Dermatological imaging system and method for generating three-dimensional (3D) image models

Abstract

A system and method for generating a three-dimensional (3D) image model of a skin surface is described. An exemplary method includes analyzing, by one or more processors, a plurality of images of a portion of a user's skin, the plurality of images being captured by a camera having an imaging axis extending through one or more lenses configured to focus on the portion of the skin, and each image of the plurality of images being illuminated by a different subset of a plurality of LEDs configured to be positioned around the portion of the skin. The exemplary method may further include generating, by the one or more processors, a 3D image model defining a topographical representation of the portion of the skin based on the plurality of images, and generating, by the one or more processors, a user-specific recommendation based on the 3D image model of the portion of the skin.

Description

[Technical field]

[0001] The present disclosure relates generally to dermatological imaging systems and methods, and more particularly to dermatological imaging systems and methods for generating three-dimensional (3D) image models. [Background technology]

[0002] Skin health, and correspondingly, skin care, plays a vital role in the overall health and appearance of all people. Since many common activities have a negative impact on skin health, a well-informed skin care routine, as well as regular visits to a dermatologist for evaluation and diagnosis of any skin condition, are a priority for many people. Problematically, scheduling a dermatologist visit can be tedious and time-consuming, and can put patients at risk of worsening their skin condition if they are unable to get a prompt appointment. Furthermore, traditional dermatological methods for evaluating many common skin conditions can be inaccurate, such as by failing to accurately and reliably identify abnormal textures or features on the skin surface.

[0003] As a result, many patients may neglect to undergo regular dermatological evaluations, and may even neglect skin care altogether due to a general lack of understanding.This problem is acutely evident, considering the myriad of skin conditions that may occur, and the myriad of related products and available treatment plans.Such existing skin care products may also provide little or no feedback or guidance to assist users in determining whether the product is applicable to their skin condition, or how best to utilize the product to treat the skin condition.Thus, many patients misdiagnose their skin condition, or do not purchase a product that effectively treats the skin condition, and therefore purchase the wrong or unnecessary product to treat or otherwise manage the actual or perceived skin condition. Summary of the Invention [Problem to be solved by the invention]

[0004] For the foregoing reasons, there is a need for a dermatological imaging system and method for generating a three-dimensional (3D) image model of the skin surface. [Means for solving the problem]

[0005] Described herein is a dermatological imaging system configured to generate a 3D image model of a skin surface. The dermatological imaging system includes a dermatological imaging device comprising a plurality of light-emitting diodes (LEDs) configured to be positioned around a portion of a user's skin and one or more lenses configured to focus on the portion of the skin. The dermatological imaging system further includes a computer application (app) including computing instructions that, when executed on a processor, cause the processor to analyze a plurality of images of the portion of the skin, the plurality of images being captured by a camera having an imaging axis extending through the one or more lenses, each image of the plurality of images being illuminated by a different subset of the plurality of LEDs, and generate a 3D image model defining a topographical representation of the portion of the skin based on the plurality of images. User-specific recommendations can be generated based on the 3D image model of the portion of the skin.

[0006] The dermatological imaging system described herein includes improvements over other technologies or technical fields, at least as the present disclosure describes or introduces improvements over the field of dermatological imaging devices and associated skin care products. For example, the dermatological imaging device of the present disclosure allows a user to quickly and conveniently capture a skin surface image and receive a complete 3D image model of the imaged skin surface on the display of the user's mobile device. In addition, the dermatological imaging system includes adding certain features outside of routine conventional activities well understood in the art, or unconventional steps that limit the scope of the claims to certain useful applications, such as capturing skin surface images for analysis using an imaging device in contact with the skin surface, where the camera is positioned at a short imaging distance from the skin surface.

[0007] The dermatological imaging system herein provides an improvement in computer capabilities or over other technologies because it at least improves the intelligence or predictive capabilities of the user computing device with a trained 3D image modeling algorithm. The 3D image modeling algorithm running on the user computing device or imaging server can accurately generate a 3D image model that defines a topographical representation of a portion of the user's skin based on pixel data of the portion of the user's skin. The 3D image modeling algorithm also generates user-specific recommendations (e.g., for manufactured products or medical care, etc.) designed to address features identifiable within the pixel data of the 3D image model. This is at least an improvement over conventional systems because conventional systems lack such real-time generation or classification capabilities and simply cannot accurately analyze user-specific images to output user-specific results to address features identifiable within the pixel data of the 3D image model. [Brief description of the drawings]

[0008] [Figure 1] 1 illustrates an embodiment of a digital imaging system. [Figure 2A]FIG. 2 is an overhead view of the imaging device. [Figure 2B] FIG. 2B is a cross-sectional side view of the imaging device of FIG. 2A along axis 2B. [Figure 2C] FIG. 2C is an enlarged view of the portion shown in FIG. 2B. [Figure 3A] 1 illustrates a camera calibration surface used to calibrate a camera. [Figure 3B] FIG. 1 is a lighting calibration diagram. [Figure 4] 1 illustrates an example video sampling period that may be used to synchronize camera image capture with a lighting sequence. [Figure 5A] 1 illustrates an example image and its associated pixel data that may be used to train and / or implement a 3D image modeling algorithm. [Figure 5B] 1 illustrates an example image and its associated pixel data that may be used to train and / or implement a 3D image modeling algorithm. [Figure 5C] 1 illustrates an example image and its associated pixel data that may be used to train and / or implement a 3D image modeling algorithm. [Figure 6] 1 illustrates an example workflow of a 3D image modeling algorithm that uses an input skin surface image to generate a 3D image model that defines a topographical representation of the skin surface. [Figure 7] 1 illustrates a diagram of an imaging method for generating a 3D image model of the skin surface. [Figure 8] 1 illustrates an exemplary user interface rendered on a display screen of a user computing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] FIG. 1 illustrates an exemplary digital imaging system 100 configured to analyze pixel data of images (e.g., images 130a, 130b, and / or 130c) of a user's skin surface to generate a 3D image model of the user's skin surface according to various embodiments disclosed herein. As referred to herein, "skin surface" may refer to any part of the human body, including the torso, hips, face, head, arms, legs, or other appendages, or any part or portion of the user's body. In the exemplary embodiment of FIG. 1, the digital imaging system 100 includes an imaging server 102 (also referred to herein as a "server"), which may comprise one or more computer servers. In various embodiments, the imaging server 102 includes multiple servers, which may comprise multiple, redundant, or replicated servers, as part of a server farm. In still further embodiments, the imaging server 102 may be implemented as a cloud-based server, such as a cloud-based computing platform. For example, the server 102 may be any one or more cloud-based platforms, such as MICROSOFT AZURE, AMAZON AWS, and the like. The server 102 may include one or more processors 104 as well as one or more computer memories 106 .

[0010] The memory 106 may include one or more forms of volatile and / or non-volatile memory fixed and / or removable memory, such as read-only memory (ROM), electronic programmable read-only memory (EPROM), random access memory (RAM), erasable electronic programmable read-only memory (EEPROM), and / or other hard drives, flash memory, MicroSD cards, etc. The memory 106 may store an operating system (OS) (e.g., Microsoft Windows, Linux, Unix, etc.) that may facilitate functions, apps, methods, or other software as discussed herein. The memory 106 may also store 3D image modeling algorithms 108, which may be artificial intelligence based models, such as machine learning models, trained on various images (e.g., images 130a, 130b, and / or 130c), as described herein. Additionally or alternatively, the 3D image modeling algorithm 108 may also be stored in a database 105 accessible to or otherwise communicatively coupled to the imaging server 102, and / or in memory of one or more user computing devices 111c1-111c3 and / or 112c1-112c3. The memory 106 may also store machine-readable instructions, including any of one or more applications, one or more software components, and / or one or more application programming interfaces (APIs), that may be implemented to facilitate or perform features, functions, or other disclosures described herein, such as any methods, processes, elements, or limitations, as illustrated, depicted, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein.For example, at least some of the applications, software components, or APIs may be, include, or otherwise be part of imaging-based machine learning models or components, such as 3D image modeling algorithm 108, where each may be configured to facilitate their various functions as discussed herein. It should be understood that one or more other applications may be envisioned and executed by processor 104.

[0011] The processor 104 may be connected to the memory 106 via a computer bus responsible for transmitting electronic data, data packets, or otherwise electronic signals between the processor 104 and the memory 106 to implement or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flowcharts, illustrations, schematics, figures, and / or other disclosures herein.

[0012] The processor 104 may interface with the memory 106 via a computer bus to execute an operating system (OS). The processor 104 may also interface with the memory 106 via a computer bus to create, read, update, delete, or otherwise access or interact with data stored in the memory 106 and / or the database 104 (e.g., a relational database such as Oracle, DB2, MySQL, or a NoSQL based database such as MongoDB). The data stored in the memory 106 and / or the database 105 may include all or a portion of any of the data or information described herein, including, for example, training images and / or user images (e.g., any of which may include any of the images 130a, 130b, and / or 130c), or other information of the user including demographics, age, race, skin type, and the like.

