areas or pixels includes minimizing the cost function.

[0017] In a second aspect, any of the imaging system embodiments described above are used in wound debridement, optionally before, during or after wound debridement, such as smoothing the contours or edges of a wound.

[0018] In a third aspect, a method of improving wound healing includes generating a 3D model of the wound using an imaging system of any one of the preceding imaging system embodiments, and performing wound debridement by reference to the 3D model generated by the imaging system, wherein performing wound debridement includes reference to the 3D model of the wound before, during, or after debridement, such as to smooth out a contour or edge of the wound. [Brief description of the drawings]

[0019] [Figure 1A] 1 shows an example of light entering a filter at various chief ray angles of incidence.

[0020] [Figure 1B] 1B is a graph showing an example of the transmission efficiency obtained from the filter of FIG. 1A through which light is transmitted at various chief ray angles of incidence.

[0021] [Figure 2A] 1 shows an example of a multispectral image data cube.

[0022] [Figure 2B] An example is shown of how a particular multispectral imaging technique generates the data cube of FIG. 2A.

[0023] [Figure 2C] 2B shows an example of a snapshot-type imaging system capable of generating the data cube of FIG. 2A.

[0024] [Figure 3A]FIG. 1 shows a cross-sectional schematic diagram illustrating the optical design of an example of a multi-aperture imaging system with curved multi-bandpass filters according to the present disclosure.

[0025] [Figure 3B-3D] 3B shows an example of an optical design of optical components that constitute one optical path of the multi-aperture imaging system shown in FIG. 3A.

[0026] [Figure 4A-4E] 3A and 3B illustrate an embodiment of a multispectral, multiaperture imaging system having the optical design described in connection with FIG.

[0027] [Diagram 5] FIG. 4 illustrates another embodiment of a multispectral, multiaperture imaging system with the optical design described in connection with FIGS. 3A and 3B.

[0028] [Figures 6A-6C] FIG. 4 illustrates another embodiment of a multispectral, multiaperture imaging system with the optical design described in connection with FIGS. 3A and 3B.

[0029] [Figure 7A-7B] FIG. 4 illustrates another embodiment of a multispectral, multiaperture imaging system with the optical design described in connection with FIGS. 3A and 3B.

[0030] [Figure 8A-8B] FIG. 4 illustrates another embodiment of a multispectral, multiaperture imaging system with the optical design described in connection with FIGS. 3A and 3B.

[0031] [Figure 9A-9C] FIG. 4 illustrates another embodiment of a multispectral, multiaperture imaging system with the optical design described in connection with FIGS. 3A and 3B.

[0032] [Figure 10A-10B] Another embodiment of a multi-spectral, multi-aperture imaging system with the optical design described in connection with FIGS. 3A and 3B is shown.

[0033] [Figure 11A-11B] An example of a set of frequency bands that can pass through the filters of the multi-spectral, multi-aperture imaging system shown in FIGS. 3A - 10B is shown.

[0034] [Figure 12] A schematic block diagram of an imaging system that can be used in the multi-spectral, multi-aperture imaging system shown in FIGS. 3A - 10B is shown.

[0035] [Figure 13] A flowchart showing an example of a process of capturing image data using the multi-spectral, multi-aperture imaging system shown in FIGS. 3A - 10B is shown.

[0036] [Figure 14] A schematic block diagram of a workflow for processing image data, such as image data captured using the multi-spectral, multi-aperture imaging system shown in FIGS. 3A - 10B and / or using the process shown in FIG. 13, is shown.

[0037] [Figure 15] Parallax and parallax correction for processing image data, such as image data captured using the multi-spectral, multi-aperture imaging system shown in FIGS. 3A - 10B and / or using the process shown in FIG. 13, are illustrated.

[0038] [Figure 16]FIG. 13 illustrates a workflow for pixel-by-pixel classification of multispectral image data, such as image data captured using the multispectral, multi-aperture imaging system shown in FIGS. 3A-10B and / or using the process shown in FIG. 13 and then processed according to FIGS. 14 and 15.

[0039] [Figure 17] A simplified block diagram of an example of a distributed computing system including the multispectral, multi-aperture imaging system shown in Figures 3A to 10B is shown.

[0040] [Figures 18A-18C] 1 illustrates an embodiment of a portable multispectral, multiaperture imaging system.

[0041] [Figure 19A-19B] 1 illustrates an embodiment of a portable multispectral, multiaperture imaging system.

[0042] [Figure 20A-20B] An example of a multispectral, multi-aperture imaging system for a compact USB 3.0 camera housed in a standard camera housing is shown.

[0043] [Figure 21] An example of a multispectral, multi-aperture imaging system that includes an additional light source to improve image alignment is shown.

[0044] [Fig. 22A-22B] An example of the relationship between a burn image captured by a camera and a generated 3D model is shown.

[0045] [Figures 23A-23C] An integrated 3C model of the prosthetic leg is shown.

[0046] [Figure 24]1 illustrates an example of the relationship between pixel disparity and depth or distance information in a multi-camera imaging system.

[0047] [Diagram 25] 1 shows an example of the relationship between multiple camera views and an estimated 3D tissue scene.

[0048] [Figures 26A-26D] 1 illustrates an example process for generating a 3D tissue model based on multiple spatially offset 2D images.

[0049] [Figure 27A] 1 shows an example of a calibration target for keypoint extraction in a 3D imaging system.

[0050] [Figure 27B] 27B shows a set of feature correspondence graphs illustrating calibrated estimates of 3D distances between image acquisition devices around the calibration target of FIG. 27A.

[0051] [Figure 27C] 27B shows an example set of camera positions for calibration using the calibration target of FIG. 27A.

[0052] [Fig. 28A-28B] 3 illustrates the segmentation of tissue pixels from background pixels in an image.

[0053] [Figures 29A-29D] 1 illustrates the assessment of wound edge geometry using tissue topological feature assessment of the present technique.

[0054] [Diagram 30] Wound tracking against diagnostic and therapeutic timeline including a. treatment duration, b. wound status, c. feedback components, and d. wound progression model.

[0055] [Diagram 31] An example of a hidden Markov model (HMM) created for a wound progression model is shown below.

[0056] [Fig. 32A-32E] 1 illustrates an example of an application of the tissue topological feature assessment technique of the present technology.

[0057] [Diagram 33] 1 shows an example of a point cloud characterizing a wound, generated by the techniques of the present technology. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0058] In general, the present disclosure relates to 3D imaging and / or topological characterization of tissue using two or more image sensors. In some embodiments, the 3D imaging and / or topological characterization described herein can be performed by a spectral imaging system configured to obtain image data in multiple different frequency bands. A given system disclosed herein can be pre-calibrated using one or more objects having known geometric dimensions, and the calibration can be used to obtain specific dimensional measurements (e.g., length) of various aspects of tissue, such as width and / or depth of wounds, ulcers, or any other type of tissue feature. Additional aspects of the present technology are described in detail below.

[0059] In some embodiments, the present disclosure relates to spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned above each aperture. The present disclosure also relates to techniques for implementing spectral unmixing and image registration to generate a spectral data cube using image information received from such imaging systems. The techniques of the present disclosure address various challenges typically found in spectral imaging in obtaining image data that show accurate information about the wavelength bands reflected from the object being imaged, as described below. In some embodiments, the systems and methods described herein can obtain images of a large area of ​​tissue (e.g., 5.9 x 7.9 inches) in a short time (e.g., within 6 seconds) and can achieve this without the need for injection of imaging contrast agents. In some embodiments, for example, the multispectral imaging systems described herein are configured to acquire images from a large area of ​​tissue, e.g., 5.9×7.9 inches, in 6 seconds or less, and provide tissue analysis information, such as identification of multiple burn conditions, multiple wound conditions, or healing potential; or clinical characteristics including cancerous or non-cancerous status of the imaged tissue, wound depth, debridement margin, and presence of diabetic, non-diabetic, or chronic ulcers, in the absence of imaging contrast agents. Similarly, in some of the methods described herein, the multispectral imaging systems of the present invention can acquire images from a large area of ​​tissue, e.g., 5.9×7.9 inches, in 6 seconds or less, and provide tissue analysis information, such as identification of multiple burn conditions, multiple wound conditions, or healing potential; or clinical characteristics including cancerous or non-cancerous status of the imaged tissue, wound depth, debridement margin, and presence of diabetic, non-diabetic, or chronic ulcers, in the absence of imaging contrast agents.

[0060] One of the challenges with existing solutions is that the colors of the captured image can be distorted, compromising the quality of the image data. This can be especially problematic in applications that rely on the use of optical filters to accurately detect and analyze specific wavelengths of light. More specifically, color shading is a change in the wavelength of light as it passes through the image sensor area, as the angle of incidence of light on the color filter increases, the shorter the wavelength of light that passes through the color filter. Typically, this effect is observed in interference filters, which are fabricated by stacking multiple thin layers of different refractive indexes on a transparent substrate. Thus, longer wavelengths (such as red light) have a larger angle of incidence of light rays and are therefore more likely to be blocked at the edge of the image sensor, resulting in incident light of the same wavelength being detected as a spatially non-uniform color as it passes through the image sensor. Without image correction, color shading appears as a change in color near the edge of the captured image.

[0061] The disclosed technology is advantageous over other multispectral imaging systems on the market because it is not limited in lens and / or image sensor configuration and their field of view or aperture size. As one skilled in the art would appreciate, changes to the lenses, image sensors, aperture sizes, or other components of the imaging systems disclosed herein may include other adjustments to the imaging system. The disclosed technology is also an improvement over other multispectral imaging systems in that components that perform wavelength splitting or utilize the entire imaging system (e.g., optical filters, etc.) can be separated from components that convert light energy to digital output (e.g., image sensors, etc.). This can reduce cost, simplify, and / or reduce development time to reconfigure the imaging system for different multispectral wavelengths. The disclosed technology is also believed to be more robust than other multispectral imaging systems in that it can achieve imaging characteristics comparable to other multispectral imaging systems on the market in a smaller and lighter form factor. The disclosed techniques are also advantageous over other multispectral imaging systems in that they can acquire multispectral images at snapshot, video or high-speed video rates. The disclosed techniques can also multiplex several spectral bands into each aperture, reducing the number of apertures required to acquire a particular number of spectral bands in an imaging data set, and can provide a more robust implementation of multispectral imaging systems based on multi-aperture technology due to cost savings from the reduced number of apertures and improved light collection (e.g., commercially available sensor arrays can use larger apertures that fit into fixed sizes and dimensions). Moreover, the disclosed techniques can provide all of these advantages without compromising on resolution or image quality.

[0062] FIG. 1A illustrates an example of a filter 108 positioned along a light path toward an image sensor 110, and light incident on the filter 108 at various ray incidence angles. Rays 102A, 104A, and 106A are shown as lines passing through the filter 108 and then refracted by a lens 112 onto the sensor 110. The lens 112 may be replaced with other image forming optics, such as, but not limited to, a mirror and / or an aperture. In FIG. 1A, the light in each ray is considered broadband, e.g., light having a spectral composition in a wide wavelength range, and only light of a particular wavelength is selectively transmitted by the filter 108. The three rays 102A, 104A, and 106A are incident on the filter 108 at different angles of incidence. For purposes of illustration, light ray 102A is shown as being substantially normal incidence on filter 108, light ray 104A has a larger angle of incidence than light ray 102A, and light ray 106A has a larger angle of incidence than light ray 104A. Each light ray 102B, 104B, 106B that passes through the filter exhibits a unique spectrum due to the angle-of-incidence-dependent transmission characteristics of filter 108, which are detected by sensor 110. This angle-of-incidence-dependent effect causes a shift in the bandpass characteristics of filter 108, with shorter wavelengths as the angle of incidence increases. Furthermore, the angle-of-incidence dependence may reduce the transmission efficiency of filter 108 and may change the spectral shape of the bandpass characteristics of filter 108. This combined effect is referred to as angle-of-incidence-dependent spectral transmission. FIG. 1B shows the spectrum of each of the rays of FIG. 1A detected by a spectrometer assumed to be at the sensor 110, illustrating the shift in the spectrum of the bandpass characteristic of the filter 108 as the angle of incidence increases. Curves 102C, 104C, and 106C show the center wavelength of the bandpass characteristic becoming shorter, and therefore the wavelength of the light emitted from the optical system in this example becoming shorter. As shown in this figure, the spectral shape of the bandpass characteristic and the transmittance peak also change with the angle of incidence. In certain consumer applications, image processing can be performed to remove the visible effects of such angle-dependent spectral transmission.However, such post-processing techniques cannot recover the exact wavelengths of light that actually entered filter 108. Thus, the resulting image data may not be usable for certain high precision applications.

[0063] Another challenge faced by certain existing spectral imaging systems is the time required to capture a complete spectral image data set, which is discussed in relation to Figures 2A and 2B. A spectral image sensor acquires the spectral irradiance I(x,y,λ) of a particular scene and collects a three-dimensional (3D) data set, commonly referred to as a data cube. Figure 2A shows an example of a spectral image data cube 120. As shown in this figure, the data cube 120 represents three-dimensional image data, where two spatial dimensions (x and y) correspond to the two-dimensional (2D) surface of the image sensor, and the spectral dimension (λ) corresponds to a particular wavelength range. The size of the data cube 120 is N x N y N λ and N x and N y is the number of sample points in each spatial dimension (x, y), and N λ is the number of sample points along the spectral axis λ. Because the data cube has a higher dimensionality than currently available 2D detector arrays (e.g., image sensors), a typical spectral imaging system simultaneously measures all sample points of the data cube 120 by taking time-sequential 2D slices (i.e., planes) of the data cube 120 (referred to herein as a "scanning" imaging system) or by dividing the data cube 120 during computation to obtain multiple two-dimensional data that can be reintegrated into the data cube 120 (referred to herein as a "snapshot" imaging system).

[0064] FIG. 2 shows an example of how a particular scanning spectral imaging technique generates a data cube 120. Specifically, FIG. 2B shows portions 132, 134, and 136 of the data cube 120 that can be collected in one detector integration time. A point-scanning spectrometer can, for example, image portion 132 that extends across the spectral plane λ at one spatial location (x,y). A point-scanning spectrometer can build the data cube 120 by performing multiple integrations for each (x,y) location in the spatial dimensions. A filter wheel imaging system can, for example, image portion 134 that extends across the spatial dimensions x and y but is located in only a single spectral plane λ. A wavelength-scanning imaging system, such as a filter wheel imaging system, can build the data cube 120 by performing multiple integrations over the spectral plane λ. A line-scanning spectrometer can, for example, image portion 136 that extends across the entire spectral dimension λ and across one of the spatial dimensions (x or y) but is located at only one point in the other spatial dimension (y or x). A line-scan spectrometer can construct a data cube 120 by performing multiple integrations over the position of one of the spatial dimensions (y or x).

[0065] In applications where both the object and the imaging system are stationary (or relatively stationary during the exposure time), the scanning imaging systems described above provide the advantage of providing a high resolution data cube 120. Line-scan and wavelength-scanning imaging systems may provide this advantage by using the entire area of ​​the image sensor to capture each spectral or spatial image. However, motion of the imaging system and / or object between first and second exposures may result in artifacts in the resulting image data. For example, the same (x,y) location in the data cube 120 may actually represent a different physical location on the imaged object in the spectral dimension λ. Such artifacts may result in errors in subsequent analysis and / or the need for alignment (e.g., alignment of the spectral dimension λ so that a particular (x,y) location corresponds to the same physical location on the object).

