SYSTEM AND METHOD FOR RECOGNITION OF HEALTH CONDITIONS ACCORDING TO IMAGE PROCESSING

Disclosed herein is a method for identifying a health condition, comprising using at least one hardware processor for obtaining at least one image of tissue, identify edges of objects and of borders between colors in the at least one image, determine a threshold, divide images into a predetermined number of sections, juxtapose the predetermined number of sections in a non-contiguous manner, designate quadrants and rings on image, identify markers indicating detection of a health condition, generating a notification of identifying the health condition, and providing the notification to an output device.

FIELD OF THE INVENTION

The present disclosure generally relates to the field of health conditions based on image processing of lesions.

BACKGROUND

Skin is the body's largest organ protecting internal organs from the external environment. Like the canary in the mine, skin can reflect internal health status through the manifestation of changes to the skin surface which are observable and trackable. For example, changes in the skin can serve as early indicators of Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease and related Dementias.

Skin cancers are characterized by the abnormal growth of cells and are an ongoing medical concern for people around the world. One of the major issues is the impact of skin pigmentation on the accurate diagnosis of skin cancers, i.e., skin types of the patients. The incidence of the different types of skin cancer varies by skin pigmentation type. Melanoma, the most deadly form of skin cancer, tends to affect lighter skin types (Fitzsimmon Scale Type III or below) with Australia, New Zealand, European (Denmark, Netherlands, Switzerland), and Nordic (Sweden, Norway, Finland) populations consistently ranking at the top of the list. If detected early, melanoma and other skin cancers, such as Basal Cell Carcinoma (“BCC”) and Squamous Cell Carcinoma (“SCC”), along with Actinic Keratosis—considered to be a SCC precursor—are highly treatable.

Systems and methods for creating an image and/or automatically interpreting images are disclosed in U.S. Pat. Nos. 11,158,060, 11,176,675, which in their entirety are incorporated herein by reference.

Systems and methods for generating composite images are disclosed U.S. Pat. No. 10,582,189, which in its entirety is incorporated herein by reference.

A multi-purpose interactive cognitive platform is disclosed in U.S. Pat. Nos. 11,328,822, 11,298,062, which in their entirety are incorporated herein by reference.

SUMMARY

There is provided, in accordance with an embodiment, a method for identifying a health condition, including using one or more hardware processors for obtaining one or more images of tissue, identify edges of objects and of borders between colors in the one or more images, determine a threshold, divide images into a predetermined number of sections, juxtapose the predetermined number of sections in a non-contiguous manner, designate quadrants and rings on image, identify markers indicating detection of a health condition, generating a notification of identifying the health condition, and providing the notification to an output device.

In some embodiments, the processor is further configured to obtain a plurality of images, and reverse engineer the plurality of images to identify markers indicating the health condition at a previous point in time.

In some embodiments, the spatial landmarks and relationships are conserved between image parts, across the image, and between images for making comparisons over time.

In some embodiments, the processor is further configured to approximate edges thereby facilitating edge-to-edge symmetry, border regularity, color and diameter comparisons in the image.

In some embodiments, the designation of quadrants and rings introduces an artificial vertical edge—a discrete line of pixels at the interface that serves as a landmark for making edge-to-edge and quadrant comparisons of lesion parts.

In some embodiments, the processor is further configured to modify colors of the image to enhance the characteristics of the image.

In some embodiments, modifying colors reduces the color composition of the image.

There is further provided, in accordance with an embodiment, a system for identifying a health condition, including one or more servers configured to provide one or more images of a tissue, a client device including a user interface having one or more input devices and one or more output devices, and one or more processors configured to obtaining one or more images of tissue, identify edges of objects and of borders between colors in the one or more images, determine a threshold, divide the one or more images into a predetermined number of sections, juxtapose the predetermined number of sections in a non-contiguous manner, designate quadrants and rings on image, identify markers indicating detection of a health condition, generating a notification of identifying the health condition, and providing the notification to the one or more output device.

In some embodiments, the one or more processors are further configured to, obtain a plurality of images, reverse engineer the plurality of images to identify markers indicating the health condition at a previous point in time.

In some embodiments, spatial landmarks and relationships are conserved between image parts, across the image, and between images for making comparisons over time.

In some embodiments, the one or more processors are further configured to approximate edges in the image thereby facilitating edge-to-edge symmetry, border regularity, color and diameter comparisons in the image.