[0013] The imaging server 102 may further include a communication component configured to communicate (e.g., send and receive) data via one or more external / network ports to one or more network or local terminals, such as the computer network 120 and / or terminal 109 (for rendering or visualization) described herein. In some embodiments, the imaging server 102 may include a client-server platform technology, such as ASP.NET, Java J2EE, Ruby on Rails, Node.js, web services, or online APIs, responsible for receiving and responding to electronic requests. The imaging server 102 may implement a client-server platform technology that may interact with the memory 106 (including applications, components, APIs, data, etc. stored therein) and / or database 105 via a computer bus to implement or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flowcharts, illustrations, schematics, diagrams, and / or other disclosures herein. According to some embodiments, the imaging server 102 may include or interact with one or more transceivers (e.g., WWAN, WLAN, and / or WPAN transceivers) that function according to IEEE, 3GPP, or other standards and may be used to receive and transmit data via an external / network port connected to the computer network 120. In some embodiments, the computer network 120 may include a private network or a local area network (LAN). Additionally or alternatively, the computer network 120 may include a public network, such as the Internet.

[0014] The imaging server 102 may further include or implement an operator interface configured to present information to and / or receive input from an administrator or operator. As shown in FIG. 1, the operator interface may provide a display screen (e.g., via terminal 109). The imaging server 102 may also provide I / O components (e.g., ports, capacitive or resistive touch-sensitive input panels, keys, buttons, lights, LEDs), which may be directly accessible via or attached to the imaging server 102, or indirectly accessible via or attached to the terminal 109. According to some embodiments, an administrator or operator may access the server 102 via the terminal 109 to review information, make changes, input training data or images, and / or perform other functions.

[0015] As described herein above, in some embodiments, the imaging server 102 may perform functions as discussed herein as part of a “cloud” network or may communicate with other hardware or software components in the cloud to transmit, retrieve, or otherwise analyze data or information as otherwise described herein.

[0016] Typically, a computer program or computer-based product, application, or code (e.g., a model such as the AI ​​models described herein or other computing instructions) may be stored on a computer-usable storage medium having such computer-readable program code or computer instructions embodied therein, or a tangible non-transitory computer-readable medium (e.g., a standard random access memory (RAM), an optical disk, a universal serial bus (USB) drive, etc.), and the computer-readable program code or computer instructions may be installed on or otherwise adapted to be executed by the processor 104 (e.g., working in conjunction with a respective operating system in memory 106) and may facilitate, implement, or perform the machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flowcharts, illustrations, schematics, diagrams, and / or other disclosures herein. In this regard, the program code may be implemented in any desired programming language and may be implemented as machine code, assembly code, byte code, interpreted source code, or the like (e.g., via Golang, Python, C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML, CSS, XML, etc.).

[0017] 1, imaging server 102 is communicatively coupled to one or more user computing devices 111c1-111c3 via computer network 120 and / or to 112c1-112c3 via base stations 111b and 112b. In some embodiments, base stations 111b and 112b can include cellular base stations, such as cell towers, that communicate with one or more user computing devices 111c1-111c3 and 112c1-112c3 via wireless communication 121 based on any one or more of a variety of mobile telephony standards, including NMT, GSM, CDMA, UMMTS, LTE, 5G, etc. Additionally or alternatively, base stations 111b and 112b may include routers, wireless switches, or other such wireless connection points that communicate with one or more user computing devices 111c1-111c3 and 112c1-112c3 via wireless communication 122 based on any one or more of a variety of wireless standards, including, by way of non-limiting example, IEEE 802.11a / b / c / g (WIFI), BLUETOOTH standards, etc.

[0018] Any of the one or more user computing devices 111c1-111c3 and / or 112c1-112c3 may comprise a mobile device and / or a client device for accessing and / or communicating with the imaging server 102. In various embodiments, the user computing devices 111c1-111c3 and / or 112c1-112c3 may comprise a cellular phone, a mobile phone, a tablet device, a personal data assistant (PDA), etc., including, by way of non-limiting example, an APPLE iPhone or iPad device, or a GOOGLE ANDROID based mobile phone or tablet. In still further embodiments, the user computing devices 111c1-111c3 and / or 112c1-112c3 may comprise, for example, a home assistant device and / or a personal assistant device having a display screen, including, by way of non-limiting example, any one or more of a GOOGLE HOME device, an AMAZON ALEXA device, an ECHO SHOW device, etc.

[0019] Additionally, user computing devices 111c1-111c3 and / or 112c1-112c3 may comprise, for example, a retail computing device configured in the same or similar manner as described herein for user computing devices 111c1-111c3. The retail computing device may include a processor and memory for implementing or communicating with (e.g., via server 102) the 3D image modeling algorithm 108 as described herein. However, the retail computing device may be located, installed, or otherwise positioned within the retail environment to enable users and / or customers of the retail environment to utilize the digital imaging system and methods on-site within the retail environment. For example, the retail computing device may be installed within a kiosk for access by the user. The user may then upload or transfer images (e.g., from a user mobile device) to the kiosk for implementing the dermatological imaging system and methods described herein. Additionally or alternatively, the kiosk may be configured with a camera and dermatological imaging device 110 to allow the user to take new images of themselves for uploading and analysis (e.g., in a private manner, if warranted). In such an embodiment, the user or consumer may use the retail computing device to receive and / or render user-specific recommendations on a display screen of the retail computing device, as described herein. Additionally or alternatively, the retail computing device may be a mobile device (as described herein) carried by employees or other personnel of the retail environment to interact with users or consumers on-site.In such embodiments, the user or consumer may be able to interact with employees or other personnel of the retail environment via the retail computing device (e.g., by transferring images from the user's mobile device to the retail computing device or by capturing new images with the retail computing device's camera focused through the dermatological imaging device 110) to receive and / or render user-specific recommendations on a display screen of the retail computing device, as described herein.

[0020] Additionally, one or more of the user computing devices 111c1-111c3 and / or 112c1-112c3 may implement or execute an operating system (OS) or mobile platform, such as Apple's iOS and / or Google's Android operating system. Any of the one or more of the user computing devices 111c1-111c3 and / or 112c1-112c3 may include one or more processors and / or one or more memories for storing, implementing, or executing computing instructions or code, such as a mobile application or a home or personal assistant application, configured to perform some or all of the functionality of the present disclosure, as described in various embodiments herein. As shown in FIG. 1, the 3D image modeling algorithm 108 may be stored locally on the memory of the user computing device (e.g., user computing device 111c1). Additionally, the mobile applications stored on the user computing devices 111c1-111c3 and / or 112c1-112c3 may utilize the 3D image modeling algorithm 108 to perform some or all of the functionality of the present disclosure.

[0021] Additionally, one or more of user computing devices 111c1-111c3 and / or 112c1-112c3 may include a digital camera and / or a digital video camera for capturing or filming digital images and / or frames (which may be, for example, images 130a, 130b, and / or 130c). Each digital image may include pixel data for training or implementing a model, such as an artificial intelligence (AI), machine learning model, and / or rule-based algorithm, as described herein. For example, the digital camera and / or the digital video camera of any of user computing devices 111c1-111c3 and / or 112c1-112c3 may be configured to film, capture, or otherwise generate digital images, and, in at least some embodiments, may store such images in a memory of the respective user computing device. A user may also attach a dermatological imaging device 110 to the user computing device to facilitate the user computing device capturing sufficient images to locally process the captured images using 3D image modeling algorithm 108.

[0022] Still further, each of one or more of user computing devices 111c1-111c3 and / or 112c1-112c3 may include a display screen for displaying graphics, images, text, product recommendations, data, pixels, features, and / or other such visualizations or information as described herein. These graphics, images, text, product recommendations, data, pixels, features, and / or other such visualizations or information may be generated by the user computing device as a result of implementing 3D image modeling algorithm 108 utilizing images captured by a camera of the user computing device focused through dermatological imaging device 110. In various embodiments, the graphics, images, text, product recommendations, data, pixels, features, and / or other such visualizations or information may be received by server 102 for display on any one or more display screens of user computing devices 111c1-111c3 and / or 112c1-112c3. Additionally or alternatively, the user computing device may, at least in part, comprise, implement, have access to, render, or otherwise expose an interface or guided user interface (GUI) for displaying text and / or images on its display screen.

[0023] User computing devices 111c1-111c3 and / or 112c1-112c3 may include wireless transceivers for receiving and transmitting wireless communications 121 and / or 122 to / from base stations 111b and / or 112b. Pixel-based images (e.g., images 130a, 130b, and / or 130c) may be transmitted via computer network 120 to imaging server 102 for model training and / or image analysis, as described herein.

[0024] 2 is an overhead view 200, a side view 210, and a cutaway view 214 of a dermatological imaging device 110, according to various embodiments disclosed herein. The overhead view 200 features the dermatological imaging device 110 attached to the rear of a user mobile device 202. Generally, the dermatological imaging device 110 is configured to couple to the user mobile device 202 in a manner that positions the camera of the user mobile device in optical alignment with the lens and aperture of the dermatological imaging device 110. It should be understood that the dermatological imaging device 110 can be detachably or immovably coupled to the user mobile device 202 using any suitable means.

[0025] The side view 210 illustrates the position of the dermatological imaging device 110 relative to the camera 212 of the user mobile device 202. More specifically, the cutaway view 214 illustrates the alignment of the camera 212 of the user mobile device 202 with the lens set 216 and aperture 218 of the dermatological imaging device 110. The lens set 216 may be configured to focus the camera 212 on an object positioned at a distance of the aperture 218 from the camera 212. Thus, as discussed further herein, a user may place the aperture of the dermatological imaging device 110 in contact with a portion of the user's skin, and the lens set 216 enables the camera 212 of the user mobile device 202 to capture an image of the user's skin portion. In various embodiments, the distance from the aperture 218 to the camera 212 may define a short imaging distance that may be 35 mm or less. In various embodiments, the aperture 218 may be circular and have a diameter of approximately 20 mm.