[0066] In contrast, a snapshot imaging system 140 can capture the entire datacube 120 in one integration time or exposure, thus avoiding the effects of motion on image quality. FIG. 2C shows an example of an image sensor 142 and an optical filter array 144 (such as a color filter array (CFA)) that can be used to create a snapshot imaging system. In this example, the color filter array (CFA) 144 is a repeating pattern of color filter units 146 arranged on the surface of the image sensor 142. This method of obtaining spectral information can also be called a multispectral filter array (MSFA) or a spectrally resolved detector array (SRDA). In the example shown in this figure, the color filter unit 146 includes various color filters arranged in a 5×5 array, thereby generating 25 spectral channels in the resulting image data. The CFA can use various color filters to split the incoming light into bands for each color filter and direct the split light to the photoreceptors of each color on the image sensor. In this way, for a given color 148, only one of the 25 photoreceptors actually detects a signal indicative of light of that wavelength. Thus, with such a snapshot imaging system 140, 25 color channels may be generated in one exposure, but the amount of measurement data for each color channel is less than the total output of the image sensor 142. In some embodiments, the CFA may include a filter array (MSFA) and / or a spectrally resolved detector array (SRDA), and / or may include conventional Bayer filters, CMYK filters, or other absorptive or interferometric filters. One type of interferometric filter is a thin film filter array arranged in a grid, with each grid element corresponding to one or more sensor elements. Another type of interferometric filter is the Fabry-Perot filter.Nano-etched Fabry-Perot interference filters typically exhibit bandpass characteristics with a full width at half maximum (FWHM) of about 20-50 nm, but are advantageous in some embodiments because they exhibit a small roll-off at the transition from the center wavelength of the filter passband to the stopband. In addition, such filters exhibit a low optical density (OD) in the stopband, which can improve sensitivity to light outside the passband. This combined effect allows these particular filters to be sensitive to spectral regions that would be blocked by the high roll-off of a similar FWHM high optical density interference filter constructed from multiple thin film layers deposited by coating deposition techniques such as evaporation or ion beam sputtering. In embodiments using dye-based CMYK filters or RGB-based (Bayer) filters, it is preferable to have a small spectral roll-off and a large FWHM in the passband of each individual filter, which allows for unique spectral transmission for each wavelength across the entire spectrum being observed.

[0067] Therefore, the data cube 120 obtained by the snapshot imaging system has one of the following two characteristics that can be problematic for high-precision imaging applications. First, the data cube 120 obtained by the snapshot imaging system has a size N larger than the (x, y) size of the detector array. x and N y The second problem is that the data cube 120 obtained by the snapshot imaging system has a lower resolution than the data cube 120 obtained by the scanning imaging system with the same image sensor because the size of the (x, y) position can be small. x and N y The dimensions of the (x,y) may be the same as the (x,y) dimensions of the detector array. However, if interpolation was used in generating the data cube, this means that any particular value in the data cube is not an actual measurement of the wavelength of light incident on the sensor, but an estimate of that measurement based on surrounding values.

[0068] Another existing component used in multispectral imaging with a single exposure is the multispectral imaging beamsplitter. In such imaging systems, a beamsplitter cube splits the incoming light into different color bands, each of which is observed by separate image sensors. Although the design of the beamsplitter can be modified to adjust the spectral bands measured, it is not easy to split the incoming light into more than four beams without compromising the performance of the imaging system. Thus, four spectral channels appears to be the practical limit of this approach. A closely related method splits the light using thin film filters instead of bulky beamsplitter cubes / prisms, but the cumulative transmission attenuation and spatial limitations of multiple successive filters limit this approach to about six spectral channels.

[0069] In some embodiments, the aforementioned challenges, among others, can be addressed by the multi-aperture spectral imaging system and associated image data processing techniques disclosed herein, which includes curved, multi-bandpass filters that selectively transmit light incident through each aperture. This particular configuration can achieve all of the design goals of fast imaging speed, high resolution images, and high fidelity of detection wavelength. Thus, the optical design and associated image data processing techniques disclosed herein can be used in portable spectral imaging systems and / or for imaging moving subjects and can obtain data cubes suitable for high precision applications (e.g., clinical tissue analysis, biometric authentication, and episodic clinical signs). Such high precision applications can include diagnosing the stage (0-3) of pre-metastatic malignant melanoma, classifying the severity of burns in skin tissue, or tissue diagnosis of the severity of diabetic foot ulcers. Thus, the snapshot spectral acquisition and small form factor described in some embodiments allows the present invention to be used in clinical settings dealing with transient events, including diagnosis of various types of retinopathies (e.g., non-proliferative diabetic retinopathy, proliferative diabetic retinopathy, and age-related macular degeneration), as well as imaging of pediatric patients with high motion. Thus, one skilled in the art will appreciate that the use of the multi-aperture system with flat or curved multi-bandpass filters disclosed herein represents a significant technological advance over conventional spectral imaging implementations. More specifically, the multi-aperture system of the present invention may enable the collection of 3D spatial images of or relating to the curvature, depth, volume, and / or area of ​​an object based on the calculated perspective difference between each aperture. However, the multi-aperture method disclosed herein is not limited to any particular filter and may include flat and / or low-profile filters based on interference or absorption filtering.The invention disclosed herein can be modified to include flat filters in the image space of the imaging system when the angle of incidence to the appropriate lens or aperture is small or within the acceptable range. Filters may be placed at the entrance and / or exit pupil of the imaging lens or aperture stop, and such configurations would be readily employable by one skilled in the art of optical engineering.

[0070] Various aspects of the disclosure are described below with reference to specific examples and embodiments. These examples and embodiments are for illustrative purposes and are not intended to limit the disclosure. While the examples and embodiments described herein focus on specific calculations and algorithms for illustrative purposes, those skilled in the art will appreciate that these examples are for illustrative purposes only and are not intended to limit the invention. For example, while some examples are provided with respect to multispectral imaging, the multi-aperture imaging systems and associated filters disclosed herein can be configured to perform hyperspectral imaging in other implementations. Furthermore, while certain examples are presented as achieving advantages in portable applications and / or applications with moving target objects, it will be appreciated that the imaging system designs and associated processing techniques disclosed herein can provide highly accurate data cubes suitable for fixed imaging systems and / or analysis of relatively stationary targets.

[0071] Overview of Electromagnetic Spectral Range and Image Sensors Reference is made herein to particular colors or portions of the electromagnetic spectrum, and these wavelengths are hereinafter described as defined by ISO 21348, "Definitions of Irradiance Spectral Types." As will be described in more detail below, in certain imaging applications, wavelength ranges of particular colors may be passed collectively through particular filters.

[0072] Electromagnetic radiation in the wavelength range of 760 nm to 380 nm or about 760 nm to 380 nm is usually considered to be the "visible" spectrum, i.e., the portion of the spectrum that is perceptible by the color receptors of the human eye. In the visible spectrum, red light is usually considered to have a wavelength of 700 nm or about 700 nm, or to be in the range of 760 nm to 610 nm or about 760 nm to 610 nm. Orange light is usually considered to have a wavelength of 600 nm or about 600 nm, or to be in the range of 610 nm to 591 nm or about 610 nm to 591 nm. Yellow light is usually considered to have a wavelength of 580 nm or about 580 nm, or to be in the range of 591 nm to 570 nm or about 591 nm to 570 nm. Green light is usually considered to have a wavelength of 550 nm or about 550 nm, or in the range of 570 nm to 500 nm or about 570 nm to about 500 nm. Blue light is usually considered to have a wavelength of 475 nm or about 475 nm, or in the range of 500 nm to 450 nm or about 500 nm to about 450 nm. Purple (magenta) light is usually considered to have a wavelength of 400 nm or about 400 nm, or in the range of 450 nm to 360 nm or about 450 nm to about 360 nm.

[0073] Looking beyond the visible spectrum, infrared (IR) refers to electromagnetic radiation that has longer wavelengths than visible light and is generally not visible to the human eye. IR has wavelengths that extend from about 760 nm or about 1 mm to the nominal lower limit of the red visible spectrum, which is at 760 nm. Within this range, near infrared (NIR) refers to the portion of the spectrum adjacent to the red range, and is the wavelength range of about 760 nm to about 1400 nm or 760 nm to 1400 nm.

[0074] Ultraviolet (UV) light refers to a portion of electromagnetic radiation with wavelengths shorter than visible light and is generally invisible to the human eye. UV has wavelengths ranging from about 40 nm or the nominal lower limit of the visible spectrum at violet at 40 nm to about 400 nm. Within this range, near ultraviolet (NUV) refers to a portion of the spectrum adjacent to the violet range and has a wavelength range of about 400 nm to about 300 nm or 400 nm to 300 nm, mid ultraviolet (MUV) has a wavelength range of about 300 nm to about 200 nm or 300 nm to 200 nm, and far ultraviolet (FUV) has a wavelength range of about 200 nm to about 122 nm or 200 nm to 122 nm.

[0075] The image sensors described herein can be configured to detect electromagnetic radiation in any of the above ranges, depending on the particular wavelength range that is appropriate for a particular application. Typical silicon charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) sensors have spectral sensitivity that spans the entire visible spectrum, but also most of the near infrared (IR) spectrum and parts of the UV spectrum. In some implementations, back-illuminated or front-illuminated CCD or CMOS arrays can additionally or alternatively be used. For applications requiring high signal-to-noise ratio and scientific-grade measurements, some implementations can additionally or alternatively use scientific complementary metal-oxide semiconductor (sCMOS) cameras or electronically multiplied CCD cameras (EMCCD). In other implementations, sensors known to operate in a particular color range (e.g., short-wave infrared (SWIR), mid-wave infrared (MWIR), or long-wave infrared (LWIR)) and corresponding optical filter arrays can additionally or alternatively be used, depending on the intended application. These sensors and optical filter arrays may additionally or alternatively comprise cameras based on detector materials such as indium gallium arsenide (InGaAs) or indium antimony (InSb), or cameras based on microbolometer arrays.

[0076] The image sensor used in the multispectral imaging techniques disclosed herein may be used in conjunction with an optical filter array, such as a color filter array (CFA). Some types of CFAs can split the incident light into red (R), green (G) and blue (B) visible regions and direct the split visible light to dedicated red, green or blue photodiode photoreceptors on the image sensor. A common example of a CFA is the Bayer pattern, which is a color filter array that arranges RGB color filters in a specific pattern on a rectangular grid of photodetectors. The Bayer pattern is composed of 50% green, 25% red and 25% blue, with rows of repeated red and green color filters alternating with rows of repeated blue and green color filters. Some types of CFAs (e.g., CFAs for RGB-NIR sensors) can also split NIR light and direct the split NIR light to dedicated photodiode photoreceptors on the image sensor.

[0077] Thus, the wavelength range of the filter components of the CFA may determine the wavelength range that each image channel exhibits in the captured image. Thus, in various embodiments, the red channel of the image may correspond to the red wavelength range of the color filter, may include some yellow and orange light, and may range from about 570 nm to about 760 nm or from 570 nm to 760 nm. In various embodiments, the green channel of the image may correspond to the green wavelength range of the color filter, may include some yellow light, and may range from about 570 nm to about 480 nm or from 570 nm to 480 nm. In various embodiments, the blue channel of the image may correspond to the blue wavelength range of the color filter, may include some violet light, and may range from about 490 nm to about 400 nm or from 490 nm to 400 nm. Those skilled in the art will appreciate that the exact start and end wavelengths (or portions of the electromagnetic spectrum) that define the colors (e.g., red, green, and blue) of the CFA may vary depending on the implementation of the CFA.

[0078] Additionally, typical visible CFAs transmit light outside the visible spectrum. Thus, many image sensors limit their sensitivity to infrared by placing a thin-film infrared-reflecting filter in front of the sensor that blocks infrared wavelengths but passes visible light. However, in some of the imaging systems disclosed herein, the thin-film infrared-reflecting filter may be omitted to allow infrared light to pass. Thus, the red, green and / or blue channels may be used to collect infrared wavelength bands. In some implementations, the blue channel may be used to collect a specific NUV wavelength band. Because the red, green and blue channels have different spectral responses associated with each channel having a unique transmission efficiency at each wavelength in the stacked spectral image, a known transmission profile may be used to provide a unique weighting response for each spectral band prior to unmixing. For example, the weighting response may include the known transmission response of the red, blue and green channels in the infrared and ultraviolet wavelength regions, allowing each channel to be used to collect bands from these wavelength regions.

[0079] As will be described in more detail below, additional color filters can be placed in front of the CFA along the light path to the image sensor to selectively extract specific bands of light incident on the image sensor. The color filters disclosed herein are combinations of (thin film) dichroic and / or absorptive filters, or dichroic and / or absorptive filters alone. Some of the color filters disclosed herein may be bandpass filters that pass frequencies within a certain region (in the passband) but block (attenuate) frequencies outside that region (in the blocking region). Some of the color filters disclosed herein may be multi-bandpass filters that pass multiple, discontinuous wavelength regions. These "frequency bands" may have narrower passbands, greater attenuation in the blocking region, and steeper spectral roll-off than the broad color range of the CFA filter. Roll-off is defined as the steepness of the spectral response at the transition from the passband to the blocking region of the filter. For example, the color filters disclosed herein can cover a passband of about 20 nm to about 40 nm or 20 nm to 40 nm. The particular configuration of the color filters can determine the wavelength bands that are actually incident on the sensor, which can improve the accuracy of the imaging techniques disclosed herein. The color filters described herein can be configured to selectively block or pass specific bands of electromagnetic radiation within the above-mentioned regions depending on the particular wavelength bands that are suitable for a particular application.

[0080] The term "pixel" is used herein to describe the output generated by an element of a two-dimensional detector array. In contrast, a photodiode, i.e., a single light-sensitive element, of a two-dimensional detector array behaves as a transducer that can convert photons into electrons via the photoelectric effect and then convert the electrons into a signal that can be used to determine a pixel value. An element of a data cube can be referred to as a "voxel" (e.g., an element of volume). A "spectral vector" refers to a vector that represents the spectral data (e.g., the spectrum of light received from a particular point in object space) at a particular (x,y) location of the data cube. A horizontal plane of a data cube (e.g., an image representing one spectral dimension) is referred to herein as an "image channel." In certain embodiments described herein, spectral video information may be captured, and the resulting data dimension can be N x N y N λ N t We can imagine the shape of a "hypercube" denoted by t is the number of frames captured during the video sequence).

[0081] Overview of an example of a multi-aperture imaging system with curved multi-bandpass filters FIG. 3A shows a schematic diagram of an example of a multi-aperture imaging system 200 with curved multi-bandpass filters according to the present disclosure. The schematic diagram shown includes a first image sensor area 225A (photodiodes PD1-PD3) and a second image sensor area 225B (photodiodes PD4-PD6). The photodiodes PD1-PD6 may be, for example, photodiodes formed on a semiconductor substrate (e.g., a CMOS image sensor). Typically, each photodiode PD1-PD6 may be a single unit of some material, semiconductor, sensor element, or other device capable of converting incident light into an electric current. This diagram shows only a small portion of the entire multi-aperture imaging system for purposes of explaining the structure and operation of the multi-aperture imaging system, and it will be appreciated that in an implementation, the image sensor area may include hundreds or thousands of photodiodes (and corresponding color filters). The first image sensor area 225A and the second image sensor area 225B may be implemented as separate sensors or as separate areas on the same image sensor, depending on the implementation. Although FIG. 3A shows two apertures and corresponding optical paths and sensor areas, it will be appreciated that the optical design principles illustrated in FIG. 3A can be extended to designs including three or more apertures and corresponding optical paths and sensor areas, depending on the implementation.

[0082] The multi-aperture imaging system 200 includes a first opening 210A that provides a first light path to a first sensor area 225A, and a second opening 210B that provides a first light path to a second sensor area 225B. The apertures may be adjustable to increase or decrease the brightness of the light reflected in the image, or the exposure time of a particular image may be changed by adjusting the apertures such that the brightness of the light incident on the image sensor area remains unchanged. The apertures may be located anywhere along the optical axis of the multi-aperture imaging system as would be reasonable for a person skilled in the art of optical design. The optical axis of the optical components located along the first optical path is shown by dashed line 230A, and the optical axis of the optical components located along the second optical path is shown by dashed line 230B, although it will be appreciated that the dashed lines do not represent the physical structure of the multi-aperture imaging system 200. Optical axis 230A and optical axis 230B are separated by a distance D, which can result in parallax between the images captured by first sensor area 225A and the images captured by second sensor area 225B. "Parallax" refers to the distance between two corresponding points on the left and right (or top and bottom) sides of a stereoscopic image, where the same physical point in object space appears at different locations in each image. Processing techniques that correct for and exploit this parallax are described in more detail below.