In some embodiments, the designation of quadrants and rings introduces an artificial vertical edge—a discrete line of pixels at the interface that serves as a landmark for making edge-to-edge and quadrant comparisons of lesion parts.

In some embodiments, the one or more processors are further configured to further configured to modify colors of the image to enhance the characteristics of the image.

In some embodiments, modifying colors reduces the color composition of the image.

There is further provided, in accordance with an embodiment, a computer program product for identifying a health condition, the computer program product including a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by one or more hardware processors to obtain at one or more images of tissue, identify edges of objects and of borders between colors in the one or more images, determine a threshold, divide images into a predetermined number of sections, juxtapose the predetermined number of sections in a non-contiguous manner, designate quadrants and rings on image, identify markers indicating detection of a health condition, generate a notification of identifying the health condition, provide the notification to an output device.

In some embodiments, the computer program product is further configured to obtain a plurality of images, reverse engineer the plurality of images to identify markers indicating the health condition at a previous point in time.

In some embodiments, the spatial landmarks and relationships are conserved between image parts, across the image, and between images for making comparisons over time.

In some embodiments, the computer program product is further configured to approximate edges thereby facilitating edge-to-edge symmetry, border regularity, color, and diameter comparisons in the image.

In some embodiments, the designation of quadrants and rings introduces an artificial vertical edge—a discrete line of pixels at the interface that serves as a landmark for making edge-to-edge and quadrant comparisons of lesion parts.

In some embodiments, the computer program product is further configured to modify colors of the image to enhance characteristics of the image.

DETAILED DESCRIPTION

A general non-limiting overview of practicing the present disclosure is presented below. The overview outlines the exemplary practice of embodiments of the present disclosure, providing a constructive basis for variant and/or alternative and/or divergent embodiments, some of which are subsequently described.

Described herein is a system and method for recognition of health conditions based on the processing of images of tissue, according to certain exemplary embodiments.

Skin pigmentation can serve as an important metric for detecting changes in skin health with pigmentation features associated with the interior of a lesion, around its edge, and relative to a background skin pigmentation. In developing strategies for analyzing skin features, traditional augmentation methods used for non-medical images can create authenticity issues, introducing hallucinatory and/or unrealistic representations. A subset of augmentation methods can be used which do not change pixel-level information such as rotation, flip, and image resizing. Similarly, pre-processing image filters methods used to highlight previously recognized or known Regions of Interest (“ROI”) can compromise input data by extracting seemingly “unnecessary” information; in this case enhancing lesion features but at the expense of background skin pigmentation information.

Furthermore, image datasets can have qualitative and quantitative issues, in part related to image sourcing and annotations. For example, an applied bias in annotations that follow traditional methods for evaluating lesions and elaborating through rule-in/rule-out differential algorithms applying Asymmetry, Border, Color, Diameter (“ABCD”) features within defined and variable thresholds but which are geared toward diagnosis and not early detection. While class-balancing can serve to equalize under-represented lesion categories, class balancing doesn't necessarily add true diversity to challenging atypical lesions, pigmented lesions on darker pigmented skin types, hypo and amelanotic lesions across all skin types, as well as historical images showing lesion progression which can help advance skin lesion analysis and our understanding of lesion progression.

From a health professional's perspective, lesion analysis combines both art and science—a “hands-on and minds-on” determination, even if initially made remotely through live telehealth consultations or store and forward interactions.

The utilization of artificial intelligence analysis can facilitate determining the health of certain tissues, such as skin health, as well as internal organs, such as the brain. Furthermore, applying computer vision methods can serve as an adjunct in supporting end-user consumers with early detection tools, and providers with analytical tools to track subtle changes over time thereby identifying lesion nuances and advancing predictive analytics in the field.

As will be described through certain exemplary embodiments, the system and method disclosed herein provide an agile image-based solution, by integrating image manipulation methods to: 1) support early detection of skin health changes by advancing logic-leaping pattern generalization and inference capabilities for image parts recognition; and, 2) develop an innovative generative adversarial model for identifying previously unrecognized predictive biomarkers of early/prodromal stage changes in skin health.