[0026] The dermatological imaging device 110 may also include light emitting diodes (LEDs) 220 configured to illuminate an object disposed within a field of view (FOV) of the camera 212 through the aperture 218. Each of the LEDs 220 may be positioned within the dermatological imaging device 110 and may be disposed within the dermatological imaging device 110 such that the LEDs 220 form a perimeter around an object disposed within the FOV defined by the aperture 218. For example, a user may place the combination of the user mobile device 202 and the dermatological imaging device 110 on a portion of the user's skin such that the portion of the skin is visible to the camera 212 through the aperture 218. The LEDs 220 may be positioned within the dermatological imaging device 110 in a manner that forms a perimeter around the portion of the skin. Additionally, the dermatological imaging device 110 may include any suitable number of LEDs 220. In various embodiments, the dermatological imaging device 110 may include 21 LEDs 220, which may be uniformly distributed in an approximately circular, ring-like fashion to establish a perimeter around an object placed within the FOV defined by the aperture 218. In some embodiments, the LEDs 220 may be positioned between the camera 212 and the aperture 218, approximately half the distance from the camera 212 to the aperture 218.

[0027] At such short imaging distances, conventional imaging systems may suffer from substantial internal reflections of the light source, resulting in poor image quality. To avoid these problems of conventional imaging systems, the inner surface 222 of the dermatological imaging device 110 may be coated with a highly light-absorbing paint. In this manner, the LED 220 may illuminate an object in contact with the outer surface of the aperture 218 without generating substantial internal reflections, thereby ensuring optimal image quality.

[0028] However, to further ensure optimal image quality and enable the 3D image modeling algorithms to optimally perform the functions described herein, the camera 212 and LED 220 may be calibrated. Conventional systems may struggle to calibrate the camera and lighting devices at such short imaging distances due to distorted image characteristics (e.g., degradation of the object surface) and other similar anomalies. The techniques of the present disclosure solve these problems associated with conventional systems, for example, using a random sampling consensus algorithm (discussed with respect to FIG. 3A) and ray path tracing (discussed with respect to FIG. 3B). More generally, each of FIGS. 3A, 3B, and 4 describes a calibration technique that may be used to overcome shortcomings of conventional systems and that may be performed prior to or as part of the 3D imaging modeling techniques described herein with reference to FIGS. 5A-8.

[0029] FIG. 3A illustrates an exemplary camera calibration surface 300 used to calibrate a camera (e.g., camera 202) for use with the dermatological imaging device 110 of FIGS. 2A-2C according to various embodiments disclosed herein. In general, the exemplary camera calibration surface 300 may have known dimensions and may include a pattern or other design used to divide the exemplary camera calibration surface 300 into equally spaced / dimensional subsections. As illustrated in FIG. 3A, the exemplary camera calibration surface 300 may include a checkerboard pattern, with each square of the pattern having equal dimensions. Using image data derived from a captured image of the exemplary camera calibration surface 300, the user mobile device 202 may determine imaging parameters corresponding to the camera 212 and lens set 216. The image data may broadly refer to dimensions of identifiable features represented in an image of the exemplary camera calibration surface 300. For example, the user mobile device 202 may determine (e.g., via a mobile application) scaling parameters, focal length, distance to focal plane, and / or other suitable parameters based on image data derived from an image of the exemplary camera calibration surface 300 to be applied to an image captured by the camera 212 when the dermatological imaging device 110 is attached to the user mobile device 202.

[0030] To begin calibrating the camera 212, a user may place the combination of the user mobile device 202 and the dermatological imaging device 110 on the exemplary camera calibration surface 300. When the user mobile device 202 and the dermatological imaging device 110 are in place, the user mobile device 202 may prompt the user to perform a calibration image capture sequence and / or the user may manually initiate the calibration image capture sequence. The user mobile device 202 may proceed to capture one or more images of the exemplary camera calibration surface 300, and the user may slide or otherwise move the combination of the user mobile device 202 and the dermatological imaging device 110 across the exemplary camera calibration surface 300 to capture images of different portions of the surface 300. In some embodiments, the calibration image capture sequence is a video sequence, and the user mobile device 202 may analyze still frames from the video sequence to derive image data. In other embodiments, the calibration image capture sequence is a series of single image captures, with the user mobile device 202 prompting the user between each capture to move the combination of the user mobile device 202 and the dermatological imaging device 110 to a different location on the exemplary camera calibration surface 300.

[0031] During (e.g., in real-time) or after the calibration image capture sequence, the user mobile device 202 may select a set of images from the video sequence or a series of single image captures to determine image data. In general, each image in the set of images may feature ideal imaging characteristics suitable for determining image data. For example, the user mobile device 202 may select images that represent or include each of the regions 302a, 302b, and 302c by using a random sampling consensus algorithm configured to identify such regions based on their image characteristics. Images that include these regions 302a, 302b, 302c may include optimal contrast between different color / pattern squares of the checkerboard, minimal image degradation (e.g., resolution interference) due to physical effects associated with moving the user mobile device 202 and dermatological imaging device 110 combination across the exemplary camera calibration surface 300, and / or any other suitable imaging characteristics or combinations thereof.

[0032] Using each image in the set of images, the user mobile device 202 (e.g., via a mobile app) may determine image data, for example, by correlating identified image features with known feature dimensions. A single square in the checkerboard pattern of the exemplary camera calibration surface 300 may measure 10 mm by 10 mm. Thus, if the user mobile device 202 identifies that the image representing the area 302c contains one complete square, the user mobile device 202 may correlate the area in the image to measure 10 mm by 10 mm. This image data may also be compared to the known dimensions of the dermatological imaging device 110. For example, the aperture 218 of the dermatological imaging device 110 may measure 20 mm in diameter, such that when the combination of the user mobile device 202 and the dermatological imaging device 110 are in contact with a surface, the area represented by the image captured by the camera 212 generally does not measure more than 20 mm in diameter. Thus, the user mobile device 202 may more accurately determine the image data taking into account the approximate dimensions of the area represented by the image. Of course, surface anomalies or other imperfections may cause the area represented by the image to be larger than the known dimensions of the aperture 218. For example, a user may press the dermatological imaging device 110 against a flexible surface (e.g., a skin surface) using sufficient force to deform the surface, causing more surface area to enter the dermatological imaging device 110 through the aperture 218 than a circular area defined by a 20 mm diameter.

[0033] In any case, the LEDs 220 may also require calibration to optimally perform the 3D image modeling functions described herein. FIG. 3B is an illumination calibration diagram 310 corresponding to an exemplary calibration technique for the illumination components (e.g., LEDs 220) of the dermatological imaging device 110 of FIGS. 2A-2C, according to various embodiments disclosed herein. The illumination calibration diagram 310 includes a camera 212, a number of LEDs 220 illuminating the object 312, and light rays 314 representing the path traversed by the illumination emitted from the LEDs 220 to reach the camera 212. The user mobile device 202 (e.g., via a mobile application) may initiate an illumination calibration sequence in which each of the LEDs 220 in the dermatological imaging device 110 individually ramps up / down to illuminate the object 312, and the camera 212 captures an image corresponding to each respective LED 220 individually illuminating the object 312. The object 312 may be, for example, a ball bearing and / or any other suitable object or combination thereof.

[0034] As illustrated in FIG. 3B, illumination emitted from the left-most LED 220 is incident on each of the objects 312 and reflected along a path represented by the light ray 314 to the camera 212. The user mobile device 202 may include, as part of a mobile application, a path tracking module configured to track each of the light rays reflected from the objects 312 back to their intersection points. In doing so, the path tracking module may identify the location of the left-most LED 220. Thus, the user mobile device 202 may calculate a 3D position and orientation corresponding to each of the LEDs 220 and their respective illumination, along with, for example, the number of LEDs 220, the illumination angle associated with each respective LED 220, the intensity of each respective LED 220, the temperature of the illumination emitted from each respective LED 220, and / or any other suitable illumination parameters. The illumination calibration diagram 310 includes four objects 312, and the user mobile device 202 may require at least two objects 312 reflecting illumination from the LEDs 220 to accurately identify the intersection points, thereby enabling the illumination calibration sequence.

[0035] Advantageously, with a properly calibrated camera 212 and LEDs 220, the combination of the user mobile device 202 and the dermatological imaging device 110 can perform the 3D image modeling functions described herein. However, other physical effects (e.g., camera jitter) may further impair the 3D image modeling functions despite the calibration. To minimize the impact of these other physical effects, the camera 212 and the LEDs 220 may be controlled asynchronously. Such asynchronous control may prevent the surface being imaged from moving during image capture, and as a result, may minimize the impact of effects such as camera jitter. As part of the asynchronous control, the camera 212 may perform a video sampling period in which the camera 212 captures a series of frames (e.g., high-definition (HD) video) while each LED 220 independently ramps up / down in an illumination sequence.