[0083] The optical axis 230A and the optical axis 230B pass through the center C of the corresponding aperture. Other optical components can also be arranged around these optical axes (e.g., the rotational symmetry points of the optical components can be arranged along this optical axis). For example, the first curved multi-bandpass filter 205A and the first imaging lens 215A can be arranged around the first optical axis 230A, and the second curved multi-bandpass filter 205B and the second imaging lens 215B can be arranged around the second optical axis 230B.

[0084] As used herein, the terms "on" and "above" when discussing the placement of optical elements refer to the location of a particular structure (e.g., a color filter or lens) through which light entering imaging system 200 from object space passes before reaching (or entering) another structure. To explain this, along a first optical path, curved multi-bandpass filter 205A is located above aperture 210A, which is located above imaging lens 215A, which is located above CFA 220A, which is located above first image sensor area 225A. Thus, light entering from object space (e.g., the physical space to be imaged) first passes through first curved multi-bandpass filter 205A, then through aperture 210A, then through imaging lens 215A, then through CFA 220A, and finally enters first image sensor area 225A. The second optical path (e.g., the optical path including the curved multi-bandpass filter 205B, the aperture 210B, the imaging lens 215B, the CFA 220B, and the second image sensor area 225B) may be similarly arranged. In another implementation, the aperture 210A, 210B and / or the imaging lens 215A, 215B may be located above the curved multi-bandpass filter 205A, 205B. Additionally, another implementation may not use a physical aperture and may rely on a clear aperture of the optics to control the brightness of the light imaged onto the sensor area 225A and the sensor area 225B. Thus, the lenses 215A, 215B may be located above the apertures 210A, 210B and the curved multi-bandpass filters 205A, 205B. In this implementation, the apertures 210A, 210B may be located above the lenses 215A, 215B, or the apertures 210A, 210B may be located below the lenses 215A, 215B, as may be determined by one of ordinary skill in the art of optical design.

[0085] The first CFA 220A, located above the first sensor area 225A, and the second CFA 220B, located above the second sensor area 225B, can act as filters that selectively pass certain wavelengths and can split the incoming visible light into red, green, and blue regions (denoted by the symbols R, G, and B, respectively). The light is "split" by passing only the selected wavelengths through the color filters of the first CFA 220A and the second CFA 220B. The split light is received by a dedicated red, green, or blue diode on the image sensor. Although red, blue, and green filters are commonly used, in other embodiments, different color filters can be used depending on the color channels required for the captured image data, such as filters that selectively pass ultraviolet, infrared, or near infrared light, as with an RGB-IR CFA.

[0086] As shown in FIG. 3A, the filters of the CFA are disposed above single photodiodes PD1-PD6. FIG. 3A also shows an example of a microlens (designated ML) that may be formed or otherwise disposed on each color filter to focus incident light onto the active detector area. Alternative implementations may have multiple photodiodes (e.g., a collection of two, four or more adjacent photodiodes) below a single filter. In the example shown, photodiodes PD1 and PD4 are located below a red filter and therefore output pixel information for a red channel, photodiodes PD2 and PD5 are located below a green filter and therefore output pixel information for a green channel, and photodiodes PD3 and PD6 are located below a blue filter and therefore output pixel information for a blue channel. Additionally, as described in more detail below, the particular color channel output by a given photodiode can be an even narrower frequency band based on the active light source and / or the particular frequency band passed through the multi-bandpass filters 205A, 205B, thereby allowing a given photodiode to output different image channel information with different exposures.

[0087] The imaging lenses 215A, 215B can be shaped to focus the image of the object scene on the sensor area 225A, 225B. Each imaging lens 215A, 215B is not limited to a single convex lens as shown in FIG. 3A, but can be composed of as many optical elements and surfaces as needed to form an image, and various types of imaging lenses or lens assemblies, either commercially available or custom designed, can be used. Each optical element or lens assembly can be formed or coupled to each other to be housed in series or stacked using an opto-mechanical barrel with a retaining ring or bezel. In some embodiments, the optical element or lens assembly can include one or more coupled lens groups, which can include two or more optical components bonded or otherwise coupled together. In various embodiments, the multi-bandpass filters described herein may be placed in front of a lens assembly of a multispectral imaging system, in front of a single lens of a multispectral imaging system, behind a lens assembly of a multispectral imaging system, behind a single lens of a multispectral imaging system, inside a lens assembly of a multispectral imaging system, inside a group of combined lenses of a multispectral imaging system, directly on the surface of a single lens of a multispectral imaging system, or directly on the surface of an element of a lens assembly of a multispectral imaging system. Additionally, apertures 210A and 210B may be eliminated and lenses 215A and 215B may be of the type typically used in digital single-lens reflex (DSLR) or mirrorless camera photography. Additionally, lenses 215A and 215B may be of the type used in machine vision using a C-mount or S-mount thread for attachment.Focus adjustment can be achieved, for example, by moving the imaging lenses 215A, 215B relative to the sensor areas 225A, 225B, or by moving the sensor areas 225A, 225B relative to the imaging lenses 215A, 215B, based on manual focus, contrast-based autofocus or other suitable autofocus techniques.

[0088] Each of the multi-bandpass filters 205A, 205B can be configured to selectively pass a narrow frequency band of light, for example, in some embodiments, a 10-50 nm frequency band (or in other embodiments, a broader or narrower frequency band of light). As shown in FIG. 3A, both of the multi-bandpass filters 205A, 205B can pass a frequency band λc (a "common frequency band"). In implementations with three or more optical paths, each of the multi-bandpass filters can pass this common frequency band. In this manner, each sensor area captures image information in the same frequency band (a "common channel"). As described in more detail below, this image information obtained in the common channel can be used to align a set of images captured by each sensor area. Some implementations may have one common frequency band and a corresponding common channel, or may have multiple common frequency bands and corresponding common channels.

[0089] Each of the multiple bandpass filters 205A, 205B can be configured to selectively pass one or more unique frequency bands in addition to the common frequency band λc. In this manner, the imaging system 200 can increase the number of different spectral channels collectively imaged by the sensor areas 205A, 205B beyond the number of spectral channels that can be imaged by a single sensor area. This aspect is advantageous in that the unique frequency bands λc can be selectively passed. u1 and a multi-bandpass filter 205A that passes a specific frequency band λ u2where λ u1 and λ u2 indicates different frequency bands. Although shown as passing two frequency bands in this figure, the multi-bandpass filters of the present disclosure can each pass more than one frequency band at a time. For example, as described below in connection with Figures 11A and 11B, in some implementations, each multi-bandpass filter can pass four frequency bands. In various embodiments, more frequency bands may be passed. For example, an implementation using four cameras may include a multi-bandpass filter configured to pass eight frequency bands. In some embodiments, the number of frequency bands may be, for example, four, five, six, seven, eight, nine, ten, twelve, fifteen, sixteen, or more.

[0090] The multiple bandpass filters 205A, 205B have curvatures selected to reduce the angle-of-incidence dependent spectral transmission at their respective sensor regions 225A, 225B. As a result, when receiving narrowband illumination from object space, each photodiode (e.g., a color filter covering the sensor region that passes that wavelength) located across the surface of the sensor region 225A, 225B that is sensitive to that wavelength will receive substantially the same wavelength of light without the wavelength shift seen in photodiodes located near the edge of the sensor as discussed above in connection with FIG. 1A. This configuration can produce more accurate spectral image data than would be possible using flat filters.

[0091] Figure 3B shows an example of an optical design for the optical components that make up one optical path of the multi-aperture imaging system shown in Figure 3A. More specifically, Figure 3B shows a custom achromatic doublet 240 that can be used as the multiple bandpass filters 205A, 205B. The custom achromatic doublet 240 passes light through a housing 250 to an image sensor 225. The housing 250 may include apertures 210A, 210B and imaging lenses 215A, 215B as previously described.

[0092] The achromatic doublet 240 is configured to correct optical aberrations due to the incorporation of the required surfaces for the multiple bandpass filter coatings 205A, 205B. The achromatic doublet 240 shown in this figure includes two lenses that can be made of glass or other optical materials with different dispersions and refractive indices. In other implementations, three or more lenses may be used. These achromatic doublet lens designs incorporate the multiple bandpass filter coatings 205A, 205B on a curved front surface 242, and incorporate the optical surfaces of a curved single lens onto which the filter coatings 205A, 205B are deposited to cancel optical aberrations, and the combined effect of the curved front surface 242 and curved back surface 244 of the achromatic doublet 240 limits the refractive or light gathering power, allowing these lenses alone, housed in the housing 250, to constitute the primary elements for light gathering. The achromatic doublet 240 can thus contribute to improving the accuracy of image data captured by the imaging system 200. These individual lenses can be mounted adjacent to one another, e.g., by bonding or cementing the individual lenses together, and shaped so that the aberrations of one lens are cancelled by the aberrations of the other lens. The curved front surface 242 or the curved back surface 244 of the achromatic doublet 240 can be coated with multiple bandpass filter coatings 205A, 205B. Alternative doublet designs may be implemented in the systems described herein.

[0093] Further variations of the optical designs described herein may be implemented. For example, in some embodiments, a single lens or other optical single lens, such as a convex lens (positive lens) or concave lens (negative lens) as shown in FIG. 3A, may be included in the optical path instead of the doublet 240 shown in FIG. 3B. FIG. 3C shows an example of an implementation in which a flat filter 252 is placed between the lens housing 250 and the sensor 225. The achromatic doublet 240 of FIG. 3C, which incorporates a flat filter 252 with multi-band transmission characteristics, corrects optical aberrations without significantly contributing to the optical power of each lens housed in the housing 250. FIG. 3D shows another example of an implementation in which a multi-bandpass coating is implemented by applying a multi-bandpass coating 254 to the front surface of the lens assembly housed in the housing 250. Thus, this multi-bandpass coating 254 may be applied to any curved surface of any optical element housed in the housing 250.

[0094] 4A-4E show an embodiment of a multispectral, multi-aperture imaging system 300 with the optical design described in connection with FIG. 3A and FIG. 3B. FIG. 4A shows a perspective view of the imaging system 300 with a housing 305 shown semi-transparently to reveal the internal components. The actual housing 305 may be larger or smaller than the housing 305 shown in this figure, depending, for example, on the size of the embedded computing resource desired. FIG. 4B shows a front view of the imaging system 300. FIG. 4C shows a cutaway side view of the imaging system 300 taken along line CC of FIG. 4B. FIG. 4D shows a bottom view of the imaging system 300, showing the processing board 335. FIGS. 4A-4D are now described.

[0095] The housing 305 of the imaging system 300 may be contained within another housing. For example, in a portable implementation, the imaging system 300 may be contained within a housing that may have one or more handles shaped to allow the imaging system 300 to be held stably. An example of a portable implementation is shown in more detail in Figures 18A-18CC and 19A-19B. The top surface of the housing 305 includes four openings 320A-320D. A different multi-bandpass filter 325A-325D is disposed above each opening 320A-320D and is held in place by a filter cap 330A-330B. The multi-bandpass filters 325A-325D may be curved, and as described herein, each multi-bandpass filter passes a common frequency band and at least one unique frequency band, thereby enabling high-precision multispectral imaging in more spectral channels than would normally be captured by an image sensor covered with a color filter array. The image sensors, imaging lenses, and color filters described above are disposed within the camera housings 345A-345D. In some embodiments, the image sensors, imaging lenses, and color filters described above may be housed within a single camera housing, as shown, for example, in FIGS. 20A-20B. Thus, while the implementations shown in these figures use separate sensors (e.g., one sensor housed within each of the camera housings 345A-345D), it will be appreciated that other implementations may use a single image sensor disposed over the entire area exposed through the openings 320A-320D. In this embodiment, the camera housings 345A-345D are secured to the imaging system housing 305 using a support 340, but may be secured using other suitable means in various implementations.

[0096] The top surface of the housing 305 supports an optional illumination board 310, which is covered with an optical diffusing element 315. The illumination board 310 is described in more detail in connection with FIG. 4E below. The diffusing element 315 may be made of glass, plastic, or other optical materials and diffuses the light emitted from the illumination board 310 so that the object space receives substantially uniform illumination in space. Uniform illumination of the target object may be beneficial in certain imaging applications, such as clinical analysis of tissue by imaging, since it provides a substantially uniform amount of illumination across the object's surface at each wavelength. In some embodiments, the imaging systems disclosed herein may utilize ambient light instead of or in addition to the light from the optional illumination board.

[0097] Because the illumination board 310 generates heat during use, the imaging system 300 includes a heat sink 350 with a number of heat dissipation fins 355. The heat dissipation fins 355 may extend into the space between each of the camera housings 345A-345D, and the top of the heat sink 350 may conduct heat away from the illumination board 310 to the fins 355. The heat sink 350 may be made from a suitable thermally conductive material. The heat sink 350 may assist in dissipating heat from other components, which may eliminate the need for a fan in some implementations of the imaging system.

[0098] A processing board 335 is secured within the housing 305 by a number of supports 365, which communicate with the cameras 345A-345D. The processing board 335 may control the operation of the imaging system 300. Although not shown in FIGS. 4A-4D, the imaging system 300 may also be configured with one or more memories, such as a memory for storing data generated using the imaging system, and / or a computer-executable instruction module for system control. The processing board 335 may be configured in various ways depending on the system design objectives. For example, the processing board may be configured (e.g., via a computer-executable instruction module) to control the activation of specific LEDs on the illumination board 310. In some implementations, a highly stable synchronous buck LED driver may be used, which allows for software analog current control of the LEDs and detection of LED failures. In some implementations, the processing board 335 or a separate processing board may further provide image data analysis capabilities (e.g., via a computer-executable instruction module). Although not shown in Figures 4A-4D, imaging system 300 may include a data interconnect between the sensor and processing board 335 so that processing board 335 can receive data from the sensor and process the data, and may include a data interconnect between illumination board 310 and processing board 335 so that the processing board can activate specific LEDs on illumination board 310.

[0099] FIG. 4E illustrates an example of an illumination board 310 that may be included in imaging system 300, with the illumination board 310 separated from other components. The illumination board 310 includes four arms extending from a central portion, with three columns of LEDs arranged along each arm. The spacing between the LEDs in adjacent columns is offset laterally from one another to separate adjacent LEDs. Each LED column includes multiple rows of LEDs of various colors. Four green LEDs 371 are arranged in the center, with one green LED at each corner of the center. Starting from the innermost row (e.g., closest to the center), each column includes a row of two crimson LEDs 372 (for a total of eight crimson LEDs). Continuing radially outward, each arm has a row in the center column with one amber LED 374, a row on either side with two short blinking blue LEDs 376 (for a total of eight short blinking blue LEDs), another row in the center column with one amber LED 374 (for a total of eight amber LEDs), a row on either side with one non-PPG NIR LED 373 and one red LED 375 (for a total of four non-PPG NIR LEDs and four red LEDs), and a PPG NIR LED 377 in the center column (for a total of four PPG NIR LEDs). "PPG" LEDs refer to LEDs that are activated in multiple successive exposures to capture photoplethysmography (PPG) information representative of pulsatile blood flow in living tissue. It will be appreciated that various other colors and / or arrangements of LEDs may be used in the illumination board in alternative embodiments.

[0100] FIG 5 illustrates another embodiment of a multispectral, multi-aperture imaging system 300 with the optical design described in connection with FIG 3A and FIG 3B. Similar to the design of imaging system 300, imaging system 400 includes four optical paths, shown here as apertures 420A-420D with multiple bandpass filter lens groups 425A-425D secured to housing 405 by retaining rings 430A-430D. Imaging system 400 further includes an illumination board 410 secured to a front portion of housing 405 between retaining rings 430A-430D and a diffuser 415 disposed on illumination board 410 to aid in providing spatially uniform illumination of light onto the target object.