The system and method disclosed herein execute computer vision capabilities, which transform unseen data making unknown information known information thereby advancing predictive capabilities towards identifying skin health change biomarkers. Skin, the body's largest organ, while possessing the same fundamental structure is nonetheless unique in its external appearance and manifesting in health status changes over time, both within the same individual and across different populations. Qualitative and quantitatively useful dermatopathology datasets containing historical images of disease/condition progression are not always available, easily obtained, or may express highly individualized change pattern characteristics, including Asymmetry, Border, Color, Diameter, and Evolution/elevation (“ABCDE”) features in cancerous lesion patterns. Datasets can be biased, lacking an authentic diversity representative of real-world medical images able to support differential diagnosis or medical decision-making, contributing to a further exacerbation of health disparity issues. To address these challenges, the system and method disclosed herein present image processing operations for improving visual attention of image parts by manipulating and interrogating embedded image Gestalt characteristics, according to certain exemplary embodiments.

In some embodiments, the system and methods disclosed herein allow for the differentiation of lesion features which can be visualized using image enhancement filters with dermoscopy obtained polarized and non-polarized light images; evaluate lesions with sequential color-reductions that preserve interior lesion-edge-background pigmentation features; the identification of Lesion Factors (LF: (ABCDEF&G, primary/secondary morphologies, texture, location distribution, color, and other pattern characteristics) for developing lesion categories; visual correlate between LF and five (5 putative, characteristics-based lesion categories; and, knowledge transfer of “figure” attributes/edge contiguity characteristics in non-medical images.

Through the execution of Stitch and Peel operations which juxtapose non-adjacent image sections, logically pre-pooling and reducing image pixels across an image, computer visual attention can be focused on key image features, including edge characteristics to interrogate lesion ABCDE and pattern features with expert-level questions to improve recognition accuracy in affected tissue. Image manipulation tools will be used to cooperatively: 1) advance our understanding of parts recognition and parts-of-the-whole feature extraction by leveraging an image's Gestalt features; 2) develop skin lesion categories based on image characteristics, rather than diagnostic criteria or annotations; and, 3) design stitched chimeric constructs as source/target images in a GANs model for generating putative “known/unknown unknowns” biomarker change candidates.

In certain embodiments, these digital pathology tools are used to manipulate and evaluate image Gestalt characteristics, introducing top-down, supra-level changes designed to improve imaging inputs for feature extraction and image analysis. Ideally, by enriching the quality and quantity of lesion characteristics available for analysis, the methods will help deepen understanding of lesion progression and be able to focus attention on key Regions of Interest and hallmark changes/progressions in identifying early/earlier-stage skin health change biomarkers. In developing a new diagnostic-independent, features/characteristics-based classification system, the invention can re-define skin lesion characteristics and transform AI features' extraction and pattern analysis capabilities, giving consumer and provider stakeholders advanced visualization and early detection/early-warning tools to assess any skin lesion.

In some embodiments, an image of a lesion and/or image containing a “Region of Interest” of tissue is stitched and/or peeled, and the part and/or parts are assessed based on the image that was stitched and/or peeled. Stitching methods are configured to facilitate interior to edge comparisons, edge-to-edge analysis and figure element assessments relative to the ground elements, using embedded or constructed symmetry of the lesions, reducing pixel content while retaining spatial contexts. Lesions can be broadly defined as disruptions to the integrity of the tissue in which it is found and can constitute single cells, cell clusters, solid tumors and/or cancerous and/or benign growths regardless of location. In some embodiments, a figure position or ground position within an image of a lesion are determined, and the lesion is assessed based on the figure characteristics and/or ground characteristics determined and or their relative positions. Alternatively or additionally, the images of the lesion may be analyzed for color blocks and/or edges and/or horizon-type contiguities, analyzing and/or comparing interior, edge and background (ground) features which can be used to assess a lesion.

FIG.2schematically illustrates a system200configured for recognizing abnormalities in tissue and providing an indication of a health condition, according to certain exemplary embodiments. For example, system200may analyze images, and apply image manipulation modules to enhance and expedite recognition of abnormal tissue. The image analysis can include leveraging embedded multiple Gestalt principles, (figure-ground, closure, continuation), engaging top-down cognition and bottom-up sensory processing, to virtually reassemble portions of one or more images through virtual reconstruction of the intact image and of the image portion occupying the ground position.

System200can include a computerized device205communicating with one or more servers220illustrated as three instances of servers220, representing any number of servers220, as indicated by dashed line230. In some embodiments, computerized device205can be a smartphone, a laptop, a desktop, a tablet, or the like. Computerized device205is connected to a network215by any communication facility or facilities included in system200as schematically illustrated by arrow210. Servers220are connected to network215by any communication facility or facilities included in system200as illustrated by arrow225. Communication facilities210,225facilitate communication between computerized device205and servers220.