[0036] In general, asynchronous control of the camera 212 and the LEDs 220 may result in frames captured by the camera 212 as part of a video sampling period that do not feature the respective LEDs 220 fully ramped up (e.g., fully illuminated). To address this potential issue, the user mobile device 202 may include a synchronization module (e.g., as part of a mobile application) configured to synchronize the camera 212 frames with the ramp-up times of the LEDs 220 by identifying individual frames that correspond to fully ramped-up LED 220 illumination. FIG. 4 is a graph 400 illustrating an example video sampling period that a synchronization module may use to synchronize the camera 212 frame capture with the illumination sequence of the illumination components (e.g., LEDs 220) of the dermatological imaging device 110 of FIGS. 2A-2C according to various embodiments disclosed herein. The graph 400 includes an x-axis corresponding to individual frames captured by the camera 212 and a y-axis corresponding to the average pixel intensity of the respective frames. Each circle included in the graph (e.g., frame captures 404, 406a, 406b) corresponds to a single image capture by camera 212, and some of the circles (e.g., frame captures 404, 406a) additionally include a square circumscribing the circle indicating that the image capture represented by the circumscribing circle has the maximum average pixel intensity corresponding to the emitting illumination of an individual LED 220.

[0037] As illustrated in FIG. 4, the graph 400 has 21 peaks, each corresponding to the ramp-up / ramp-down sequence of a particular LED 220. The user mobile device 202 (e.g., via a mobile application) may asynchronously initiate the video sampling period and the illumination sequence such that the camera 212 may capture HD video during the video sampling period of each LED 220 that individually ramps up / down to illuminate a region of interest (ROI) that is visible through the aperture 218 as part of the illumination sequence. As a result, the camera 212 may capture multiple frames of the ROI that include illumination from one or more LEDs 220 while partially and / or fully illuminated. The synchronization module may analyze each frame to generate a plot similar to the graph 400 that characterizes the average pixel intensity of each captured frame, and may further determine a frame capture that corresponds to a maximum average pixel intensity for each LED 220. The synchronization module may use, for example, a predetermined number of LEDs 220 to determine the number of maximum average pixel intensity frame captures, and / or the module may determine the number of peaks included in the generated plot.

[0038] To illustrate, the synchronization module may analyze the pixel intensity of the first seven captured frames based on a known ramp-up time for each LED 220 (e.g., ramp-up / ramp-down frame bandwidth), determine the maximum average pixel intensity value among the first seven frames, designate the frame corresponding to the maximum average pixel intensity as the LED 220 illumination frame, and proceed to analyze the subsequent seven captured frames in a similar manner until all captured frames have been analyzed. Additionally or alternatively, the synchronization module may continue to analyze the captured frames until a number of frames are designated as the maximum average pixel intensity frames corresponding to the predetermined number of LEDs 220. For example, if the predetermined number of LEDs 220 is 21, the synchronization module may continue to analyze the captured frames until 21 captured frames are designated as the maximum average pixel intensity frames.

[0039] Of course, the pixel intensity values ​​may be analyzed according to mean pixel intensity, average pixel intensity, weighted average pixel intensity, and / or any other suitable pixel intensity measurement, or combinations thereof. Furthermore, the pixel intensity may be calculated in a modified color space (e.g., a color space different from the red-green-blue (RGB) space). In this way, the signal profile of the pixel intensities within the ROI may be improved, and as a result, the synchronization module may more accurately specify / determine the maximum average pixel intensity frame.

[0040] Once the synchronization module designates a maximum average pixel intensity frame corresponding to each LED 220, the synchronization module may automatically identify frames including full illumination from each respective LED 220 in subsequent video sampling periods captured by the combination of the user mobile device 202 and the dermatological imaging device 110. Each video sampling period may span the same number of frame captures, and the asynchronous control of the LEDs 220 may cause each LED 220 to ramp up / down in the same sequential firing order in the same frame of the video sampling period. Thus, after a particular video sampling period, the synchronization module may automatically designate frame capture 404, 406a as the maximum average pixel intensity frame and frame capture 406b as the non-maximum average pixel intensity frame. It should be understood that the synchronization module may perform the synchronization techniques described herein once to initially calibrate (e.g., synchronize) the video sampling period and illumination sequence, multiple times to periodically recalibrate the video sampling period and illumination sequence according to a predetermined frequency or as determined in real time, and / or as part of each video sampling period and illumination sequence.

[0041] When the combination of the user mobile device 202 and the dermatological imaging device 110 is properly calibrated, the user may begin capturing images of their skin surface to receive a 3D image model of their skin surface in accordance with the techniques of the present disclosure. For example, FIGS. 5A-5C illustrate example images 130a, 130b, and 130c that may be captured and analyzed by the combination of the user mobile device 202 and the dermatological imaging device 110 to generate a 3D image model of the user's skin surface. Each of these images may be collected / aggregated at the user mobile device 202 and analyzed by and / or used to train a 3D image modeling algorithm (e.g., the 3D image modeling algorithm 108). In some embodiments, the skin surface images may be collected or aggregated at the imaging server 102 and analyzed by and / or used to train a 3D image modeling algorithm (e.g., an AI model such as a machine learning image model as described herein).

[0042] Each image representing the exemplary regions 130a, 130b, 130c may include pixel data 502ap, 502bp, and 502cp (e.g., RGB data) that represent feature data and correspond to each of the particular attributes of the respective skin surface in the respective image. Generally, as described herein, the pixel data 502ap, 502bp, 502cp include points or squares of data in the image, each point or square representing a single pixel in the image (e.g., pixels 502ap1, 502ap2, 502bp1, 502bp2, 502cp1, and 502cp2). Each pixel may be a particular location in the image. In addition, each pixel may have a particular color (or lack thereof). The pixel color may be determined by the color format and associated channel data associated with a given pixel. For example, a common color format includes a red-green-blue (RGB) format having red, green, and blue channels. That is, in RGB format, the data of a pixel is represented by three numerical RGB components (red, green, blue), which may be referred to as channel data, to manipulate the color of the pixel's area in an image. In some implementations, the three RGB components may be represented as three 8-bit numbers per pixel. Three 8-bit bytes (one byte for each of RGB) are used to generate a 24-bit color. Each 8-bit RGB component can have 256 possible values ​​ranging from 0 to 255 (i.e., in a base-2 binary system, an 8-bit byte can contain one of 256 numerical values ​​ranging from 0 to 255). This channel data (R, G, and B) can be assigned values ​​from 0 to 255, which are used to set the color of the pixel. For example, three values ​​such as (250, 165, 0), meaning (red=250, green=165, blue=0), can represent an orange pixel. As a further example, (Red=255, Green=255, Blue=0) means red and green are each fully saturated (255 is the brightness 8 bits can provide) and no blue (0), the resulting color is yellow. As a still further example, black has RGB values ​​of (Red=0, Green=0, Blue=0) and white has RGB values ​​of (Red=255, Green=255, Blue=255). Gray has the property of having equal or similar RGB values.Thus, (red=220, green=220, blue=220) is light gray (closer to white) and (red=40, green=40, blue=40) is dark gray (closer to black).

[0043] In this way, the combination of the three RGB values ​​produces the final color for a given pixel. In a 24-bit RGB color image using 3 bytes, there can be 256 shades of red, 256 shades of green, and 256 shades of blue. This provides 256x256x256, or 16.7 million possible combinations or colors for a 24-bit RGB color image. In this way, the RGB data value of a pixel indicates how much of each of red, green, and blue the pixel is made up of. The three colors and intensity levels are combined at that image pixel, i.e., at that pixel location on the display screen, to illuminate the display screen with that color at that location. However, it should be understood that other bit sizes having fewer or more bits, e.g., 10 bits, can be used to provide fewer or more overall colors and ranges. For example, the user mobile device 202 can analyze an image captured in grayscale instead of RGB color space.

[0044] Collectively, the various pixels positioned together in a grid pattern form a digital image (e.g., images 130a, 130b, and / or 130c). A single digital image may contain thousands or millions of pixels. Images may be captured, generated, stored, and / or transmitted in a number of formats, such as JPEG, TIFF, PNG, and GIF. These formats use pixels to store and represent images.

[0045] 5A illustrates an example image 130a and its associated pixel data (e.g., pixel data 502ap) that may be used to train and / or implement a 3D image modeling algorithm (e.g., 3D image modeling algorithm 108) according to various embodiments disclosed herein. The example image 130a illustrates a portion of a user's skin surface (e.g., a user's facial area) that characterizes an acne lesion. In various embodiments, a user may capture an image for analysis by at least one user mobile device 202 of the user's face, user's cheek, user's neck, user's chin, user's head, user's groin, user's armpits, user's chest, user's back, user's legs, user's arms, user's abdomen, user's feet, and / or any other suitable area of ​​the user's body, or combination thereof. The example image 130a may represent, for example, a user attempting to track the formation and removal of acne lesions over time using a combination of a user mobile device 202 and a dermatological imaging device 110 as discussed herein.

[0046] Image 130a is comprised of pixel data 502ap, including, for example, pixels 502ap1 and 502ap2. Pixel 502ap1 may be a relatively dark pixel (e.g., a pixel with low R, G, and B values) located in image 130a resulting from, for example, a user having a relatively low degree of skin relief / reflectance at the location represented by pixel 502ap1 due to an anomaly on the skin surface (e.g., an enlarged pore or damaged skin cells). Pixel 502ap2 may be a relatively bright pixel (e.g., a pixel with high R, G, and B values) located in image 130a resulting from a user having an acne lesion at the location represented by pixel 502ap2.