[0101] The illumination board 410 of the imaging system 400 has LEDs arranged in four arms of a cross, with each arm having two rows of closely spaced LEDs. In this manner, the illumination board 410 may be more compact than the illumination board 310 described above, and may be suitable for use with imaging systems having smaller form factor requirements. In this example configuration, each arm has one green LED and one blue LED in the outermost row, and moving inward, includes two rows of yellow LEDs, one row of orange LEDs, one row of red LEDs, one row of crimson LEDs, one row of amber LEDs, and one row of NIR LEDs. Thus, in this implementation, the LEDs are arranged such that the LEDs emitting longer wavelengths of light are located in the center of the illumination board 410, and the LEDs emitting shorter wavelengths of light are located at the ends of the illumination board 410.

[0102] 6A-6C illustrate another embodiment of a multispectral, multi-aperture imaging system 500 with the optical design described in connection with FIGS. 3A and 3B. More specifically, FIG. 6A illustrates a perspective view of the imaging system 500, FIG. 6B illustrates a front view of the imaging system 500, and FIG. 6C illustrates a cutaway side view of the imaging system 500 taken along line CC of FIG. 6B. The imaging system 500 includes similar components as previously described in connection with the imaging system 300 (e.g., a housing 505, an illumination board 510, a diffuser 515, multiple bandpass filters 525A-525D secured above the apertures by retaining rings 530A-530D, etc.), but in a shorter form factor (e.g., in one embodiment, fewer embedded computer components and / or smaller dimensions of embedded computer components). The imaging system 500 further includes a direct camera-to-frame mount 540 for rigidly and robustly fixing the position of the camera alignment.

[0103] 7A-7B show another embodiment of the multispectral, multi-aperture imaging system 600. FIG. 7A-7B shows another possible arrangement of the ambient light sources 610A-610C of the multi-aperture imaging system 600. As shown in this figure, four lens assemblies with multiple bandpass filters 625A-625D with the optical design described in connection with FIG. 3A-3D can be arranged in a rectangular or square shape to be received by four cameras 630A-630D (including image sensors). Three rectangular light emitting elements 610A-610C can be arranged parallel to each other outside and between each lens assembly with multiple bandpass filters 625A-625D. These rectangular light emitting elements can be broad spectrum light emitting panels, each of which can be arranged with LEDs emitting light in different frequency bands.

[0104] 8A-8B show another embodiment of the multispectral, multi-aperture imaging system 700. FIG. 8A-8B shows another possible arrangement of the ambient light sources 710A-710D of the multi-aperture imaging system 700. As shown in this figure, four lens assemblies with multiple bandpass filters 725A-725D employing the optical design described in connection with FIG. 3A-3D can be arranged in a rectangular or square shape to be received by four cameras 730A-730D (including image sensors). The four cameras 730A-730D are shown as an example of a closer arrangement, which may minimize the perspective difference between the lenses. Four rectangular light emitting elements 710A-710D can be arranged in a square shape surrounding the lens assemblies with multiple bandpass filters 725A-725D. These rectangular light emitting elements may be broad spectrum light emitting panels, each of which may be arranged with LEDs emitting light in different frequency bands.

[0105] 9A-9C show another embodiment of a multispectral, multi-aperture imaging system 800. The imaging system 800 includes a frame 805 connected to a lens assembly front frame 830, which includes an aperture 820 and a support structure for a micro-video lens 825, which may include multiple bandpass filters using the optical design described with respect to FIGS. 3A-3D. The micro-video lens 825 receives light from four cameras 845 (including imaging lenses and image sensor areas) mounted on a lens assembly rear frame 840. Four linear rows of LEDs 811 are arranged along the four sides of the lens assembly front frame 830, with a diffusing element 815 for each LED on each of the four sides. FIGS. 9B and 9C show an example of possible dimensions in inches for the multi-aperture imaging system 800.

[0106] FIG. 10A illustrates another embodiment of a multispectral, multiaperture imaging system 900 with the optical design described in connection with FIGS. 3A-3D. The imaging system 900 can be implemented as a set of multiple bandpass filters 905 that can be attached to a multiaperture camera 915 of a mobile terminal 910. For example, a particular mobile terminal 910, such as a smartphone, can be equipped with a stereoscopic imaging system having two apertures leading to two image sensor areas. The multiaperture spectral imaging techniques disclosed herein can be implemented in such a device by providing a suitable set of multiple bandpass filters 905 that pass multiple narrow frequency bands of light to the sensor areas. The set of multiple bandpass filters 905 can be equipped with a light source (such as an LED array and a diffuser) that irradiates the object space with light in such frequency bands.

[0107] The imaging system 900 may further include a mobile application that configures the mobile terminal to generate and process the multispectral datacube (e.g., in a clinical tissue classification application, a biometric application, a material analysis application, or other application). Alternatively, the mobile application may configure the mobile terminal 910 to transmit the multispectral datacube over a network to a remote processing system and then receive and display the analysis results. An example of a user interface 910 for such an application is shown in FIG. 10B.

[0108] 11A-11B show an example of a set of frequency bands that can pass through four filters when implemented in the multispectral, multi-aperture imaging system shown in FIGS. 3A-10B, and the set of frequency bands is incident on an image sensor having, for example, a Bayer CFA (or another RGB CFA or RGB-IR CFA). JPEG2025504857000002.jpg28150These transmission curves also include the effect of quantum efficiency of the sensor used in this example. As shown in these figures, the set of four cameras captures eight unique channels or eight unique frequency bands. Each filter passes two common frequency bands for each camera (the two leftmost peaks) and two other frequency bands. In this implementation, the first and third cameras receive light in the first shared NIR frequency band (the rightmost peak), and the second and fourth cameras receive light in the second shared NIR frequency band (the second peak from the right). Each camera receives one unique frequency band ranging from about 550 nm to about 800 nm or from 550 nm to 800 nm. Thus, these cameras can capture eight unique spectral channels using a compact configuration. Graph 1010 in FIG. 11B shows the spectral irradiance of the LED board shown in FIG. 4E that may be used to illuminate the four cameras shown in FIG. 11A.

[0109] In this implementation, the eight frequency bands were selected based on generating spectral channels suitable for clinical tissue classification, but may be optimized for signal-to-noise ratio (SNR) and frame rate while limiting the number of LEDs (which transmit heat into the imaging system). The eight frequency bands include a common frequency band of blue light (the left-most peak in graph 1000) that passes through all four filters because tissue (e.g., animal tissue such as human tissue) exhibits higher contrast in blue wavelengths than in green and red wavelengths. More specifically, as shown in graph 1000, human tissue has the highest contrast when imaged with a frequency band centered at about 420 nm. The channel corresponding to this common frequency band is used for disparity correction, so that higher contrast allows for more accurate correction. For example, in performing disparity correction, the image processor can use a local or global method to find a set of disparities that maximizes a figure of merit that indicates similarity between local image patches or local images. Alternatively, the image processor can use a similar method to minimize a figure of merit that indicates dissimilarity. These figures of merit may be based on entropy, correlation, absolute difference or deep learning methods. The calculation of disparity by global methods may be performed iteratively and terminated when the figure of merit is stable. Local methods may calculate disparity pixel by pixel, using a fixed patch in one image as input for the figure of merit and evaluating different disparity values ​​for many patches cut from other images during testing. All such methods may constrain the range of disparities considered. The constraint may be based, for example, on knowledge of the depth and distance of the object. It may also be constrained based on the range of expected tilts of the object. The calculated disparity may be constrained by projective geometry, such as epipolar constraints. The output of the disparity calculated at a lower resolution may be used as an initial value or constraint for the calculation of the disparity at the next level of resolution to calculate the disparity.For example, one can use the disparity calculated at a resolution of 4 pixels in a first calculation and set a constraint of ±4 pixels in the next calculation of the disparity at a higher resolution. Any algorithm that calculates from disparity benefits from higher contrast, especially when the contrast source is relevant for all viewpoints. Typically, a common frequency band can be selected to accommodate the highest contrast imaging of the materials expected to be imaged in a particular application.

[0110] Since the color separation between adjacent channels after capturing an image may not be perfect, in this implementation, an additional common frequency band is provided as a green frequency band adjacent to the blue frequency band, which passes through all the filters shown in graph 1000. The reason for using such a frequency band is that the pixels of the blue color filter have a wide spectral passband and therefore are sensitive to the green spectral region. This is usually shown as a spectral overlap and may be considered as an intentional crosstalk between adjacent RGB pixels. This spectral overlap allows the spectral sensitivity of the color camera to approach that of the human retina, making the final color space qualitatively similar to human vision. Thus, the common green channel for each filter allows the separation of the part of the signal generated by the blue photodiode that truly corresponds to the received blue light. This separation can be achieved using a spectral unmixing algorithm that factors in the transmittance of the multi-bandpass filters (shown as solid black lines in the T legend) and the corresponding transmittance of the CFA color filters (shown as dashed red, green, and blue lines in the Q legend, respectively). It will be appreciated that in some implementations, red light may be used as the common frequency band, in which case a second common channel may not be required.

[0111] 12 shows a high-level block diagram of an example of a compact imaging system 1100 with high resolution spectral imaging capabilities, the imaging system 1100 comprising a set of components including a processor 1120 coupled to a multi-aperture spectral camera 1160 and a light source 1165. A working memory 1105, a storage device 1110, an electronic display 1125 and a memory 1130 are also in communication with the processor 1120. As described herein, the imaging system 1100 may be able to capture more image channels by placing multiple different bandpass filters over the aperture of the multi-aperture spectral camera 1160 than would be possible with different color filters placed on the CFA of the image sensor.

[0112] The compact imaging system 1100 may be a device such as a mobile phone, a digital camera, a tablet computer, a personal digital assistant, etc. The compact imaging system 1100 may be a stationary device such as a desktop computer or a videoconferencing station using an internal or external camera for image capture. The compact imaging system 1100 may be a combination of an image capture device and a separate processing device that receives image data from the image capture device. A user may have multiple applications available on the compact imaging system 1100. These applications may include traditional photography applications, applications for still and video capture, dynamic color correction applications, brightness and shade correction applications, etc.

[0113] The image capture system 1100 includes a multi-aperture spectral camera 1160 for image capture. The multi-aperture spectral camera 1160 may be, for example, any of the devices shown in Figs. 3A-10B. The multi-aperture spectral camera 1160 may be connected to the processor 1120 so that images captured in different spectral channels can be sent from different sensor areas to the image processor 1120. Also, as described in more detail below, a single or multiple light sources 1165 may be controlled by the processor to emit light of specific wavelengths during a particular exposure. The image processor 1120 may be configured to perform various processes on the received captured images so as to output a high-quality parallax-corrected multi-spectral data cube.

[0114] The processor 1120 may be a general-purpose processing unit or a processor specifically designed for imaging applications. As shown in FIG. 12, the processor 1120 is connected to a memory 1130 and a working memory 1105. In the embodiment shown in this figure, the memory 1130 stores an acquisition control module 1135, a data cube creation module 1140, a data cube analysis module 1145, and an operating system 1150. These modules contain instructions that configure the processor to perform various image processing and device management tasks. The processor 1120 uses the working memory 1105 to store a working set of processor instructions contained in each module of the memory 1130. Alternatively, the processor 1120 uses the working memory 1105 to store dynamic data created during operation of the image acquisition system 1100.

[0115] As mentioned above, the processor 1120 is composed of several modules stored in the memory 1130. In some implementations, the imaging control module 1135 includes instructions for configuring the processor 1120 to adjust the focus position of the multi-aperture spectral camera 1160. The imaging control module 1135 further includes instructions for configuring the processor 1120 to capture images, such as multispectral images captured in various spectral channels and PPG images captured in the same spectral channel (e.g., NIR channel), with the multi-aperture spectral camera 1160. Non-contact PPG imaging typically uses near-infrared (NIR) wavelengths as illumination, taking advantage of the increased penetration of photons into tissue at these wavelengths. Thus, the imaging control module 1135, the multi-aperture spectral camera 1160 and the processor 1120 with the working memory 1105 are one of the means for capturing a series of spectral images and / or consecutive images.

[0116] The datacubing module 1140 includes instructions that configure the processor 1120 to generate a multispectral data cube based on the intensity signals received from each photodiode in the different sensor regions. For example, the datacubing module 1140 can estimate the disparity between the same regions of the imaged object based on the spectral channels corresponding to the common frequency bands passed by all the multiple bandpass filters, and use this disparity to align all the spectral images in all the captured channels with each other (e.g., the same point on a particular object is aligned at substantially the same pixel location (x,y) in all the spectral channels). The aligned images can be combined to form a multispectral data cube, and the disparity information can be used to measure the depth of the separately imaged objects, for example, to measure the difference between the depth of normal tissue and the depth of the deepest point of a wound site. In some embodiments, the datacubing module 1140 can further perform spectral unmixing to determine which portions of the photodiode intensity signals correspond to which frequency bands passed by the filters, for example, based on a spectral unmixing algorithm that factors in the transmittance of the filters and the quantum efficiency of the sensor.

[0117] The datacube analysis module 1145 can implement various techniques to analyze the multispectral datacube generated by the datacube creation module 1140 for a particular application. For example, some implementations of the datacube analysis module 1145 can provide the multispectral datacube (which may include depth information) to a machine learning model trained to classify each pixel according to a particular condition. The particular condition may be a clinical condition for tissue imaging, such as a burn condition (e.g., first degree burn, second degree burn, third degree burn, or normal tissue type), a wound condition (e.g., hemostasis, inflammation, proliferation, remodeling, or normal skin type), healing potential (e.g., a score reflecting the likelihood that the tissue will heal from the wound condition with or without a particular treatment), a perfusion state, a cancer state, or other tissue condition associated with the wound. The datacube analysis module 1145 can also analyze the multispectral datacube for biometric and / or material analysis purposes.

[0118] The operating system module 1150 configures the processor 1120 to manage memory and processing resources of the image capture system 1100. For example, the operating system module 1150 may include device drivers that manage hardware resources such as the electronic display 1125, the storage device 1110, the multi-aperture spectral camera 1160, the light source 1165, and the like. Thus, in some embodiments, the instructions included in the image processing module described above may not directly interact with these hardware resources, but instead may interact through standard subroutines or APIs included in the operating system component 1150. The instructions in the operating system 1150 may then directly interact with these hardware components.

[0119] Additionally, the processor 1120 may be configured to control the display 1125 to display the captured images and / or the analysis results of the multispectral datacube (e.g., classified images) to a user. The display 1125 may be external to the imaging device including the multi-aperture spectral camera 1160 or may be part of the imaging device. The display 1125 may also be configured to provide a viewfinder to the user prior to capturing an image. Additionally, the display 1125 may include an LCD screen or an LED screen and may implement touch-sensitive technology.

[0120] The processor 1120 may write data to the storage module 1110, such as data representing captured images, multispectral data cubes, and analysis results of the data cubes. In this figure, the storage module 1110 is shown as a conventional disk device, but one skilled in the art will appreciate that the storage module 1110 may be configured as any type of storage media device. For example, the storage module 1110 may include a disk drive, such as a floppy disk drive, a hard disk drive, an optical disk drive, a magneto-optical disk drive, or a solid-state memory, such as a FLASH memory, a RAM, a ROM, and / or an EEPROM. The storage module 1110 may further include multiple memory units, any one of which may be configured to be located inside the image capture device 1100 or may be located outside the image capture system 1100. For example, the storage module 1110 may include a ROM memory that includes system program instructions stored within the image capture system 1100. Additionally, the storage module 1110 may include a memory card or high-speed memory that is removable from the camera and configured to store captured images.

[0121] 12 shows a system including separate components such as a processor, an image sensor, and a memory, those skilled in the art will recognize that these separate components can be combined in various ways to achieve particular design goals. For example, in another embodiment, memory components may be combined with processor components to reduce cost and improve performance.

[0122] Further, while FIG. 12 shows two memory components, a memory component 1130 including several modules and a separate memory 1105 including a working memory, one skilled in the art will recognize that other memory structures may be utilized in some embodiments. For example, a ROM or static RAM memory may be utilized to store processor instructions implementing each module included in the memory 1130. Alternatively, the processor instructions may be read during system startup from a disk storage device integrated into the image capture system 1100 or connected via an external device port. The processor instructions may then be loaded into a RAM to facilitate execution of the instructions by the processor. For example, the working memory 1105 may be a RAM memory, and the instructions may be loaded into the working memory 1105 prior to execution of the instructions by the processor 1120.