FIG.3schematically illustrates computerized device205, according to certain exemplary embodiments. Computerized device205includes one or more processors300, one or more communication interfaces318, computer device memory320and one or more communication buses302for interconnecting these components. One or more communication interfaces318can include a wired and/or wireless interface for receiving data from and/or transmitting data to one or more servers220(FIG.2).

In some implementations, data communications are carried out using any of a variety of custom or standard wireless protocols (e.g., NFC, RFID, IEEE 802.15.4, Wi-Fi, ZigBee, 6LoWPAN, Thread, Z-Wave, Bluetooth, ISA100.11a, WirelessHART, MiWi, etc.). Furthermore, in some implementations, data communications are carried out using any of a variety of custom or standard wired protocols (e.g., USB, Firewire, Ethernet, etc.). For example, the one or more communication interfaces318include a wireless interface for enabling wireless data communications with servers220and/or other wireless (e.g., Bluetooth-compatible) devices. Furthermore, in some implementations, the wireless interface (or a different communications interface of the one or more communication interfaces318) enables data communications with other WLAN-compatible devices.

Computer device memory320includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Computer device memory320may optionally include one or more storage devices remotely located from the processor300. Computer device memory320, or alternately, the non-volatile memory within computer device memory320, includes a non-transitory computer-readable storage medium. In some implementations, computer device memory320or the non-transitory computer-readable storage medium of computer device memory320stores the following programs, modules, and data structures, or a subset or superset thereof:

an operating system322that includes procedures for handling various basic system services and for performing hardware-dependent tasks;

an edge identification module325to identify an edge or border region surrounding a portion of tissue of interest that is to be isolated and processed. For example, identifying the edges of a skin abnormality in an image.

a threshold module328for determining a threshold of color difference between the tissue and the surrounding skin. In some embodiments, threshold module335is executed concurrently edge identification module325

a color map module330to modify colors of the image or to identify different color pixels in the image.

a segmentation module332to segment and divide the image according to pixel density differences in the images. Segmentation module332is configured to match a segmented image with an unsegmented image thereby isolating the tissue portion of interest. Segmentation module332is configured to split the isolated image into a predetermined number of sections, such as three sections.

a juxtaposition module335to juxtapose the predetermined number of sections thereby reducing the size of the image that has to be analyzed by processor305.

an ambiguity module338to enlarge and enhance an image for representation purposes and to facilitate determining the characteristics of the tissue. In some embodiments ambiguity module338may execute a Sobel edge analysis and invert the colors to facilitate detecting internal asymmetries in the predetermined segments.

a stitch and peel module340to split the isolated image into quadrants and apply equally spaced, concentric rings to the isolated image thereby allowing for tissue edge comparisons of borders, color, and other relevant characteristics. In some embodiments, multiple comparative images can be made between concentric rings and quadrants for color and content identity and differences. In some embodiments, sub-quadrants, two-dimensional and three-dimensional, measurements can be recorded to detect subtle changes in the tissue.

a reverse engineering module350to facilitate identifying tissue features at earlier points in time that indicate a health condition to ensure faster treatment. Reverse engineering module350enables obtaining lesion characteristics for identifying earlier-stage biomarkers based on GANs chimera-generated candidates model and identification of differentiated features and which can be matched to the detection of early-stage biomarkers, when available. In developing robust image categories based on lesion features with ranged value correlates, the illustrations-developed categories, as illustrated inFIG.1, can be matched to “wild” lesions and be used to identify outliers and previously unseen/unidentified image category patterns. In some embodiments, reverse engineering module350can obtain images of other tissues having the characteristics of the health condition from which processor300can generate an algorithm for identifying a health condition of a tissue, for example, a lesion on skin. Thereby, reverse engineering module350can roll back the early detection timeline at which images may be available of the tissue to facilitate identifying unknown biomarkers of disease and improving health outcomes with new research and design discoveries.

In some embodiments, where historical and/or progression images of tissue are unavailable whether from the same patients, multiple patients, in a dataset, and/or to build potential early-stage and/or progression characteristics, a generative model such as StyleGans may be used. Chimeric and/or half-constructs can be used as source and/or targets for generative modeling, and/or using a features-driven tool for effecting targeted changes to individual attributes. In some embodiments, chimeric stitched constructs would combine different percentages of Normal (N) and Abnormal (Abn) image parts (source image), from the same or different categories/clusters and with the reversed percentages in target images (0.7N:0.3Abn→0.3N:0.7Abn). In some embodiments, the chimera construct can include a gap, an uneven percentage size between the source and the target (Source 0.6N:---0.3Abn→Target 0.3N:0.7Abn) to generate additional output images with different transition image attributes.