[0047] The combination of the user mobile device 202 and the dermatological imaging device 110 may capture the image 130a under multiple angles / intensities of illumination (e.g., via the LED 220) as part of a video sampling period and illumination sequence. Thus, the pixel data 502ap may include multiple darkness / lightness values ​​for individual pixels (e.g., 502ap1, 502ap2) corresponding to the multiple illumination angles / intensities associated with each capture of the image 130a during the video sampling period. The pixel 502ap1 may generally appear darker than the pixel 502ap2 in the image capture of the video sampling period due to differences in the characteristics represented by the two pixels 502ap1, 502ap2. Thus, this difference in dark / light appearance and any shadow cast by the pixel 502ap2 may, in part, cause the 3D image modeling algorithm 108 to display the pixel 502ap2 as a raised portion of the skin surface represented by the image 130a relative to the pixel 502ap1, as discussed further herein.

[0048] 5B illustrates a further exemplary image 130b and its associated pixel data (e.g., pixel data 502bp) that may be used to train and / or implement a 3D image modeling algorithm (e.g., 3D image modeling algorithm 108) according to various embodiments disclosed herein. The exemplary image 130b illustrates a portion of a user's skin surface (e.g., a user's hand or arm area) that includes an actinic keratosis lesion. The exemplary image 130b may represent a user utilizing a combination of a user mobile device 202 and a dermatological imaging device 110 to inspect / analyze the microrelief of a skin lesion formed on the user's hand, for example.

[0049] Image 130b is composed of pixel data including pixel data 502bp. Pixel data 502bp includes a plurality of pixels including pixel 502bp1 and pixel 502bp2. Pixel 502bp1 may be a light pixel (e.g., a pixel with high R, G, and / or B values) located in image 130b resulting from a user having a relatively low degree of skin relief at the location represented by pixel 502bp1. Pixel 502bp2 may be a dark pixel (e.g., a pixel with low R, G, and B values) located in image 130b resulting from a user having a relatively high degree of skin relief at the location represented by pixel 502bp2, for example due to a skin lesion.

[0050] The combination of the user mobile device 202 and the dermatological imaging device 110 may capture the image 130b under multiple angles / intensities of illumination (e.g., via the LED 220) as part of a video sampling period and illumination sequence. Thus, the pixel data 502bp may include multiple darkness / lightness values ​​for individual pixels (e.g., 502bp1, 502bp2) corresponding to the multiple illumination angles / intensities associated with each capture of the image 130b during the video sampling period. The pixel 502bp2 may generally appear darker than the pixel 502bp2 in the image capture of the video sampling period due to differences in the characteristics represented by the two pixels 502bp1, 502bp1. Thus, this difference in dark / light appearance and any shadow cast on the pixel 502bp2 may, in part, cause the 3D image modeling algorithm 108 to display the pixel 502bp1 as a raised portion of the skin surface represented by the image 130b relative to the pixel 502bp2, as discussed further herein.

[0051] 5C illustrates a further exemplary image 130c and its associated pixel data (e.g., 502cp) that may be used to train and / or implement a 3D image modeling algorithm (e.g., 3D image modeling algorithm 108) according to various embodiments disclosed herein. The exemplary image 130c illustrates a portion of a user's skin surface (e.g., the user's chest or back area) that includes a skin rash as a result of an allergic reaction experienced by the user. The exemplary image 130c may represent a user utilizing a combination of a user mobile device 202 and a dermatological imaging device 110 to inspect / analyze a rash caused by an allergic reaction, for example, as discussed further herein.

[0052] Image 130c is comprised of pixel data including pixel data 502cp. Pixel data 502cp includes a plurality of pixels including pixel 502cp1 and pixel 502cp2. Pixel 502cp1 may be a bright red pixel (e.g., a pixel with a relatively high R value) located in image 130c resulting from a user having a skin rash at the location represented by pixel 502cp1. Pixel 502cp2 may be a light pixel (e.g., a pixel with high R, G, and / or B values) located in image 130c resulting from a user 130cu having a minimal skin rash at the location represented by pixel 502cp2.

[0053] The combination of the user mobile device 202 and the dermatological imaging device 110 may capture the image 130c under multiple angles / intensities of illumination (e.g., via the LED 220) as part of a video sampling period and illumination sequence. Thus, the pixel data 502cp may include multiple darkness / lightness values ​​and multiple color values ​​for individual pixels (e.g., 502cp1, 502cp2) corresponding to the multiple illumination angles / intensities associated with each capture of the image 130c during the video sampling period. The pixel 502cp2 may generally appear lighter and more neutral skin tone than the pixel 502cp1 in the image capture of the video sampling period due to differences in the characteristics represented by the two pixels 502cp1, 502cp2. Thus, this difference in dark / light appearance, RGB color values, and any shadow cast due to pixel 502cp2, as discussed further herein, may, in part, cause the 3D image modeling algorithm 108 to display pixel 502cp1 as a raised, redder portion of the skin surface represented by image 130c relative to pixel 502cp2.

[0054] Each of the pixel data 130ap, 130bp, and 130cp includes various remaining pixels that include the remaining portion of the user's skin surface area characterized by various lightness / darkness and color values. Each of the pixel data 130ap, 130bp, and 130cp further includes pixels that represent additional features including the contours of the user's skin due to anatomical and other features of the user's skin surface, as shown in Figures 5A-5C.

[0055] It should be appreciated that each of the images represented in Figures 5A-5C may arrive and be processed in real-time and / or near real-time according to a 3D image modeling algorithm (e.g., 3D image modeling algorithm 108), as described further herein. For example, a user may capture image 130c as an allergic reaction occurs, and the 3D image modeling algorithm may provide feedback, recommendations, and / or other comments in real-time or near real-time.

[0056] In any case, when an image is captured by the combination of the user mobile device 202 and the dermatological imaging device 110, the image may be processed by a 3D image modeling algorithm 108 stored on the user mobile device 202 (e.g., as part of a mobile application). Figure 6 illustrates an example workflow of the 3D image modeling algorithm 108 that uses an input skin surface image 600 to generate a 3D image model 610 that defines a topographical representation of the skin surface. In general, the 3D image modeling algorithm 108 may analyze pixel values ​​of multiple skin surface images (e.g., similar to the input skin surface image 600) to construct the 3D image model 610.

[0057] More specifically, the 3D image modeling algorithm 108 may estimate the 3D image model 610 by utilizing the pixel values ​​to solve a photometric stereo equation as given by:

[0058]

number

[0059]

number

[0060]

number

[0061] Estimating the 3D image model 610 may depend heavily on the skin type (e.g., skin color, skin surface area, etc.) corresponding to the skin surface represented in the captured image. Advantageously, the 3D image modeling algorithm 108 may automatically determine the skin type corresponding to the skin surface represented in the captured image by iteratively estimating normals according to equation (1). The 3D image modeling algorithm 108 may also balance pixel intensities across the captured image to facilitate skin type determination, taking into account the estimated normals for each pixel.

[0062] Additionally, the 3D image modeling algorithm 108 may estimate a probabilistic cone of illumination for a particular captured image when generating the 3D image model 610. In general, when the light source illuminating the imaged plane is at infinity, the light rays incident on the plane are assumed to be parallel, and all points on the plane are illuminated with equal intensity. However, when the light source is very close to the surface (e.g., within 35 mm), the light rays incident on the plane form a cone. As a result, points on the plane closer to the light source are brighter than points on the plane further away from the light source. Thus, the 3D image modeling algorithm 108 may use the captured image in conjunction with known dimensional parameters describing the combination of the user mobile device 202 and the dermatological imaging device 110 (e.g., the position of the 3D LED 220, the distance from the LED 220 to the ROI, the distance from the camera 212 to the ROI, etc.) to estimate a probabilistic cone of illumination for the captured image.

[0063] 7 illustrates a diagram of a dermatological imaging method 700 that analyzes pixel data of images (e.g., images 130a, 130b, and / or 130c) of a user's skin surface to generate a three-dimensional (3D) image model of the skin surface, according to various embodiments disclosed herein. An image, as described herein, is generally a pixel image such as captured by a digital camera (e.g., camera 212 of user mobile device 202). In some embodiments, an image may include or refer to multiple images, such as multiple images (e.g., frames) such as collected using a digital video camera. A frame may include a sequence of images that define a movement, and may include a motion picture, video, and the like.

[0064] At block 702, method 700 includes analyzing, by one or more processors, images of a portion of a user's skin, the images captured by a camera (e.g., camera 212) having an imaging axis extending through one or more lenses (e.g., lens set 216) configured to focus on the portion of the skin. Each image may be illuminated by a different subset of LEDs (e.g., LEDs 220) configured to be positioned approximately around the portion of the skin. For example, the images may represent the respective user's acne lesions (e.g., as illustrated in FIG. 5A), the respective user's actinic keratosis lesions (e.g., as illustrated in FIG. 5B), the respective user's allergy rashes (e.g., as illustrated in FIG. 5C), and / or the respective user's skin condition (or lack thereof) of any kind located on the respective user's head, the respective user's groin, the respective user's armpits, the respective user's chest, the respective user's back, the respective user's legs, the respective user's arms, the respective user's stomach, the respective user's feet, and / or any other suitable area of ​​the respective user's body, or a combination thereof.