[0123] Overview of an example of image processing technology FIG. 13 is a flow chart illustrating an example process 1200 for capturing image data using the multispectral, multi-aperture imaging system illustrated in FIGS. 3A-10B and 12. FIG. 13 illustrates an example of four exposures that can be used to generate a multispectral data cube as described herein: a visible light exposure 1205, an additional visible light exposure 1210, a non-visible light exposure 1215, and an ambient light exposure 1220. It will be appreciated that the exposures may be captured in any order and that some of the exposures may be omitted from or added to certain workflows described below. The process 1200 is described below with reference to the frequency bands illustrated in FIGS. 11A and 11B, although a similar workflow may be implemented using image data generated based on a different set of frequency bands. Additionally, in various embodiments, image acquisition and / or parallax correction may be enhanced by further implementing flat-field correction according to various known flat-field correction techniques.

[0124] To obtain the visible light exposure 1205, a control signal may be sent to the illumination board to light up the LEDs corresponding to the first five peaks (the five peaks on the left side corresponding to visible light in the graph 1000 of FIG. 11A). It may be necessary to stabilize the light output waveform of the particular LEDs simultaneously (e.g., for 10 ms). The imaging control module 1135 may then start the exposure of the four cameras and continue this exposure for, for example, about 30 ms. The imaging control module 1135 then stops the exposure and acquires data from the sensor areas (e.g., by transferring raw intensity signals from the photodiodes to the working memory 1105 and / or data store 1110). This data may include a common spectral channel used for parallax correction, as described herein.

[0125] To improve the signal-to-noise ratio, in some implementations, an additional visible light exposure 1210 may be taken using the same process as described for visible light exposure 1205. Taking two identical or nearly identical exposures may improve the signal-to-noise ratio, allowing for more accurate analysis of the image data. However, in implementations where the signal-to-noise ratio of only a single image is acceptable, this second exposure may be omitted. Also, in some implementations, taking two exposures in a common spectral channel may provide more accurate parallax correction.

[0126] In some implementations, a non-visible light exposure 1215 corresponding to NIR or IR light can be further captured. For example, the capture control module 1135 can activate two NIR LEDs corresponding to the two NIR channels shown in FIG. 11A. It may be necessary to stabilize the light output waveform of certain LEDs simultaneously (e.g., for 10 ms). The capture control module 1135 can then start an exposure of the four cameras and continue this exposure for, for example, about 30 ms. The capture control module 1135 then stops the exposure and acquires data from the sensor areas (e.g., by transferring raw intensity signals from the photodiodes to the working memory 1105 and / or data store 1110). Since this exposure can safely assume no change in the shape or position of the object relative to the exposures 1205 and 1210, a common frequency band that passes through all the sensor areas can be used to align each NIR channel using previously calculated disparity values.

[0127] In some implementations, multiple exposures may be used to capture a sequence of images to generate PPG data indicative of deformation of the tissue site due to pulsatile blood flow. In some implementations, such PPG exposures may be captured at non-visible wavelengths. Combining the PPG data with the multispectral data may improve the accuracy of certain medical imaging analyses, but capturing the PPG data may increase the time required for the image capture process. In some implementations, the increased time required for the image capture process may result in movement of the portable imaging device and / or the object, which may introduce errors. Thus, in certain implementations, capturing the PPG data may be omitted.

[0128] In some implementations, an ambient light exposure 1220 may be further taken. In this exposure, all LEDs may be turned off and an image may be taken using ambient lighting (e.g., sunlight or light from other sources). The capture control module 1135 may then initiate an exposure of the four cameras and continue this exposure for a desired time (e.g., about 30 milliseconds). The capture control module 1135 may then stop the exposure and acquire data from the sensor area (e.g., by transferring raw intensity signals from the photodiodes to the working memory 1105 and / or data store 1110). The effect of ambient light may be removed from the multispectral data cube by subtracting the intensity values ​​of the ambient light exposure 1220 from the numerical values ​​of the visible light exposure 1205 (or the visible light exposure 1205 corrected for signal-to-noise ratio by the second exposure 1210) and also from the numerical values ​​of the non-visible light exposure 1215. Such processing can isolate portions of the resulting signal indicative of light emitted by the light source and light reflected from the object / tissue site to improve the accuracy of downstream analysis. In some implementations, this step may be omitted if sufficient analytical accuracy is achieved using only visible light exposures 1205, 1210 and non-visible light exposures 1215.

[0129] It will be appreciated that the particular exposure times listed above are examples in one implementation and that in other implementations the exposure times may vary depending on the image sensor, the intensity of the light source, and the object being imaged.

[0130] Figure 14 shows a simplified block diagram of a workflow 1300 for processing image data, such as image data captured using the multispectral, multi-aperture imaging system shown in Figures 3A-10B and 12 and / or using process 1200 shown in Figure 13. Although workflow 1300 shows the output of two RGB sensor areas 1301A, 1301B, the number of sensor areas in workflow 1300 can be increased and there can be sensor areas corresponding to different CFA color channels.

[0131] The RGB sensor outputs from the two sensor regions 1301A, 1301B are stored in 2D sensor output modules 1305A, 1305B, respectively. These sensor region values ​​are sent to non-linear mapping modules 1310A, 1310B, which can perform parallax correction by determining the disparity between images captured using a common channel and then applying this determined disparity across all channels to align all spectral images with each other.

[0132] The output of these non-linear mapping modules 1310A, 1310B are then provided to a depth calculation module 1335, which can calculate the depth of a particular region of interest in the image data. For example, the depth may represent the distance between the object and the image sensor. In some implementations, multiple depth values ​​can be calculated and compared to measure the depth of the object relative to some other object than the image sensor. For example, the deepest depth of the wound bed can be measured, as can the depth of the normal tissue surrounding the wound bed (deepest, shallowest or average depth). The deepest depth of the wound can be measured by subtracting the normal tissue depth from the wound bed depth. This depth comparison can also be performed at other locations in the wound bed (e.g., all or some of the given sampling points), thereby constructing a 3D map of the wound depth at various points (shown in FIG. 14 as z(x,y) where z is the depth value). Larger disparities impose higher computational burden on algorithms for calculating depth as described above, although in some embodiments larger disparities may improve depth calculations.

[0133] The output of these nonlinear mapping modules 1310A, 1310B are then also provided to a linear equation module 1320, which can process the sensed values ​​as a set of linear equations for spectral unmixing. In one implementation, the Moore-Penrose pseudo-inverse equation can be used as a function of at least the quantum efficiency of the sensor and the transmittance values ​​of the filters to calculate the actual spectral values ​​(e.g., the intensity of light of a particular wavelength incident on each image point (x,y)). Spectral unmixing can be used in implementations requiring high accuracy, such as clinical diagnostics and other biological applications. Spectral unmixing can also be applied to provide estimates of photon flux and signal-to-noise ratio.

[0134] Based on the parallax-corrected spectral channel images and spectral unmixing, the workflow 1300 can generate a spectral data cube 1325, such as in the form shown in this figure of F(x,y,λ), where F denotes the light intensity at a particular image location (x,y) at a particular wavelength or frequency band λ.

[0135] FIG. 15 illustrates parallax and parallax correction for processing image data, such as image data captured using the multispectral, multi-aperture imaging system shown in FIGS. 3A-10B and 12 and / or using the process shown in FIG. 13. A first set of images 1410 shows image data of the same physical location on the same object captured by four sensor regions. As shown in this figure, the object locations are not at the same location in these raw images based on the (x,y) coordinate frame of the photodiode grid of the image sensor region. A second set of images 1420 shows the same object location after parallax correction, where the images are at the same (x,y) location in the coordinate frame of the aligned images. It will be appreciated that such alignment may include cropping certain data from edge regions of the images that do not completely overlap one another.

[0136] FIG. 16 illustrates a workflow 1500 for pixel-by-pixel classification of multispectral image data, such as image data captured using the multispectral, multi-aperture imaging system shown in FIGS. 3A-10B and 12 and / or using the process shown in FIG. 13 and then processed according to FIGS. 14 and 15 .

[0137] In block 1510, a multispectral, multi-aperture imaging system 1513 can capture image data indicative of physical points 1512 on an object 1511. In this example, the object 1511 includes tissue of a patient having a wound. The wound may include a burn, a diabetic ulcer (e.g., a diabetic foot ulcer), a non-diabetic ulcer (e.g., a pressure ulcer or a slow-healing wound), a chronic ulcer, a post-operative incision, an amputation site (before or after an amputation), a cancerous lesion, or damaged tissue. When PPG information is included, the imaging system disclosed herein provides a method for evaluating conditions involving changes in tissue blood flow and changes in pulse rate, including tissue perfusion; cardiovascular health; injuries such as ulcers; peripheral arterial disease; and respiratory health.

[0138] In block 1520, the data captured by the multispectral, multi-aperture imaging system 1513 may be processed to generate a multispectral datacube 1525 having multiple different wavelengths 1523, which may include multiple different images (PPG data 1522) of the same wavelength corresponding to different times. For example, the image processor 1120 may be configured via the datacube creation module 1140 to generate the multispectral datacube 1525 according to the workflow 1300. Some implementations may be associated with depth values ​​at various points in the spatial dimensions, as previously described.

[0139] In block 1530, the multispectral data cube 1525 can be analyzed as input data 1525 to a machine learning model 1532 to generate a classification mapping 1535 of the imaged tissue. The classification mapping allows each pixel in the image data (each pixel representing a particular point on the imaged object 1511 after registration) to be assigned a particular tissue classification or a particular healability score. Different classifications or scores can be indicated in the classified image output by using visually distinct colors or patterns. Thus, even if many images of the object 1511 are taken, the output can be a single image of the object (e.g., a regular RGB image) overlaid with multiple visual indications of the pixel-by-pixel classification.

[0140] In some implementations, the machine learning model 1532 may be an artificial neural network. An artificial neural network is a computational entity inspired by biological neural networks but modified for implementation by a computer device. Although dependencies between inputs and outputs are not easily found, artificial neural networks can be used to model complex relationships between inputs and outputs and to find certain patterns in data. A neural network typically includes an input layer, one or more intermediate layers ("hidden layers"), and an output layer, each of which includes many nodes. The number of nodes may vary from layer to layer. A neural network that includes two or more hidden layers is considered to be "deep". The nodes of each layer are connected to all or some of the nodes of the next layer, and the weights of these connections are typically learned from data during a training process, for example using backpropagation to adjust the parameters of the neural network to generate expected outputs from corresponding inputs in the labeled training data. An artificial neural network can thus be thought of as an adaptive system configured to change its structure (e.g., connections and / or weights) during training based on the information flowing through it, and the hidden layer weights can be thought of as encoding meaningful patterns in the data.

[0141] A fully connected neural network is a neural network in which each node in the input layer is connected to each node in the next layer (the first hidden layer), each node in the first hidden layer is connected to each node in the next hidden layer, and so on until each node in the final hidden layer is connected to each node in the output layer.

[0142] A convolutional neural network (CNN) is a type of artificial neural network that, like the previously described artificial neural networks, is composed of multiple nodes with learnable weights. Unlike the previously described artificial neural networks, each layer of a CNN may have nodes arranged in three dimensions, with width, height, and depth corresponding to the pixel values ​​of a 2×2 array of each video frame (e.g., width and height) and the number of video frames in the sequence (e.g., depth). The nodes of each layer may be locally connected to a small region of the previous layer, called the receptive field, which is also of width and height. The weights of the hidden layer may take the form of a convolution filter applied to the receptive field. In some embodiments, the convolution filter may be two-dimensional, so that the convolution (or convolution transformation of an image) can be repeated with the same filter for each frame of the input volume or for a specified subset of frames. In another embodiment, the convolution filter may be three-dimensional, so that it spans the full depth of multiple nodes of the input volume. Each node in each convolutional layer of a CNN shares weights, duplicating the convolutional filters of a given layer across the width and height of the input volume (e.g., for every frame), thereby reducing the total number of trainable weights and improving the applicability of the CNN to datasets outside the training data. Values ​​from certain layers may be pooled to reduce the number of calculations in the next layer (e.g., values ​​representing certain pixels may be propagated to the next layer, while other values ​​are discarded), and furthermore, as the CNN progresses in depth, a pooling mask may be reintroduced to the discarded values, returning the number of data points relative to the original dimensions. Multiple layers, some of which may be fully connected, may be stacked to form a CNN structure.

[0143] During training, the artificial neural network can be applied to the paired data in the training data to vary parameters and predict the output of a particular pair given the input. For example, the training data can include a multispectral data cube (input) and a labeled classification mapping (expected output), such as by a clinician designating wound areas that correspond to particular clinical conditions, which may include a healed label (1) or an unhealed label (0) that is assigned some time after the initial imaging of the wound and actual healing is confirmed. Alternative implementations of the machine learning model 1532 can be trained to make other types of predictions, such as predicting the likelihood that a wound will heal and reduce the wound area by a certain percentage within a certain time period (e.g., at least a 50% reduction in wound area within 30 days), or predict the wound condition classified as hemostasis, inflammation, proliferation, remodeling, normal skin, etc. In some implementations, to further improve the accuracy rate of classification, the patient's evaluation metrics may be further incorporated into the input data, or the training data may be segmented based on the patient's evaluation metrics and used on different patients with the same evaluation metrics to train the machine learning model 1532 on different examples. Patient evaluation indicators may include textual information or medical history or aspects thereof describing characteristics of the patient or the patient's health condition, such as, for example, the area of ​​the wound, lesion or ulcer; the patient's BMI; the patient's diabetic status; the presence of peripheral vascular disease or chronic inflammation in the patient; the number of other wounds the patient currently has or has had; whether the patient is receiving or has recently received immunosuppressants (e.g., chemotherapy) or other medications that positively or negatively affect wound healing rate; HbA1c; chronic renal failure stage IV; whether the patient has type 1 or type 2 diabetes; chronic anemia; asthma; medication use; smoking status; diabetic neuropathy; deep vein thrombosis; previous myocardial infarction; transient ischemic attack; and sleep apnea; and any combination thereof.These metrics can be converted into vector representations by appropriate processing, such as vector embeddings of words, vectors of binary values ​​indicating whether a patient meets a particular metric (e.g., whether or not the patient has type 1 diabetes), or numerical values ​​indicating the extent of each metric in the patient.

[0144] At block 1540, a classification mapping 1535 can be output to a user. In one example, the classification mapping 1535 uses a first color 1541 to indicate pixels classified according to a first state and a second color 1542 to indicate pixels classified according to a second state. The classification and resulting classification mapping 1535 can exclude background pixels based on, for example, object recognition, background color identification, and / or depth values. As shown in FIG. 16, some implementations of the multispectral and multiaperture imaging system 1513 can project the classification mapping 1535 onto the tissue site. Such an embodiment can be particularly beneficial when a visual indication of recommended resection margins and / or resection depth is included in the classification mapping.

[0145] Such methods and systems may assist clinicians and surgeons in the process of managing skin wounds, such as making decisions on burn excision, amputation level, lesion removal, and wound triage. The embodiments described herein can be used to identify and / or classify the severity of decubitus ulcers, hyperemia, limb deterioration, Raynaud's phenomenon, scleroderma, chronic wounds, abrasions, lacerations, bleeding, ruptures, puncture wounds, penetration wounds, skin cancer (basal cell carcinoma, squamous cell carcinoma, malignant melanoma, or actinic keratosis), or any type of tissue change in which the characteristics and properties of the tissue differ from normal. The devices described herein can also be used to monitor normal tissue; aid and improve wound treatment methods (e.g., allowing for faster and more sophisticated approaches to determining debridement margins); and evaluate the progress of recovery from wounds or diseases (especially after treatment has been applied). Some embodiments described herein provide devices capable of identifying normal tissue adjacent to damaged tissue, determining margins and / or depth during resection, monitoring the healing process after implantation of a prosthetic device such as a left ventricular assist device, assessing the viability of tissue or regenerative cell grafts, or monitoring post-operative recovery (particularly following reconstructive surgery). Additionally, embodiments described herein can be used to assess wound changes or post-wound normal tissue development, particularly following administration of therapeutic agents such as steroids, hepatocyte growth factor, fibroblast growth factor, antibiotics, regenerative cells (e.g., isolated or enriched cell populations including stem cells, endothelial cells, and / or endothelial progenitor cells).