In reverse engineering tissue maladies to their point of origin, progenitor cells might be classed into two general categories: 1) those emerging from an existing skin feature such as a mole, scar, nevus, irregular patch, and pigmented area; and, 2) lesions appearing in areas without any noticeable and/or visual manifestation. The underlying dermis and the interplay between other skin structures, specifically melanosomes which show different expression and distribution patterns based on skin pigmentation types among other factors that can provide insights into skin integrity issues, cancer development, and systems failure and product defects using new detection tools and devices as knowledge continues to evolve.

FIG.4schematically illustrates server220, according to certain exemplary embodiments. Server220includes one or more processors400, one or more communication interfaces405, server memory410and one or more communication buses402for interconnecting these components. One or more communication interfaces405can include a wired and/or wireless interface for receiving data from and/or transmitting data to computerized device205.

In some implementations, data communications are carried out using any of a variety of custom or standard wireless protocols (e.g., NFC, RFID, IEEE 802.15.4, Wi-Fi, ZigBee, 6LoWPAN, Thread, Z-Wave, Bluetooth, ISA100.11a, WirelessHART, MiWi, etc.). Furthermore, in some implementations, data communications are carried out using any of a variety of custom or standard wired protocols (e.g., USB, Firewire, Ethernet, etc.). For example, the one or more communication interfaces405include a wireless interface for enabling wireless data communications with computerized device205and/or or other wireless (e.g., Bluetooth-compatible) devices. Furthermore, in some implementations, the wireless interface (or a different communications interface of the one or more communication interfaces405) enables data communications with other WLAN-compatible devices.

Server memory410includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Server memory410may optionally include one or more storage devices remotely located from the processor400. Server memory410, or alternately, the non-volatile memory within server memory410, includes a non-transitory computer-readable storage medium. In some implementations, server memory410or the non-transitory computer-readable storage medium of server memory410stores the following programs, modules, and data structures, or a subset or superset thereof:

an operating system415that includes procedures for handling various basic system services and for performing hardware-dependent tasks.

an edge identification module418to identify an edge or border region surrounding a portion of tissue of interest that is to be isolated and processed. For example, identifying the edges of a skin abnormality in an image.

a threshold module420for determining a threshold of color difference between the tissue and the surrounding skin. In some embodiments, threshold module420is executed concurrently edge identification module418

a color map module423to modify colors of the image or to identify different color pixels in the image.

a segmentation module425to segment the image based on a pixel density difference in the images. Segmentation module425is configured to match a segmented image with an unsegmented image thereby isolating the tissue portion. Segmentation module425is configured to split the isolated image into a predetermined number of sections, such as three sections.

a juxtaposition module428to juxtapose the predetermined number of sections thereby reducing the size of the image that has to be analyzed by processor305.

an ambiguity module430to enlarge and enhance an image for representation purposes and to facilitate determining the characteristics of the tissue. In some embodiments ambiguity module430may execute a Sobel edge analysis and invert the colors to facilitate detecting internal asymmetries in the predetermined segments.

a stitch and peel module435to designate quadrants and equally spaced, concentric rings to the isolated image thereby allowing for tissue edge comparisons of borders, color, and other relevant characteristics. In some embodiments, multiple comparative images can be made between concentric rings and quadrants for color and content identity and differences. In some embodiments, sub-quadrants, two-dimensional and three-dimensional, measurements to detect subtle changes in the tissue.

a reverse engineering module438to facilitate identifying tissue features at earlier points in time that indicate a health condition to ensure faster treatment. Reverse engineering module438enables obtaining lesion characteristics for identifying earlier-stage biomarkers based on GANs chimera-generated candidates model and identification of differentiated features and which can be matched to the detection of early-stage biomarkers, when available. In developing robust image categories based on lesion features with ranged value correlates, the illustrations-developed categories can be matched to “wild” lesions and be used to identify outliers and unseen image category patterns. In some embodiments, reverse engineering module438can obtain images of other tissues having the characteristics of the health condition from which processor400can generate an algorithm for identifying a health condition of a tissue, for example, a skin lesion. Thereby, reverse engineering module438can roll back the early detection timeline at which images of the tissue may be available to facilitate identifying unknown biomarkers of disease and improving health outcomes with new research and design discoveries.