[0065] In some embodiments, a subset of the LEDs may illuminate a portion of the skin with a first illumination intensity, and a different subset of the LEDs may illuminate the portion of the skin with a second illumination intensity that is different from the first illumination intensity. For example, a first LED may illuminate a portion of the skin with a first wattage, and a second LED may illuminate a portion of the skin with a second wattage. In this example, the second wattage may be twice the value of the first wattage, such that the second LED illuminates the portion of the skin with twice the intensity of the first LED.

[0066] Furthermore, in some embodiments, the illumination provided by each different subset of LEDs may illuminate a portion of the skin from a different illumination angle. For example, assume that a line parallel to the orientation of the user mobile device 202 extending perpendicularly in both directions from the center of the ROI (e.g., the "normal") defines an illumination angle of 0 degrees. Thus, a first LED may illuminate a portion of the skin from a first illumination angle of 90 degrees from the normal, and a second LED may illuminate a portion of the skin from a second illumination angle of 30 degrees from the normal. In this example, a first captured image illuminated by the first LED from the first illumination angle may include a different shadow than a second captured image illuminated by the second LED from the second illumination angle. As a result, each image captured by the combination of the user mobile device 202 and the dermatological imaging device 110 may feature a different set of shadows cast on a portion of the skin as a result of illumination from different illumination angles.

[0067] Additionally, in some embodiments, the user mobile device 202 (e.g., via a mobile application) may calibrate the camera 212 using a random sampling consensus algorithm prior to analyzing the captured image. The random sampling consensus algorithm may be configured to select an ideal image from a video capture sequence of the calibration plate. As referred to herein, the video capture sequence may collectively refer to the “video sampling period” and “illumination sequence” described herein. For example, the user mobile device 202 may utilize the video capture sequence to calibrate the camera 212, the LED 220, and / or any other suitable hardware. Furthermore, the user mobile device 202 may utilize the video capture sequence to generate a 3D image model of the user's skin surface. In these embodiments, the user mobile device 202 may also calibrate the LED 220 by path tracing light rays reflected from multiple reflective objects (e.g., object 312).

[0068] In some embodiments, the user mobile device 202 may capture images at a short imaging distance. For example, the short imaging distance may be 35 mm or less, such that the distance between the camera and the ROI (e.g., as defined by the aperture 218) is 35 mm or less.

[0069] In some embodiments, the camera 212 may capture images during a video capture sequence, and different subsets of the LEDs 220 may be sequentially activated and sequentially deactivated during the video capture sequence (e.g., as part of an illumination sequence). Furthermore, in these embodiments, the 3D image modeling algorithm 108 may calculate an average pixel intensity for each image and match each image with its respective maximum average pixel intensity. For example, as previously described, if the dermatological imaging device 110 includes 21 LEDs 220, the 3D image modeling algorithm 108 may designate the 21 images as the maximum average pixel intensity images. Furthermore, the LEDs 220 and the camera 212 may be asynchronously controlled by the user mobile device 202 (e.g., via a mobile application) during the video capture sequence.

[0070] At optional block 704, the method 700 may include the 3D image modeling algorithm 108 estimating a probabilistic cone of illumination corresponding to each image. For example, as previously described, the 3D image modeling algorithm 108 may utilize a processor of the user mobile device 202 (e.g., any of the user computing devices 111c1-111c3 and / or 112c1-112c3) and / or the imaging server 102 to estimate a probabilistic cone of illumination for the captured image. The probabilistic cone may represent an estimated incident illumination from the LED 220 on the ROI during image capture.

[0071] At block 706, the method 700 may include generating, by one or more processors, a 3D image model (e.g., 3D image model 610) that defines a topographical representation of the portion of the skin based on the captured image. The 3D image model may be generated, for example, by the 3D image modeling algorithm 108. In some embodiments, the 3D image modeling algorithm 108 may compare the 3D image model to another 3D image model that defines another topographical representation of the portion of the skin of another user. In these embodiments, the other users may share their age or skin condition with the user. The skin condition may include at least one of (i) skin cancer, (ii) sunburn, (iii) acne, (iv) xerosis, (v) seborrhea, (vi) eczema, or (vii) hives.

[0072] In some embodiments, the 3D image modeling algorithm 108 may determine that the 3D image model defines a topographical representation corresponding to the skin of a set of users having a skin type class. In general, the skin type class may correspond to any suitable characteristic of the skin, such as pore size, redness, scarring, lesion count, freckle density, and / or any other suitable characteristic or combination thereof. In further embodiments, the skin type class may correspond to skin color.

[0073] In various embodiments, the 3D image modeling algorithm 108 is an AI-based model trained with at least one artificial intelligence (AI) algorithm. Training the 3D image modeling algorithm 108 includes image analysis of training images to configure weights of the 3D image modeling algorithm 108 that are used to predict and / or classify future images. For example, in various embodiments herein, generating the 3D image modeling algorithm 108 includes training the 3D image modeling algorithm 108 with multiple training images of multiple users, each of the training images including pixel data of a skin surface of a respective user. In some embodiments, one or more processors of a server or cloud-based computing platform (e.g., the imaging server 102) may receive the multiple training images of the multiple users via a computer network (e.g., the computer network 120). In such embodiments, the server and / or cloud-based computing platform may train the 3D image modeling algorithm 108 with pixel data of the multiple training images.

[0074] In various embodiments, the machine learning imaging model (e.g., 3D image modeling algorithm 108), as described herein, may be trained using supervised or unsupervised machine learning programs or algorithms. The machine learning programs or algorithms may employ neural networks, which may be convolutional neural networks, deep learning neural networks, or blended learning modules or programs that learn two or more features or feature data sets (e.g., pixel data) within a particular region of interest. These machine learning programs or algorithms may also include natural language processing, semantic analysis, automated reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K-nearest neighbor analysis, naive Bayes analysis, clustering, reinforcement learning, and / or other machine learning algorithms and / or techniques. In some embodiments, the artificial intelligence and / or machine learning based algorithms may be included as libraries or packages that run on the imaging server 102. For example, the libraries may include TENSORFLOW-based libraries, PYTORCH libraries, and / or SCIKIT-LEARN Python libraries.

[0075] Machine learning may include identifying and recognizing patterns in existing data (such as training a model based on pixel data in images having pixel data of a respective user's skin surface) to facilitate making predictions or identifications about subsequent data (such as using the model on new pixel data of a new user to generate a 3D image model of the new user's skin surface).

[0076] Machine learning models, such as the 3D image modeling algorithm 108 described herein for some embodiments, may be created and trained based on inputs or data (which may be referred to as "features" and "labels") of exemplary data (e.g., "training data" and associated pixel data) to make valid and reliable predictions for new inputs, such as test-level or production-level data or inputs. In supervised machine learning, a machine learning program running on a server, computing device, or other processor is provided with exemplary inputs (e.g., "features") and their associated or observed outputs (e.g., "labels"), and the machine learning program or algorithm may determine or discover rules, relationships, patterns, or other machine learning "models" that map such inputs (e.g., "features") to outputs (e.g., labels), for example, by determining and / or assigning weights or other measures for the model across the model's various feature categories. Such rules, relationships, or other models may then be provided with subsequent inputs and the model executed on the server, computing device, or other processor to predict expected outputs based on the discovered rules, relationships, or models.

[0077] In unsupervised machine learning, a server, computing device, or other processor may be required to find its own structure in unlabeled example inputs, where, for example, multiple training iterations are performed by the server, computing device, or other processor to train multiple generations of models until a satisfactory model is generated, e.g., a model that provides sufficient predictive accuracy given test or production level data or inputs. The disclosure herein may use one or both of such supervised or unsupervised machine learning techniques.

[0078] The image analysis may include training a machine learning based algorithm (e.g., 3D image modeling algorithm 108) on pixel data of one or more images of the user's skin surface. Additionally or alternatively, the image analysis may include using a machine learning imaging model, as previously trained, to generate a 3D image model of a particular user's skin surface based on pixel data (e.g., including their RGB values) of one or more images of the user. The weights of the model may be trained through an analysis of various RGB values ​​of the user's pixels of a given image. For example, dark or low RGB values ​​(e.g., pixels having values ​​R=25, G=28, B=31) may indicate relatively low areas of the user's skin surface. Reddish RGB values ​​(e.g., pixels having values ​​R=215, G=90, B=85) may indicate irritated skin. Brighter RGB values ​​(e.g., pixels having R=181, G=170, and B=191) may indicate relatively elevated areas of the user's skin (e.g., acne lesions, etc.). In this manner, pixel data from tens of thousands of training images (e.g., detailing one or more features of a user's skin surface) may be used to train or employ machine learning imaging algorithms to generate a 3D image model of a particular user's skin surface.

[0079] At block 708, the method 700 includes generating, by one or more processors (e.g., the user mobile device 202), a user-specific recommendation based on the 3D image model of the portion of the user's skin. For example, the user-specific recommendation may be a user-specific product recommendation for a manufactured product. Thus, the manufactured product may be designed to address at least one characteristic identifiable within the pixel data of the portion of the user's skin. In some embodiments, the user-specific recommendation recommends that the user apply a product to the portion of the skin or seek medical advice regarding the portion of the skin. For example, if the 3D image modeling algorithm 108 determines that the portion of the user's skin includes characteristics indicative of skin cancer, the 3D image modeling algorithm 108 may generate a user-specific recommendation advising the user to seek immediate medical attention.