[0146] Overview of an example distributed computing environment Figure 17 shows a simplified block diagram of an example of a distributed computing system 1600 including a multispectral, multi-aperture imaging system 1605, which may be any of the multispectral, multi-aperture imaging systems shown in Figures 3A-10B and 12. As shown in this figure, a datacube analysis server 1615 may include one or more computers, perhaps arranged as a server cluster or server farm, whose memory and processors may be located within one computer or may be distributed across many computers (including computers located remotely from one another).

[0147] The multispectral and multi-aperture imaging system 1605 may include networking hardware (e.g., wireless internet, satellite, Bluetooth or other communication) for communicating with the user devices 1620 and the data cube analysis server 1615 via the network 1610. For example, in some implementations, the processor of the multispectral and multi-aperture imaging system 1605 may be configured to control image capture and then transmit the raw data to the data cube analysis server 1615. In other implementations, the processor of the multispectral and multi-aperture imaging system 1605 may be configured to control image capture, perform spectral unmixing and parallax correction to generate a multispectral data cube, and then transmit the multispectral data cube to the data cube analysis server 1615. In some implementations, all processing and analysis may be performed locally on the multispectral and multi-aperture imaging system 1605, and the multispectral data cube and resulting analysis results may be transmitted to the data cube analysis server 1615 for performing aggregate analysis and / or for use in training or retraining machine learning models. Thus, the datacube analysis server 1615 may provide the latest machine learning models to the multispectral and multiaperture imaging system 1605. The processing load for the final result of analyzing the multispectral datacube may be shared in various ways between the multiaperture imaging system 1605 and the datacube analysis server 1615 depending on the processing power of the multiaperture imaging system 1605.

[0148] The network 1610 may include any suitable network, such as an intranet, the Internet, a cellular network, a local area network, other similar networks, or combinations thereof. The user device 1620 may include any network-equipped computing device, such as a desktop computer, a laptop, a smart phone, a tablet, an e-reader, a video game console, etc. For example, when tissue classification is required, the results (e.g., classified images) measured by the multi-aperture imaging system 1605 and the data cube analysis server 1615 may be transmitted to a patient or physician designated user device, a hospital information system that stores the patient's electronic medical record, and / or a centralized public health database (e.g., maintained by the Centers for Disease Control and Prevention, etc.).

[0149] An example of the implementation result background: Burn morbidity and mortality pose a challenge to wounded combatants and their medical personnel. Burn injury rates in historical combat casualties range from 5-20%, with approximately 20% of these casualties requiring complex burn surgery, such as at the U.S. Army Institute of Surgical Research (ISR) Burn Center. Burn surgery requires specialized training, and is therefore performed by ISR staff rather than U.S. Army hospital staff. Limited numbers of burn specialists greatly complicate the logistics of providing medical care to burned soldiers. Thus, the use of novel objective methods to detect burn depth preoperatively and intraoperatively can ensure that a greater number of medical personnel (including personnel from non-ISR organizations) are available to provide medical care to burned soldiers during ongoing combat. Increased medical personnel availability can expand the scope of more complex burn care to advance the medical role for burned combatants.

[0150] To begin to address this need, we developed a novel cart-based imaging device that uses multispectral imaging (MSI) and artificial intelligence (AI) algorithms to aid in the preoperative assessment of burn healability. The device can acquire images of a large area of ​​tissue (e.g., 5.9 x 7.9 in2) in a short time (e.g., within 6 seconds, 5 seconds, 4 seconds, 3 seconds, 2 seconds, or 1 second) and does not require the injection of imaging contrast agents. In this study of the general public, the accuracy rate of the device in assessing burn healability was shown to be superior to clinical judgment by burn specialists (e.g., 70-80%).

[0151] method: Civilian patients with burns of various severities were imaged within 72 hours of burn injury and at several time points up to 7 days after burn injury. The exact burn severity in each image was determined using a 3-week healing assessment or punch biopsy. The accuracy of the imaging device in identifying and discriminating between healing and non-healing burn tissue in first-, second-, and third-degree burns was analyzed on a pixel-by-pixel basis.

[0152] result: Data was collected from 38 civilian patients, totaling 58 burns and 393 images. The AI ​​algorithm achieved a sensitivity of 87.5% and a specificity of 90.7% in predicting non-healing burn tissue.

[0153] Conclusion: The accuracy rate of the novel imaging device and its AI algorithm in determining the healability of burns was shown to be superior to that of a burn specialist's clinical judgment. Future studies will focus on redesigning the device to make it portable and evaluating its use in an intraoperative setting. Design modifications to make the device portable include reducing the size of the device to that of a portable system, increasing the field of view, shortening the acquisition time for a single snapshot, and evaluating the device in an intraoperative setting using a porcine model. These developments are being performed using a benchtop multispectral imaging (MSI) subsystem that shows comparable capabilities in basic imaging studies.

[0154] Additional light source for image alignment In various embodiments, one or more additional light sources may be used in conjunction with any of the embodiments disclosed herein to improve the accuracy of image alignment. FIG. 21 illustrates an example embodiment of a multi-aperture spectral imager 2100 including a projector 2105. In some embodiments, the projector 2105 or other suitable light source may be, for example, one of the light sources 1165 described above with respect to FIG. 12. In embodiments including an additional light source such as the projector 2105 for alignment, the method may further include additional exposures. The additional light source such as the projector 2105 may project one or more dots, stripes, grids, random spots, or other suitable spatial patterns in a single spectral band, multiple spectral bands, or broadbands, individually or cumulatively visible through all cameras of the imager 2100, into the field of view of the imager 2100. For example, the projector 2105 may project shared or common channel light, broadband illumination, or cumulatively visible illumination that may be used to verify the accuracy of image alignment calculated based on the common band approach described above. As used herein, "cumulatively visible illumination" refers to a selected plurality of wavelengths whose pattern is transformed by each image sensor of a multispectral imaging system. For example, the cumulatively visible illumination may include a plurality of wavelengths, where at least one of the plurality of wavelengths is transformed by each channel, even if the plurality of wavelengths does not include a wavelength common to all channels. In some embodiments, the type of pattern projected by projector 2105 may be selected based on the number of apertures on which the pattern is imaged. For example, if the pattern is only visible through one aperture, it may be preferable for the pattern to be relatively dense (e.g., may have a relatively narrow autocorrelation, such as about 1-10 pixels, about 20 pixels, less than about 50 pixels, less than about 100 pixels, etc.), whereas if the pattern is imaged through multiple apertures, a less dense pattern or a pattern with a less narrow autocorrelation may be useful.In some embodiments, the additional exposure taken with the projected spatial pattern is included in the parallax calculation with the intention of improving the accuracy of the alignment over embodiments in which the exposure is taken without the projected spatial pattern. In some embodiments, the additional light source can project a stripe pattern of a single spectral band, multiple spectral bands, or broadband (e.g., shared or common channel, or broadband illumination, etc.) that is individually or cumulatively visible through all cameras into the field of view of the imager, and use this to improve the alignment of the images based on the phase of the stripe pattern. In some embodiments, the additional light source can project a plurality of unique spatial arrangements of dots, grids, and / or specks of a single spectral band, multiple spectral bands, or broadband (e.g., shared or common channel, or broadband illumination, etc.) that is individually or cumulatively visible through all cameras into the field of view of the imager, and use this to improve the alignment of the images. In some embodiments, the method further includes an additional sensor with an aperture or multiple apertures, which can detect the shape of a single or multiple objects in the field of view. For example, the additional sensor may use LIDAR, light field, or ultrasound technology to further improve the accuracy of image alignment using the co-band approach described above. The additional sensor may be a single aperture or multi-aperture sensor capable of sensing light field information, or may be capable of sensing other signals such as ultrasound or pulsed laser.

[0155] Tissue topological feature assessment using computer vision methods Topological characterization and / or 3D imaging of tissue structures can provide significant advantages in health care and other contexts. For example, wound measurement can be an important aspect in wound care and is used for a variety of purposes in health care applications. Wound area is used to estimate patient outcomes, triage treatment decisions, and estimate resources required to provide care. For example, wound area of ​​approximately 10 cm 2 Small third-degree burns (e.g., 1,000 cm2) can be treated with standard wound care, depending on their location. 2 ), are typically treated with surgical excision and grafting. The area of ​​the burn may be used to estimate the duration of surgical treatment, the amount of tissue harvested for grafting, and the cost to the surgeon for the medical procedure. In the treatment of diabetic foot ulcers, the wound area and volume are used to track the progression of the ulcer throughout the treatment process. In addition, the geometric contour of the wound is an important indicator of severity and may predict the rate of healing. For example, the pockets of the wound edge of each ulcer are identified and measured to determine the overall extent of the wound into the tissue. Areas with extensive pocketing indicate severe ulcers that will be more difficult to heal. The contour of the wound edge is also important in the proper management of diabetic foot ulcers. Typically, physicians use scalpels or other sharp instruments in a technique called debridement to remove unhealthy tissue in and around chronic wounds such as diabetic foot ulcers. One goal of debridement is to encourage migration of normal cells into the wound by, for example, removing the wound contours with steep edges from the wound margins by cutting, planing, or curettage, preferably so that the wound approaches a flat or nearly flat surface or is flush with the skin.

[0156] Wound area measurements are routinely performed in clinical settings using rulers and probes. These instruments are used to measure wound length (l), width (w), depth (d), pockets, and / or tunnels. From these basic measurements, wound geometry can be estimated. For example, wound area is typically estimated by modeling the wound as a rectangle with area = l × w. Analogously, volume can be estimated by modeling the wound as a cuboid with volume = l × w × d. It can also be modeled as an ellipsoid with volume = w × l × d × (π / 6). However, wounds do not follow these simple geometric models, and therefore currently used techniques to estimate wound area and volume are inaccurate and prone to error.

[0157] To address these concerns, a variety of tools have been introduced to measure wound area. These include calibrating a photograph by placing a ruler in the frame of the image, or filling the wound with putty or paste to create a wound cast or cast. Annotations such as lines and areas of the calibrated image, created by the user in a software program, can be used to draw on the image to obtain more accurate measurements of length, width, and area. However, depth cannot be easily estimated using this method. Although wound casting is an accurate method to measure wound volume and depth, it cannot be used in a clinical setting due to the risk of contamination or mechanical disruption of the delicate tissue of the healing wound.

[0158] Devices have been introduced to simplify the process and improve these measurement techniques. One device projects a laser pattern onto the tissue surface to estimate tissue measurements and estimate depth. Another uses a monochromatic projector to project a structured light pattern onto the tissue. Another method uses 3D imaging to construct a 3D surface from images captured by a calibrated camera or camera array. A variety of other techniques, such as LiDAR, can be used to estimate real-world 3D geometry from optical sensors, which can also be applied to improve wound measurement and geometry. For example, LiDAR is being investigated for tumor detection in tissue.

[0159] It is important to monitor changes in the physical dimensions of the wound over time. This provides information about wound healing. For example, a typical method of assessing wound healing is to measure the percent area reduction (PAR). PAR is the current wound area divided by the wound area at a pre-defined baseline multiplied by 100%. A PAR of 50% or greater 30 days after treatment is considered to be a well-proportioned healed wound.

[0160] The 3D models generated by the technology of the present invention can be used to improve the rate of change of wounds. For example, the percent volume loss (PVR) can be reported by comparing current 3D models with previous 3D models. Additionally, absolute changes in volume can be better measured using this technology compared to methods using rulers and probes.

[0161] A system and method for tissue topological assessment is described for use in patient management and to guide wound debridement or excision. The method uses one or more images from the tissue of interest to reconstruct the 3D geometry of the tissue in a real-world coordinate system using calibrated physical units. Techniques are described for single and multi-imaging systems. A method for obtaining local gradients is presented. The local gradients can be used to investigate the surface geometry of tissue regions and prompt the healthcare provider to adjust treatment protocols.

[0162] In a single imaging system, multiple target images (e.g., video or burst captures) can be acquired and aligned to estimate a 3D model of the target, e.g., as shown in Figures 22A and 22B. First, a set of unique, static image features in each image is identified and matched according to some photometric heuristics for each feature. These matches are used to obtain the relative camera pose, given pre-calibrated camera lens parameters or dynamically estimated from the matched features. During the pose estimation stage, outlier rejection with the M-estimator Sample Consensus (MSAC) algorithm can be used to eliminate erroneous image feature matches.

[0163] Following sparse image localization relative to the object, our method performs dense multi-view stereo reconstruction whereby image pairs are selected, stereo rectified, and disparity matches are back-projected onto an integrated 3D model of the object. This approach is illustrated in Figures 23A, 23B, and 23C, which show images of an integrated 3D model of a prosthetic leg with surface color and texture variations. The polygons in the images of Figures 23B and 23C show the ligation of surface annotated regions in an example visualization technique according to our technology. One limitation of this single imager approach is the need for markers with known dimensions to calibrate the 3D geometry to "real world" units.

[0164] In some embodiments, two or more cameras can be used for topological characterization of tissue. A set of images of a tissue region can be collected from each of at least two image collection devices (e.g., using any of the multi-aperture imaging systems disclosed herein). All of the images can be acquired simultaneously or sequentially and used to estimate a 3D model representation of the tissue. With two or more image collection devices, the method of the present invention uses geometric constraints to reduce the search space to match every pixel in one image with a pixel in an image from another device. Given the mechanical spacing between the devices, these correspondences can be triangulated in 3D space and such a model representation can be created. When two or more devices move together to capture multiple frames, the relative motion with respect to the tissue can be estimated and the sub-model estimated for each position can be used to align the 3D projection.

[0165] FIG. 24 shows an example of the relationship between pixel disparity and depth or distance information in a multi-camera imaging system. The relationship between pixel disparity and depth or distance can be defined based on the camera focal length and the baseline distance of the optical centers of the imaging sensors. Thus, the depth or distance value of an individual pixel can be equal to the focal length of the camera multiplied by the baseline distance divided by the pixel disparity value determined for the pixel. In one example of a desired implementation, the baseline distance is 55.837 cm, combining a focal length of 6 mm and a pixel pitch of 3.45 μm, resulting in a total of 1.74×10 3 Pixels corresponding to

[0166] Referring to FIG. 25, multiple cameras can simultaneously capture image features x i ` features are visible. Assuming that the camera is ideal or has been previously calibrated for intrinsic and extrinsic factors (K, [R|t]), the observation D i , the focal length of the camera f, and the baseline distance, and we can find such features X i The real-world 3D coordinates of the object can be estimated.

[0167] Figures 26A-26D show an example of a process for generating a 3D tissue model based on multiple spatially offset 2D images. As shown in Figure 26A, RAW images can be captured by a calibrated camera system.

[0168] 26B, the images may be rectified to eliminate any optical distortions and / or compensate for manufacturing defects. For example, the images may be undistorted and / or rotated. In some embodiments, the rectification may involve epipolar alignment of the images.

[0169] With reference to FIG. 26C, a dense multi-view matching process uses two or more rectified frames to estimate pixel-by-pixel disparity. In some embodiments, a horopter shift can be performed before determining pixel disparity to reduce computational and / or memory requirements. Disparity estimation can include one-dimensional pixel-by-pixel patch-based similarity estimation using normalized cross-correlation (e.g., by matching regions bounded by epipolar lines) and / or Hamming distance from CENSUS transform cost to estimate the best disparity cost for the selected view. In some embodiments, the system can select the minimum fusion cost to select disparities that match all views and the constraint geometry. In some embodiments, a mask can be used to match only pixels that are considered to be tissues of interest, as described below for image segmentation. The pixel-by-pixel disparity is then mapped to depth based on known external factors such as baseline distance to generate a depth map. In some embodiments, the disparity estimate can be filtered to improve the quality of the local surface reconstruction. This can be performed on the original disparity map, or on the depth map, or on the 3D projected points. For 3D point clouds, the per-pixel disparity can be extracted using Delaunay triangulation, which fits a surface to the 3D point cloud.

[0170] With reference to FIG. 26D, a camera projection model can be used to project the depth map into 3D space. For example, the depth map can be projected into 3D space using the mathematical relationships of depth, focal length, baseline distance, and disparity described above. This projection generates a 3D point cloud, which in some embodiments can be displayed as a mesh on a surface. In some embodiments, the inverse back projection can be used to retexture the 3D model to output a multispectral 3D model. Additionally, in embodiments where more than two image sensors are used, a pixel-by-pixel confidence value can be calculated for each pixel. A visualization such as a confidence map can be generated based on the determined confidence values. In some embodiments, pixels can be rejected from the 3D model based on, for example, pixels exceeding a confidence margin threshold.

[0171] The integrated 3D model can represent tissue in multiple interoperable forms, including but not limited to 3D point clouds, 3D textured meshes, 3D parametric surfaces, 3D voxel grids, and / or depth heat maps. Such model representations can further be associated with statistical accuracy metrics, thereby integrating model consistency across all view observations.

[0172] The 3D tissue model may be calibrated to real-world units. Samples of points in the 3D model are correlated to physical units of tissue in the real world, such that two points with a distance of 1 mm in the 3D parametric surface will show the same distance of 1 mm in real-world tissue. By using real-world units (e.g., millimeters), derived analyses (e.g., measuring the area of ​​a burn) can be performed on the device. The user or an AI algorithm may select any area of ​​tissue and calculate the area of ​​the tissue model from this area.

[0173] The camera can be pre-calibrated using multi-view geometric methods to obtain accurate real-world, scaled, geometry from images alone (e.g., no calibration object of known geometry is present in the images). The calibration process can be subdivided into internal and external components. If the system includes two or more image acquisition devices, a standardized calibration target can be performed in the calibration operation, as shown in FIG. 27A, which contains identifiable and unique features such as keypoints. A set of images can be collected by one or more image acquisition devices and aggregated into a graph of feature correspondence sets, as shown in FIG. 27B. Parameters and device pose can be estimated and optimized simultaneously to evaluate the camera model.

[0174] In some embodiments, as shown in FIG. 27C, images of the calibration target may be acquired by a multi-imaging system, with the system moving during image capture to bring the object into the field of view of all cameras. In some embodiments, a relatively large number of viewpoints may be used to provide optimal calibration. For example, images from multiple imaging devices may be acquired from 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, or more individual viewpoints. Features of the calibration target, such as dots with known geometric dimensions, may be detected in all of the acquired calibration images, with sub-pixel accuracy, providing improved calibration performance. As an example shown in FIG. 27A, in some embodiments, the calibration target includes a set of black and white dots to reliably locate dot centers used in the calibration operation, and the dots may be extracted by performing Laplacian of Gaussian (LoG) second derivative gradient filtering, least squares ellipse fitting based on contours, and target ordering based on black dot patterns. Internal factors, such as the optical performance of individual imaging sensors and / or lenses, may be estimated based on the individual images. Extrinsic factors including baseline distance and optical axis alignment can be further determined. The overall calibration result can be verified by comparing the intrinsic factor consistency between cameras, the similarity of the extrinsic factors determined by the manufacturing specifications of the multi-imager device, and the calibration reprojection error.

[0175] The stability of the calibration can also be tested or monitored using intrinsic and extrinsic factors. Since the intrinsic and extrinsic factors are related to the position of each camera part, if there is any movement of the camera parts (e.g., rotation or movement beyond the mechanical tolerance of the camera), these parameters may indicate that the movement has occurred. The reprojection error, e.g., the pixel-to-pixel distance difference between the keypoints detected in the calibrated image and the corresponding world points projected onto the same image, may be useful in determining the stability of the camera calibration. The change in the reprojection error over time may reflect the quality and reusability of the initial calibration output. The period of the stability test may be between a set of test events (e.g., mechanical shock test, thermal shock test, etc.) to simulate standard or extreme use cases of the camera, in one example.

[0176] 3D models and images from different positions or imaging devices can be related through a camera projection model, which consists of lens parameters (camera intrinsic parameters) and a 3D rigid transformation (extrinsic parameters), respectively. The resulting 3D model can then be backprojected using such a model to some arbitrary position in the 2D image space defined by a virtual camera (which can be the same position as the physical camera) or to a synthesized ortho-plane.

[0177] Thus, the systems and methods of the present disclosure, calibrated using the calibration process described herein, can provide one or more measurements (e.g., mm, mm 2, mm 3, mm 4, mm 5, mm 6, mm 7, mm 8, mm 9, mm 10, mm 11, mm 12, mm 13, mm 14, mm 15, mm 16, mm 17, mm 18, mm 19, mm 20, mm 21, mm 22, mm 23, mm 24, mm 25, mm 26, mm 27, mm 30, mm 31, mm 32, mm 33, mm 34, mm 35, mm 36, mm 37, mm 38, mm 39, mm 40, mm 41, mm 42, mm 43, mm 44, mm 45, mm 46, mm 47, mm 48, mm 49, mm 50, mm 51, mm 52, mm 53, mm 54, mm 55, mm 56, mm 57, mm 58, mm 59, mm 60, mm 61, mm 62, mm 63, mm 64, mm 65, mm 66, mm 67, mm 68, mm 69, mm 70, mm 71, mm 72, mm 73, mm 74, mm 75, mm 76, mm 77, mm 78, mm 79, mm 80, mm 81, mm 82, mm 83, mm 84, mm 85, mm 85, mm 86, mm 87, mm 88, mm 89, mm 90, mm 90, mm 91, mm 92, mm 93, mm 94, mm 95, mm 96, mm 97, mm 98 2 , mm 3That is, a calibrated imaging device can take a set of images of a feature or tissue region and determine one or more physical measurements of the feature or tissue region based on the images and depth information derived from the images, rather than based on some type of marker (such as a reference object having a known length or width) shown (or not) in the images.

[0178] Refinement of the calibration or recalibration can be completed while the tissue sample is being imaged. To improve such calibration and the resulting 3D model estimation, an object with known dimensions can be included in the image set.

[0179] As described elsewhere herein, the system may also include a device for illuminating the tissue, such as an LED or LED array, to improve imaging integrity.

[0180] Segmentation of organizations and organizational areas To improve the generation of the 3D surface, the tissue in each image in the image set can be further segmented from the background. An example of segmentation is shown in Figures 28A and 28B. Figure 28A shows an image of a burn wound, and Figure 28B shows a segmentation of the image in Figure 28A. In Figure 28B, the white areas correspond to areas of tissue in the image, and the black areas correspond to background pixels. The segmentation can be obtained manually from a person drawing the image, in a semi-automated manner, or in a fully automated manner. Semi-automated methods include graph cuts. Fully automated methods may include simple techniques such as applying a threshold to color values ​​in the pixels, or channel rates. Automated methods may include supervised and unsupervised machine learning and deep learning methods, such as using convolutional neural networks.

[0181] In some embodiments, the generation of 3D surfaces in the use of tissue segmentation can be improved by eliminating background regions of images when performing the stereo correspondence determination process between two or more imaging devices. Reducing the search space can improve several aspects of stereo correspondence. First, removing the background can reduce the probability of incorrect stereo matching points. Also, the number of operations required to complete the stereo correspondence calculation can be reduced because less image area is used to determine the correspondence.

[0182] Image segmentation can process images from multiple image acquisition devices and aggregate them into a unified 3D model. The overlapping segmentation regions in 3D space can be used to calculate the final boundary region of the 3D model. Examples of image segmentation algorithms are described below.

[0183] U-Net is an encoder-decoder deep learning semantic segmentation method that can be trained with a very small number of training images. The U-Net algorithm uses the concept of skip connections to preserve high-resolution features and improve localization.

[0184] Unet++ is a supervised deep learning encoder-decoder network that connects encoder and decoder sub-networks through a series of nested dense skip pathways. The encoder extracts features with different spatial resolutions, which the decoder can use to define an accurate segmentation mask.

[0185] LinkNet is a fully convolutional neural network for image segmentation. LinkNet bypasses spatial information from the encoder to the corresponding decoder, improving accuracy and significantly reducing processing time. LinkNet can efficiently use scarce resources available on embedded platforms.

[0186] PSPNet is a fully convolutional neural network for image segmentation that consists of an encoder and a spatial pyramid that does not use fine features in the decoder. PSPNet can be used for multi-class segmentation of high-resolution images.

[0187] DeepLabV3+ combines a spatial pyramid pooling module with an encoder-decoder type structure for image semantic segmentation. It also adds a simple but effective decoder module to improve the accuracy of the segmentation results, especially at object boundaries. Depthwise separable convolutions are applied to both the dilated spatial pyramid pooling and decoder modules.

[0188] The performance of the above listed algorithms is compared according to the tissue image segmentation of the present technique as shown in Table 1 below. [Table 1]

[0189] As shown in Table 1 above, Unet++ achieved the highest accuracy rate in the test setting. The results of different weightings in the Unet++ algorithm are shown in Table 2 below. [Table 2]

[0190] The variable data expansion results for the Unet++ algorithm are shown in Table 3 below. [Table 3]

[0191] The results for different input dataset sizes are shown in Table 4 below. [Table 4]

[0192] Multispectral 3D Imaging In some embodiments, it is advantageous to obtain multispectral information and combine it with the 3D image data. A variety of 3D images can be generated in this manner, with each point represented in more than one spectral channel. In one example, a multispectral imaging device can be used as the imager in the system. In this way, the 3D reconstruction includes the multispectral data that was originally included in the image set.

[0193] In another example, each imager in an array of two or more imagers can have a different spectral response (e.g., each camera is sensitive to a different or multiple parts of the UV / visible / IR spectrum). From at least one pair of images taken by these imagers, the process of geometric internal calibration, external calibration, rectification, stereo correspondence, and 3D geometry can be obtained. Stereo correspondence can be obtained using one channel from each of the two or more imagers, combining all channels of each imager, or using a singular subset of channels. Once stereo correspondence is obtained, a 3D geometry is obtained, where each point in 3D space contains all channels of the two or more imagers used in the system. In this way, more spectral channels can be combined beyond what can be achieved with only one imager.

[0194] One way to improve the computation of the stereo correspondence process is to have the two imagers have at least one shared channel (e.g., one channel that shares the same spectral response). For example, each of the two imagers (e.g., each imager corresponds to a combination of an image sensor and corresponding aperture as described elsewhere herein) can be configured to have multiple bandpass filters configured to pass the shared frequency bands common to the two imagers and at least one unique frequency band that is not captured by the other imager.

[0195] Wound Edge Assessment The geometry of the wound edge is an important factor in predicting the wound's ability to heal. A key step in wound healing is the re-epithelialization of the wound area. This process occurs when basal keratinocytes migrate from the wound edge to the wound surface and undergo cell division. Basal keratinocytes are the type of cell that make up the majority of the epidermis. Once in the wound, they first promote re-epithelialization by secreting a basement membrane, an extracellular matrix scaffold. These new cells then keratinize and form a waterproof confluent layer over a period of approximately 2-3 weeks.

[0196] In wound care, much attention is paid to the formation of the wound environment because it supports this re-epithelialization. One important aspect of wound environment formation is the formation of the wound edge. For example, wound edges that are flat and flush with the surface of the wound environment promote the migration of cells necessary for re-epithelialization. However, wound edges that are rolled (e.g., epiboly) or angled away from the surface of the wound environment hinder cell migration. Wound edges that are away from the wound environment are referred to as "pockets." Wound edge pockets hinder or inhibit re-epithelialization until the pocket area fills with vascularized tissue called granulation tissue and the wound edge is again flat or nearly flush with the wound surface to support sufficient cell migration.

[0197] In wound management, clinicians can manually smooth the edges of a wound to promote cell migration. Smoothing of the wound edge is traditionally done by cutting or scraping the wound edge with a scalpel or brush to an extent that the clinician visually determines that the wound edge is flat or flush against the skin surface at the wound edge. This procedure is called debridement. The purpose of debridement is to smooth the wound and remove epiboly, callus, senescent cells, or excess granulation tissue. Wound edge preparation has traditionally been done by clinicians based on visual information alone and on the clinician's skill, experience, and judgment.

[0198] To improve wound healing and refine wound edge treatment, the 3D topological map generated by the technique of the present invention can be used to assess wound edge geometry before, during, and after debridement. Based on the assessed wound edge geometry, the clinician can be aware of any undesirable edge features and can use the 3D topological map to accurately debride the wound, monitor the debridement during the procedure, and assess the efficiency of the debridement (e.g., wound margins and efficiency of wound contour flattening to reach a flat geometry). One way to achieve this is to calculate the inclination of the wound edge relative to the wound surface, as shown in Figures 29A-29D.

[0199] FIG 29A shows an example of a 2D cross-section of a wound obtained from 3D image data of the wound as described herein. FIG 29B shows a set of normal vectors to the tissue surface calculated at discrete points along the tissue contour. In some embodiments, the normal vectors can be scattered and analyzed along a one-dimensional continuum to determine the slope of the wound surface, as conceptually shown in FIG 29C. JPEG2025504857000007.jpg12130 can be calculated as shown in Figure 29D. Slopes greater than a threshold can be used to alert the clinician to undesirable edges that require careful or additional debridement. Also, vectors normal to the surface can be identified and these surface normal vectors can be compared in neighborhoods to obtain the gradient.

[0200] Example of a wound progression model In some methods of the present technology, the wound healing process can be innovatively evaluated through a set of photometric, spectroscopic, and geometric observables. Figure 30 shows a timeline of diagnosis and treatment, together with a wound progression model along the timeline. This model can be gradually expanded by recording the patient's wound over time with the above-mentioned technology and devices. Such a model can provide feedback for necessary adjustments to the treatment plan, and the model can predict the healing rate for the future (e.g., predicting the healing rate of similar wounds in other patients, which may have similar health care indicators such as peripheral vascular disease, diabetes, insulin resistance, or obesity).

[0201] As shown in Figure 31, the wound progression model can be represented by a Hidden Markov Model (HMM), where a set of measurable observables collected through an imaging device represents the unobservable wound state. For example, the subcutaneous infection state of the wound is not directly known but can be inferred from the model through current and past imaging observations. The model can connect between some or all observed time steps. Furthermore, edges can be dynamically adjusted as new data becomes available to better model the evolution of the wound. Examples of causal and observable relationships between some proposed observables and wound states measured manually, semi-automatically or fully automatically by external techniques or the imaging methods mentioned above are shown in Figure 31.

[0202] JPEG2025504857000008.jpg60155

[0203] This is particularly useful for the purposes of generating reports for billing to patients or their insurance providers. For example, US physicians are paid differently for treating minor burns than for treating major burns (e.g., CPT code 16025 is for moderate burns to the entire face or entire limbs, or 5%-10% of total body surface area, and CPT code 16030 is for major burns to 2 or more limbs, or >10% of total body surface area). In another example, a machine can be used to measure the burn area in cm 2In this example, CP15002 can be used to measure the first 100 cm of body area in infants and children with burn excision and skin grafting, and then the appropriate billing code can be recommended for the subsequent skin grafting procedure. 2 Or create a burn area up to 1%. Next, 100cm 2 Additional CPT codes, +15003CPT and +15005CPT, apply for additional areas in increments of +15003CPT and +15005CPT.

[0204] Example of 3D characterization of a diabetic foot ulcer 32A-32E show an example of 3D characterization of a diabetic foot ulcer according to the techniques of the present invention. FIG. 32A is a photograph of a leg with a diabetic foot ulcer with a dashed line surrounding a region of interest. FIG. 32B shows a 2D cross-section showing the topology of the diabetic foot ulcer along a line through the region of interest. FIG. 32C shows a visual display of 3D multispectral image data showing the region of interest in the image of FIG. 32A. FIG. 32D shows a heatmap showing pixel-by-pixel depth information corresponding to the region of interest in the image of FIG. 32A. FIG. 32E shows an example of a retextured multispectral showing the estimated 3D surface of the tissue in the region of interest. In the retextured image of FIG. 32E, the pixels of the original image are aligned to the generated 3D model using the calculated geometry of the diabetic foot ulcer.