[0080] In some embodiments, the user mobile device 202 may capture a second plurality of images of a portion of the user's skin. The camera 212 of the user mobile device 202 may capture images, and each image of the second plurality may be illuminated by a different subset of the LEDs 220. The 3D image modeling algorithm 108 may then generate a second 3D image model that defines a second topographical representation of the portion of the skin based on the second plurality of images. Furthermore, the 3D image modeling algorithm 108 may compare the first 3D image model to the second 3D image model to generate a user-specific recommendation. For example, a user may first capture a first set of images of a skin surface including acne lesions (e.g., as illustrated in FIG. 5A). After several days, the user may capture a second set of images of the skin surface including acne lesions, and the 3D image modeling algorithm may calculate the reduction in the volume / height of the acne lesions over several days by comparing the first set of images with the second set of images. As another example, the 3D image modeling algorithm 108 may compare a first set of images to a second set of images to track roughness measurements of a portion of a user's skin, which may be further applied to track the appearance of wrinkles, moles, etc. over time. Other examples may include tracking micro-undulations in skin lesions (e.g., actinic keratosis lesions as illustrated in FIG. 5B ), skin rashes caused by allergic reactions (e.g., allergy rashes as illustrated in FIG. 5C ), to measure the effectiveness of antihistamines in calming reactions, tracking scars and scar tissue to determine the effectiveness of medications intended to heal the skin surface, tracking / studying chapped lips / skin flakes to measure the effectiveness of lip balms, and / or any other suitable purpose or combination thereof.

[0081] In some embodiments, the user mobile device 202 may execute a mobile application that includes instructions executable by one or more processors of the user mobile device 202. The mobile application may be stored on a non-transitory computer-readable medium of the user mobile device 202. The instructions, when executed by the one or more processors, may cause the one or more processors to render a 3D image model on a display screen of the user mobile device 202. The instructions may further cause the one or more processors to render output on the display screen that textually describes or graphically illustrates features of the 3D image model.

[0082] In some embodiments, the 3D image modeling algorithm 108 may be trained with multiple 3D image models, each depicting a topographical representation of a portion of a skin of a respective user. The 3D image modeling algorithm 108 may be trained to generate user-specific recommendations by analyzing the 3D image model of the portion of skin (e.g., the 3D image model 610). Furthermore, the computing instructions stored on the user mobile device 202, when executed by one or more processors of the device 202, may cause the one or more processors to analyze the 3D image model with the 3D image model to generate user-specific recommendations based on the 3D image model of the portion of skin. The user mobile device 202 may additionally include a display screen configured to receive the 3D image model and render the 3D image model in real time or near real time upon or after the capture of the multiple images by the camera 212.

[0083] As an example of a graphical display, Figure 8 illustrates an exemplary user interface 802 as rendered on a display screen 800 of a user mobile device 202, according to various embodiments disclosed herein. For example, as shown in the example of Figure 8, the user interface 802 may be implemented or rendered via an application (app) running on the user mobile device 202.

[0084] As shown in the example of FIG. 8, the user interface 802 may be implemented or rendered via a native app running on the user mobile device 202. In the example of FIG. 8, the user mobile device 202 is a user computing device as described with respect to FIGS. 1 and 2, e.g., the user computing device 111c1 and the user mobile device 202 are illustrated as an APPLE iPhone implementing the APPLE iOS operating system, and the user mobile device 202 has a display screen 800. The user mobile device 202 may execute one or more native applications (apps) on its operating system. Such native apps may be implemented or coded (e.g., as computing instructions) in a computing language (e.g., SWIFT) executable by the processor of the user mobile device 202 and the user computing device operating system (e.g., APPLE iOS). Additionally or alternatively, the user interface 802 may be implemented or rendered via a web interface, e.g., via a web browser application, e.g., the Safari and / or Google Chrome app, or other such web browsers.

[0085] As shown in the example of FIG. 8, the user interface 802 includes a graphical representation (e.g., a 3D image model 610) of the user's skin. The graphical representation may be a 3D image model 610 of the user's skin surface as generated by the 3D image modeling algorithm 108 as described herein. In the example of FIG. 8, the 3D image model 610 of the user's skin surface may be annotated with one or more graphics (e.g., areas of pixel data 610ap), text renderings, and / or any other suitable renderings, or combinations thereof, that correspond to a topographical representation of the user's skin surface. It should be understood that other graphical / text rendering types or values ​​are contemplated herein, where the text rendering type or value may be rendered, for example, as a roughness measurement of an indicated portion of the skin (e.g., at pixel 610ap2), a change in volume / height of an acne lesion (e.g., at pixel 610ap1), etc. Additionally or alternatively, the color values ​​may be used and / or overlaid on a graphical representation (e.g., the 3D image model 610) shown on the user interface 802 to indicate the topographical characteristics of the user's skin surface (e.g., thermal mapping detailing changes in the topographical characteristics over time).

[0086] Other graphical overlays may include, for example, thermal mapping, where a particular color scheme overlaid on the 3D image model 610 indicates the magnitude or direction of topographical feature movement over time and / or dimensional differences between features in the 3D image model 610 (e.g., height differences between features). The 3D image model 610 may also include text overlays configured to annotate the relative magnitudes and / or directions indicated by the arrows and / or other graphical overlays. For example, the 3D image model 610 may include text such as "sunburn," "acne lesion," "mole," "scar tissue," etc. to describe the features indicated by the arrows and / or other graphical representations. Additionally or alternatively, the 3D image model 610 may include percentage scales or other numerical indicators to supplement the arrows and / or other graphical indicators. For example, the 3D image model 610 may include skin roughness values ​​from 0% to 100%, where 0% represents the minimum skin roughness for a particular skin surface portion and 100% represents the maximum skin roughness for a particular skin surface portion. Values ​​may vary across this map, where a skin roughness value of 67% represents one or more pixels detected in the 3D image model 610 having a higher skin roughness value than a skin roughness value of 10% detected for one or more different pixels in the same 3D image model 610 or a different 3D image model (of the same or a different user and / or portion of skin). Additionally, percentage scales or other numerical indicators may be used internally when the 3D image modeling algorithm 108 determines the size and / or orientation of graphical indicators, text indicators, and / or other indicators or combinations thereof.

[0087] For example, areas of pixel data 610ap may be annotated or overlaid on the 3D image model 610 to highlight areas or features identified in the pixel data (e.g., feature data and / or raw pixel data) by the 3D image modeling algorithm 108. In the example of Fig. 8, features identified in the areas of pixel data 610ap may include skin surface anomalies (e.g., moles, acne lesions, etc.), skin irritations (e.g., allergic reactions), skin type (e.g., estimated age value), skin tone, and other features depicted in the areas of pixel data 610ap. In various embodiments, pixels identified as particular features in the pixel data 610ap (e.g., pixel 610ap1 and pixel 610ap2) may be highlighted or otherwise annotated when rendered.

[0088] The user interface 802 may also include or render user-specific recommendations 812. In the embodiment of FIG. 8, the user-specific recommendations 812 include a message 812m to the user designed to address features identifiable within the pixel data (e.g., pixel data 610ap) of the user's skin surface. As shown in the example of FIG. 8, the message 812m includes a product recommendation for the user to apply a hydrating lotion to moisturize and rejuvenate their skin based on an analysis of the 3D image modeling algorithm 108 that indicated the user's skin surface is dehydrated. The product recommendation may be correlated to the identified feature within the pixel data (e.g., a hydrating lotion to alleviate skin dehydration), and the user mobile device 202 may be instructed to output the product recommendation when a feature (e.g., skin dehydration, sunburn, etc.) is identified. As previously mentioned, the user mobile device 202 may include a recommendation for the user to seek medical treatment / advice in the event that the 3D image modeling algorithm 108 identifies features within the pixel data that indicate a medical condition (e.g., skin cancer) for which the user may need / desire medical opinion.

[0089] The user interface 802 may also include or render a section for a product recommendation 822 for the manufactured product 824r (e.g., a hydrating / moisturizing lotion, as described above). The product recommendation 822 generally corresponds to the user-specific recommendation 12, as described above. For example, in the example of FIG. 8, the user-specific recommendation 812 may be displayed on the display screen 800 of the user mobile device 202 along with instructions (e.g., message 812m) for treating at least one characteristic (e.g., skin dehydration at pixels 610ap1, 610ap2) identifiable in the pixel data (e.g., pixel data 610ap) of the user's skin surface with a manufactured product (manufactured product 824r (e.g., a hydrating / moisturizing lotion)).

[0090] As shown in FIG. 8, the user interface 802 presents a recommendation for a product (e.g., manufactured product 824r (e.g., hydrating / moisturizing lotion)) based on the user-specific recommendation 812. In the example of FIG. 8, the output or analysis of the image (e.g., skin surface image 600) using the 3D image modeling algorithm 108 can be used to generate or identify a recommendation for a corresponding product. Such recommendations can include products such as hydrating / moisturizing lotion, exfoliant, sunscreen, cleanser, shaving gel, etc. to address features detected in the pixel data by the 3D image modeling algorithm 108. In the example of FIG. 4, the user interface 802 renders or provides the recommended product (e.g., manufactured product 824r) as well as the 3D image model 610 and its associated image analysis of its pixel data and various features as determined by the 3D image modeling algorithm 108. In the example of FIG. 8, this is shown and annotated (824p) on the user interface 802.