[0205] Examples of applications for classification, detection, and / or diagnosis of cancer lesions The use of the 3D geometry and tissue topology described herein, in combination with or without reflectance measurements, can be applied to the detection, classification, and / or diagnosis of cancerous tissue. For example, in melanoma, a physician makes an initial assessment of the probability that a lesion or lentigo is cancerous or will transform into cancerous tissue. Typically, this initial assessment is based on the clinician's judgment on visually inspecting the lesion using a small instrument called a dermatoscope. Dermatoscope assessment has been shown to be only 65% ​​accurate and depends on the experience of the clinician. Features of concern in the detection of melanoma are asymmetry, borders, color, diameter, and variation. These features are known as the "ABCDE rules of melanoma."

[0206] Different types of melanoma look different. General superficial melanoma has a brown and black pigmented appearance that spreads from the lentigo. Over time, it grows slowly outward before invading the deeper layers of the skin. Nodular melanoma grows vertically as a dome-shaped bump and is blue to black in color. Lentigo maligna melanoma has a large, yellow-brown pigmented appearance with irregular borders that flatten and later become raised. Acral lentigo melanoma appears on the palms, soles, knuckles, elbows, and knees. Subungual melanoma is a subtype of acral lentigo melanoma and occurs in the fingernails and toenails. This melanoma has a bruise-like appearance or, rarely, is pigmented (amelanotic or apigmented). Amelanotic melanoma lacks pigment and looks like a reddish patch or a skin-colored lump. It is round or ovoid with regular borders.

[0207] Computational methods for the evaluation of the ABCDE rule have been developed. These methods are applied to 2D optical images obtained from imaging of skin lesions. A method to generate a 3D mesh of the lentigo topology was applied to further refine the quantification of the ABCDE rule parameters for lentigines suspicious for malignant melanoma.

[0208] The desirable features of the method described in this application are twofold. First, generating a 3D image of a lesion, mole, discolored skin, or wound essentially involves mapping the reflectance (color) of the tissue to each point in a 3D mesh, for example as shown in FIG. 33. Using the generated 3D image also allows for homogenization of the reflectance values, which may reduce the effect of the inverse square on the sensed light intensity, allowing for better wound assessment. Previous methods required colocalization of the 2D color image to a 3D tissue map, resulting in mapping errors and giving only an estimate of the color at each point in 3D space. Second, the method described herein allows for the generation of 3D geometry at a scale of size determined by the optics of the lens. Thus, if the lesion has sufficient resolution determined by the lens parameters of the sensor or sensor array, a microscopic image of the lesion morphology can be obtained in 3D.

[0209] term All of the methods and tasks described herein may be performed by a computer system and may be fully automated. In some cases, the computer system includes multiple separate computers or computing devices (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.) that communicate and interoperate over a network to perform the functions described herein. Any such computing device typically includes a processor (or multiple processors) that executes program instructions or program modules stored in memory or other non-transitory computer-readable storage media or non-transitory computer-readable storage devices (e.g., solid-state storage devices, disk drives, etc.). Various functions disclosed herein may be embodied in such program instructions or may be implemented in the form of application-specific circuitry (e.g., ASICs or FPGAs) in the computer system. When a computer system includes multiple computing devices, these devices may be located in the same location or in different locations. Results of the methods and tasks disclosed herein may be persistently stored by converting physical storage devices, such as solid-state memory chips or magnetic disks, into various forms. In some embodiments, the computer system may be a cloud-based computing system in which processing resources are shared by multiple separate business entities or other users.

[0210] The processes disclosed herein may be initiated in response to an event, such as a predefined or dynamically determined schedule, upon request initiated by a user or system administrator, or in response to some other event. Once the processes are initiated, executable program instructions stored on one or more non-transitory computer-readable media (e.g., hard drives, flash memory, removable media, etc.) may be loaded into memory (e.g., RAM) of a server or other computing device. The executable instructions may then be executed by a hardware-based computer processor included in the computing device. In some embodiments, the processes disclosed herein, or portions thereof, may be implemented on multiple computing devices and / or multiple processors, either serially or in parallel.

[0211] Depending on the embodiment, certain acts, events or functions of any of the processes or algorithms described herein may be performed in a different order, may be added to one another, may be combined with one another, or may be omitted altogether (e.g., not all of the acts and events described herein are required to execute an algorithm described herein). Furthermore, in certain embodiments, multiple acts or events may be performed simultaneously rather than sequentially, for example, using multithreaded processing, interrupt processing, or multiple processors or multiple processor cores, or on other parallel structures.

[0212] The various exemplary logic blocks, modules, routines, and algorithmic steps described in connection with the embodiments disclosed herein may be implemented as electronic devices (e.g., ASIC or FPGA devices), computer software running on computer hardware, or a combination thereof. Additionally, the various exemplary logic blocks and modules described in connection with the embodiments disclosed herein may be implemented by devices such as processor devices, digital signal processors ("DSPs"), application specific integrated circuits ("ASICs"), field programmable gate arrays ("FPGAs") or other programmable logic devices, discrete gate logic or transfer logic, discrete hardware components, or combinations of these components designed to perform the functions described herein. The processor device may be a microprocessor, but in other aspects, the processor device may be a controller, a microcontroller, or a state machine, combinations thereof, and the like. The processor device may include electrical circuitry configured to process computer-executable instructions. In another embodiment, the processor device includes an FPGA or other programmable device that performs logical operations without processing computer-executable instructions. The processor unit may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of a DSP core and one or more microprocessors, or other such configurations. Although described herein primarily with respect to digital technology, the processor unit may include primarily analog components. For example, all or some of the rendering techniques described herein may be implemented using analog circuitry, or a combination of analog and digital circuitry.The computing environment can include any type of computer system, including, but not limited to, microprocessor-based computer systems, mainframe computers, digital signal processors, portable computing devices, device controllers, computer engines within appliances, to name a few.

[0213] The components of the methods, processes, routines or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, directly in software modules executed by a processor device, or directly in a combination of the hardware and software modules. The software modules may be included in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or other forms of non-transitory computer-readable storage media. A typical storage medium may be coupled to the processor device such that the processor device can read information from and write information to such storage medium. In this aspect, the storage medium may be integral to the processor device. The processor device and the storage medium may be included in an ASIC. The ASIC may be included in a user terminal. In this aspect, the processor device and the storage medium may be present as discrete components of the user terminal.

[0214] As used herein, conditional terms such as "can," "may," "potential," "may," "for example," and the like, generally mean that a particular embodiment includes a particular feature, component, or step, and another embodiment does not include the feature, component, or step, unless otherwise stated or interpreted otherwise in the context in which the terms are used. Thus, such conditional terms generally do not imply that one or more embodiments in any way require the feature, component, or step, or that one or more embodiments necessarily include decision logic, regardless of whether a particular feature, component, or step is included or performed in a particular embodiment, in the presence or absence of other inputs or promptings. Additionally, terms such as "including," "comprising," "having," and the like, are synonymous and are used in an inclusive sense in an open-ended manner, and do not exclude additional components, features, acts, operations, and the like. Additionally, the term "or" is also used in an inclusive sense (rather than an exclusive sense), e.g., when the term "or" is used to list components, it means one, some, or all of the listed components.

[0215] Unless otherwise indicated, it is understood that disjunctive language such as "at least one of X, Y, or Z" is generally used herein to indicate that a particular item, term, etc. may be X, Y, or Z, or any combination thereof (e.g., X, Y, or Z). Thus, such disjunctive language is generally not intended, and should not be intended, to imply that in a particular embodiment, the presence of at least one of X, at least one of Y, and at least one of Z is required.

[0216] While novel features applied in various embodiments have been shown, described and pointed out in the foregoing detailed description, it will be understood that the form and details of the foregoing apparatus or algorithms may be variously omitted, substituted and modified without departing from the scope of the present disclosure. Since some of the features described herein may be used or practiced separately from other features, it will be readily understood that certain embodiments described herein may be embodied in forms that do not provide all of the features and advantages described herein. All changes that come within the meaning and range of equivalency of the claims are encompassed within the scope of these claims.

Claims

1. 1. An imaging system comprising: a plurality of image sensors configured to receive light reflected by the tissue region in at least a first predetermined frequency band; a memory in which computer executable instructions are stored; one or more processors, the one or more processors configured by the instructions to perform at least Each of the at least two image sensors captures an image of the tissue region; identifying corresponding sets of pixels in the captured images, each set including pixels of a first image captured by a first image sensor of the plurality of image sensors and pixels of a second image captured by a second image sensor of the plurality of image sensors; determining pixel disparities for pixels of the corresponding set of pixels, preferably pixel disparities or individual pixel disparities; determining distance values ​​associated with respective sets of corresponding pixels based at least in part on the pixel disparity and a baseline distance between the first image sensor and the second image sensor; The imaging system generates a three-dimensional (3D) model of the tissue region based at least in part on the distance values.

2. The imaging system of claim 1 , wherein the 3D model of the tissue region comprises at least one of a 3D point cloud, a 3D textured mesh, a 3D parametric surface, or a 3D voxel grid.

3. 3. The imaging system of claim 1 or 2, wherein the one or more processors are configured to determine, in accordance with instructions, one or more measurements of at least a portion of the tissue region further based at least in part on a 3D model and a pre-calibration of the imaging system.

4. The imaging system of claim 3 , wherein the one or more measurements include at least one of a length measurement, an area measurement, and a volume measurement of a portion of a tissue region.

5. The imaging system of claim 3 or 4, wherein the one or more measurements include a cross-sectional profile of a portion of a tissue region.

6. The imaging system of any one of claims 3 to 5, wherein the pre-calibration is based on imaging a standardized calibration target with the imaging system.

7. The imaging system of any one of claims 3 to 6, wherein the one or more measurements are not determined based on a marker contained within a field of view of the imaging system.

8. The imaging system of any one of claims 3 to 7, wherein the one or more processors are configured, in accordance with instructions, to perform one or more further diagnostic analyses based at least in part on the one or more measurements.

9. The imaging system of any one of claims 3 to 8, wherein the one or more processors are further configured in accordance with instructions to store a medical billing code identified based at least in part on the one or more measurements.

10. 10. The imaging system of claim 1, wherein the one or more processors are further configured to segment the captured image prior to identifying the corresponding set of pixels according to the instructions.

11. The imaging system of claim 10 , wherein the segmentation comprises identifying at least background pixels and tissue region pixels.

12. The imaging system of claim 11 , wherein the background pixels are excluded from identifying a set of corresponding pixels.

13. The imaging system of any one of claims 1 to 12, further comprising at least one light source configured to illuminate the tissue region with light in at least the first predetermined frequency band.

14. 14. The imaging system of claim 1, wherein each image sensor of the plurality of image sensors is configured to image the tissue region in at least the first predetermined frequency band and a second frequency band different from a second frequency band of at least one other image sensor of the plurality of image sensors.

15. The imaging system of claim 14 , wherein the one or more processors are configured in accordance with instructions to obtain multispectral image data further related to the tissue region based on the captured image.

16. 16. The imaging system of claim 15, wherein the one or more processors are configured in accordance with instructions to further align multispectral image data based on image data corresponding to the first predetermined frequency band.

17. The imaging system of any one of claims 1 to 16, wherein the tissue region comprises at least a portion of a wound, an uneven tissue surface, pigmentation, or a lesion, the lesion comprising a cancerous lesion including, but not limited to, squamous cell carcinoma, basal cell carcinoma, Merkel cell carcinoma, malignant melanoma, and actinic keratosis.

18. The imaging system of any one of claims 1 to 17, wherein the one or more processors are configured in accordance with instructions to further detect at least a portion of a wound edge, an uneven tissue surface, pigmentation, or a lesion based on the 3D model.

19. 20. The imaging system of claim 18, wherein the one or more processors are further configured to calculate, in accordance with the instructions, one or more gradients of a wound edge, an uneven tissue surface, pigmentation, or a lesion relative to a wound surface, an uneven tissue surface, pigmentation, or a lesion.

20. 20. The imaging system of claim 19, wherein the one or more processors are configured, in accordance with instructions, to further determine a wound, uneven tissue surface, pigmentation, or lesion status based on a comparison of one or more gradients to a predetermined gradient threshold.

21. 21. The imaging system of claim 20, wherein the one or more processors are configured in accordance with instructions to further evaluate changes in wound, uneven tissue surface, pigmentation, or lesion condition based on multiple 3D models generated by the imaging system at different times.

22. 20. The imaging system of claim 18, wherein the one or more processors are configured in accordance with the instructions to further determine features including at least one of an area, rim height, volume, and skin configuration of the wound, uneven tissue surface, pigmentation, or lesion based at least in part on the 3D model and the detected wound edge, uneven tissue surface, pigmentation, or lesion.

23. 23. The imaging system of claim 22, wherein the one or more processors are configured, in accordance with instructions, to further evaluate changes in the characteristics based on multiple 3D models generated by the imaging system at different times.

24. The imaging system of claim 17 , wherein the wound comprises at least one of a burn, a surgical wound, a pressure ulcer, and a diabetic foot ulcer.

25. The imaging system of any preceding claim, wherein the one or more processors are configured, in accordance with instructions, to perform further image modification or transformation prior to identifying the corresponding set of pixels.

26. 26. The imaging system of claim 25, wherein the image modification or transformation includes correcting at least one of the following: size, distortion, skew, perspective, or rotation of the captured image.

27. 27. The imaging system of claim 1, wherein the one or more processors are configured to output a visual representation of the tissue region according to instructions and further based on a 3D model of the tissue region.

28. 30. The imaging system of claim 27, wherein the visual representation includes two-dimensional image data from at least one of the captured images fitted to a 3D model.

29. The imaging system of claim 27 , wherein the visual display comprises a heat map.

30. 30. The imaging system of claim 27, wherein the visual representation comprises a two-dimensional cross-sectional profile of at least a portion of the tissue region.

31. 31. The imaging system of claim 1, wherein the plurality of image sensors includes at least three image sensors, and each set further includes pixels of a third image captured by a third image sensor of the plurality of image sensors.

32. 32. The imaging system of claim 31, wherein the one or more processors are further configured, in accordance with computer executable instructions, to determine confidence values ​​corresponding to distance values ​​associated with respective corresponding sets of pixels.

33. 33. The imaging system of claim 32, wherein for each corresponding set of pixels, the distance value is determined based on pixels of a first image and a second image, and the confidence value is determined based at least in part on pixels of a third image.

34. 33. The imaging system of claim 32, wherein the confidence value comprises an uncertainty value expressed in units of a dimension, such as length.

35. 33. The imaging system of claim 32, wherein the one or more processors are configured in accordance with computer executable instructions to further generate a confidence map of the tissue region based on the 3D model and the determined confidence value.

36. 33. The imaging system of claim 32, wherein the one or more processors are further configured in accordance with computer executable instructions to eliminate pixels from the 3D model that have a confidence value that meets a predetermined threshold.

37. 33. The imaging system of claim 32, wherein identifying corresponding sets of pixels in the captured images includes evaluating a cost function that identifies matching areas or pixels in the first and second images.

38. 38. The imaging system of claim 37, wherein identifying the matching areas or pixels comprises minimizing a cost function.

39. 40. Use of the imaging system according to any one of claims 1 to 38 in wound debridement, optionally before, during or after the wound debridement procedure, such as to smooth out wound contours or edges.

40. 39. A method for improving wound healing, comprising generating a 3D model of a wound using an imaging system according to any one of claims 1 to 38, and debridement of the wound with reference to the 3D model generated by the imaging system, wherein debridement of the wound is performed with reference to the 3D model of the wound before, during or after debridement, for example to smooth contours or edges of the wound.

Images