[0091] The user interface 802 may further include selectable UI buttons 824s that allow the user to select a corresponding product (e.g., manufactured product 824r) for purchase or shipping. In some embodiments, selection of the selectable UI button 824s may cause the recommended product to be shipped to the user and / or may notify a third party that the user is interested in the product. For example, either the user mobile device 202 and / or the imaging server 102 may initiate a manufactured product 824r (e.g., a hydrating / moisturizing lotion) for shipment to the user based on the user-specific recommendation 812. In such an embodiment, the product may be packaged and shipped to the user.

[0092] In various embodiments, the graphical representation (e.g., the 3D image model 610) with the graphical annotations (e.g., areas of pixel data 610ap) and the user-specific recommendations 812 may be transmitted over a computer network (e.g., from the imaging server 102 and / or one or more processors) to the user mobile device 202 for rendering on the display screen 800. In other embodiments, no transmission of user-specific images to the imaging server 102 occurs, and the user-specific recommendations (and / or product-specific recommendations) may instead be generated locally by a 3D image modeling algorithm 108 executed and / or implemented on the user mobile device 202 and rendered on the display screen 800 of the user mobile device 202 by a processor of the mobile device.

[0093] In some embodiments, as shown in the example of FIG. 8, the user may select a selectable button 812i to reanalyze a new image (e.g., locally at the user mobile device 202 or remotely at the imaging server 102). The selectable button 812i may cause the user interface 802 to prompt the user to position the combination of the user mobile device 202 and the dermatological imaging device 110 on the user's skin surface to capture a new image and / or for the user to select a new image for upload. The user mobile device 202 and / or the imaging server 102 may receive the user's new image before, during, and / or after implementing some or all of the treatment options / suggestions presented in the user-specific recommendation 812. The new image (e.g., just like the skin surface image 600) may include pixel data of the user's skin surface. The 3D image modeling algorithm 108 executing on the memory of the user mobile device 202 may analyze the new image captured by the combination of the user mobile device 202 and the dermatological imaging device 110 to generate a new 3D image model of the user's skin surface. The user mobile device 202 may generate a new user-specific recommendation or comment based on the new 3D image model regarding features identifiable within the pixel data of the new 3D image model. For example, the new user-specific recommendation may include a new graphical representation including graphics and / or text. The new user-specific recommendation may include additional recommendations, such as that the user should continue to apply the recommended product to reduce swelling associated with a portion of the skin surface, that the user should utilize the recommended product to eliminate any allergic rash, that the user should apply sunscreen before exposing the skin surface to sunlight to avoid worsening of a current sunburn, etc. The comment may include that the user has modified at least one feature identifiable within the pixel data (e.g., the user has little or no skin irritation after applying the recommended product).

[0094] In some embodiments, the new user-specific recommendations or comments may be transmitted over a computer network to the user's user mobile device 202 for rendering on the display screen 800 of the user mobile device 202. In other embodiments, no transmission of the user's new images to the imaging server 102 occurs, and the new user-specific recommendations (and / or product-specific recommendations) may instead be generated locally by the 3D image modeling algorithm 108 running and / or implemented on the user mobile device 202 and rendered on the display screen 800 of the user mobile device 202 by a processor of the user mobile device 202.

[0095] Additionally, certain embodiments are described herein as including logic or some routines, subroutines, applications, or instructions. These may be constructed as either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc. are tangible units capable of performing certain operations and may be configured or arranged in a particular manner. In an exemplary embodiment, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware modules (e.g., processors or groups of processors) of a computer system may be configured by software (e.g., applications or application portions) as hardware modules that operate to perform certain operations as described herein.

[0096] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the associated operations. Such processors, whether temporarily or permanently configured, may form processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may, in some example embodiments, include processor-implemented modules.

[0097] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. Certain performance of those operations may be distributed to one or more processors and may be spread across multiple machines as well as being present within a single machine. In some exemplary embodiments, the processor(s) may be located in a single location, whereas in other embodiments, the processor may be distributed across several locations.

[0098] Certain performance of those operations may be distributed to one or more processors and may reside within a single machine as well as be spread across multiple machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other embodiments, one or more processors or processor-implemented modules may be distributed across multiple geographic locations.

[0099] Dimensions and values ​​disclosed herein should not be understood as being strictly limited to the exact numerical values ​​recited. Instead, unless otherwise indicated, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as "35 mm" is intended to mean "about 35 mm."

[0100] All documents cited herein, including any cross-referenced or related patents or patent applications, and any patent applications or patents to which this application claims priority or benefit, are incorporated herein by reference in their entirety, unless expressly stated to the contrary. The citation of any document shall not be deemed to be prior art to any invention disclosed or claimed herein, or to teach, suggest or disclose any such invention, either alone or in combination with any other reference(s). Furthermore, to the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition given to that term in this document shall govern.

[0101] While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims

1. 1. A dermatological imaging system configured to generate a three-dimensional (3D) image model of a skin surface, the dermatological imaging system comprising: a) a dermatological imaging device, comprising: i) a plurality of light emitting diodes (LEDs) configured to be positioned about a portion of a user's skin; ii) one or more lenses configured to focus on a portion of the skin; and b) a computer application (app) that, when executed on a processor, causes the processor to: i) analyzing a plurality of images of the portion of the skin, the plurality of images being captured by a camera having an imaging axis extending through the one or more lenses, each image of the plurality of images being illuminated by a different subset of the plurality of LEDs; ii) generating a 3D image model defining a topographical representation of the portion of the skin based on the plurality of images; Each of the plurality of images is captured by the camera at an imaging distance of 35 mm or less; The camera captures the plurality of images during a video capture sequence, and each different subset of the plurality of LEDs is sequentially activated and sequentially deactivated during the video capture sequence, and the computing instructions, when executed by the processor, cause the processor to: computing an average pixel intensity for each image of the plurality of images; matching each of the plurality of images with a respective maximum average pixel intensity.

2. 2. The dermatological imaging system of claim 1, wherein the illumination provided by each different subset of the plurality of LEDs illuminates the portion of the skin from a different illumination angle, and each image of the plurality of images is characterized by a different set of shadows cast on the portion of the skin as a result of the illumination from the different illumination angles.

3. 3. The dermatological imaging system of claim 1 or 2, wherein the computer application includes computing instructions that, when executed on a processor, cause the processor to further compare the 3D image model with at least one other 3D image model defining another topographical representation of a portion of the skin of a second user, the second user sharing an age or skin condition with the user.

4. The dermatological imaging system of any one of claims 1 to 3, wherein the computer application comprises computing instructions that, when executed on a processor, further cause the processor to determine that the 3D image model defines a topographical representation corresponding to the skin of a set of users having a skin type class.

5. The dermatological imaging system of claim 1 , wherein the plurality of LEDs and the camera are controlled asynchronously by the computer application during the video capture sequence.

6. one or more processors; one or more memories communicatively coupled to the one or more processors; an imaging model trained with a plurality of 3D image models, each depicting a topographical representation of a portion of a skin of a respective user, the imaging model being trained to generate user-specific recommendations by analyzing the 3D image models of the portions of the skin; 6. The dermatological imaging system of claim 1, further comprising computing instructions executable by the one or more processors and stored in the one or more memories, the computing instructions, when executed by the one or more processors, causing the one or more processors to analyze the 3D image model with the imaging model so as to generate the user-specific recommendation based on the 3D image model of the portion of the skin.

7. The dermatological imaging system of any one of claims 1 to 6, wherein the dermatological imaging system is configured to generate user-specific recommendations based on the 3D image model of the portion of the skin.

8. The plurality of images is a first plurality of images, the 3D image model is a first 3D image model, and the topographical representation of the portion of the skin is a first topographical representation of the portion of the skin, and generating a user specific recommendation includes: a) analyzing with the app a second plurality of images of the portion of the skin captured by the camera, each image of the second plurality of images being illuminated by a different subset of the plurality of LEDs; 2. The dermatological imaging system of claim 1, further comprising: b) generating a second 3D image model defining a second topographical representation of the portion of the skin based on the second plurality of images.

9. 9. The dermatological imaging system of claim 1, wherein at least a different subset of the plurality of LEDs illuminates the portion of the skin with a first illumination intensity and wherein at least a different subset of the plurality of LEDs illuminates the portion of the skin with a second illumination intensity different from the first illumination intensity.

10. The dermatological imaging system of any one of claims 1 to 9, wherein the camera is calibrated using a random sampling consensus algorithm configured to select one or more ideal images from a video capture sequence of a calibration plate.

11. 11. The dermatological imaging system of claim 1, wherein analyzing the multiple images of the portion of the skin of the user further comprises estimating, by the one or more processors, a probabilistic cone of illumination corresponding to each image of the multiple images.

12. 12. The dermatological imaging system of claim 1, wherein each of the plurality of LEDs is calibrated by path tracing one or more rays of light reflected from a plurality of reflective objects using the one or more processors.

13. A method of operating a dermatological imaging system as described in any one of claims 1 to 12, comprising analyzing, by one or more processors, a plurality of images of a portion of a user's skin, and generating a three-dimensional (3D) image model of the skin surface based on the plurality of images.

Images