FIG.5outlines operations of a method for image analysis to recognize a lesion, according to certain exemplary embodiments. In operation500, processor300or400obtains an image.

In operation505, processor300or400analyzes the image to identify a condition in a tissue, for example, identifying a skin lesion. Operation505includes multiples operations as follows:

In operation510, processor300or400identifies edges of an object in an image. In some embodiments, Gestalt image analysis of tissue facilitates identifying edges and borders of objects in the image. Processor300or400identifies image characteristics and an understanding of the multiplicity of interactions and the hierarchical relationship of image parts within images and between images. Recognizing and understanding the role and contribution of an image's contiguity features—edges, horizons, and color blocks—to figure (foreground) and ground (background) dynamics and depth perception is applicable to both human cognition, computer vision, and two-dimensional (“2D”) to three-dimensional (“3D”) environment translation.

In operation515, processor300or400determine a threshold for an image as shown inFIG.8A. Image datasets can have qualitative and quantitative issues, in part related to image sourcing and annotations—an applied bias that follows traditional methods for evaluating lesions and elaborating through rule-in/rule-out differential algorithms applying ABCD features within defined and variable thresholds but which are geared toward diagnosis and not early detection. While class-balancing can serve to equalize under-represented lesion categories, class balancing doesn't necessarily add true diversity to challenging atypical lesions, pigmented lesions on darker pigmented skin types, hypo and amelanotic lesions across all skin types, as well as historical images showing lesion progression which can help advance skin lesion analysis and identifying lesion progression.

In operation520, processor300or400modify the colors in the image thereby emphasizing borders and different regions in the image as shown inFIGS.7A-7C. In some embodiments, the image obtained in step500is of low quality or has distortions, the colors of the image can be modified by making the image darker or brighter to enhance regions of the image.

In operation525, processor300or400segments the image into predetermined sections, such as into three sections as shown inFIG.8D.

In some embodiments, as shown inFIGS.7B-7Clesion measurements can include an image segmentation step with a reduction in color composition to 4, 6, 8, 10, or more colors but less than the original number of colors represented in the lesion, depending on the complexity of the lesion, and which can be followed by treatment of the image with or without additional edge filters such as Sobel. In one embodiment color reduction can be used to compare lesion features while conserving background pigmentation characteristics in the area surrounding the lesion. In one embodiment, the color-reduced images can be used to evaluate lesion size, edge characteristics, and lesion coherency measured against norms and developed ranges for progressed lesions, wounds, or other presentations. In one embodiment, stitching can be applied to the image to facilitate size, edge, color, and coherency assessments where stitching can be left to right; right to left, top to bottom, bottom to top, or performed across one or more diagonals to juxtapose different combinations of image parts for point-in-time and change comparisons. Comparisons over time and/or between lesions can be presented as a series of color-reduced overlays, aligning point-in-time images, or tracking changes over time.

In operation530, processor300or400juxtaposes the segments as shown inFIG.8E. The segments are juxtaposed such that non-contiguous regions of the image are approximated thereby reducing the size of the image and the details of the image that have to be analyzed by processor300or400and to logically pre-pool pixel content, facilitating pan-lesion characteristics analysis. This facilitates creating an internal crop that logically punctuates an image, juxtaposing non-contiguous regions to interrupt an image's “native” content fluency and structural confluency. The stitch introduces an artificial vertical edge—a discrete line of pixels at the interface that serves as a landmark for making edge-to-edge and quadrant comparisons across the stitch and refining ABCDE lesion analysis. Spatial landmarks and relationships are conserved between image parts, across the image, and between images for making comparisons over time. Stitched image constructs can be generated with a single step or multiple stitches and peel steps, and can be vertical (left to right) or horizontal (top to bottom), approximating edges and facilitating edge-to-edge symmetry, regularity, color, diameter (ABCD features) comparisons and tracking of changes over time. In some embodiments, ideal Sn—P constructs may efficiently resolve contiguity disruptions to a PixelParts(min)—an endpoint requiring minimal touchpoint preprocessing; not requiring directed training with new image sets; and, enhancing computer vision-related tasks without compromising spatial contexts. Further, multiple Sn—P constructs could cooperatively be used across image scenes with smaller and/or larger peels for developing greater overall scene understanding effectively doing more with less.

In operation535, processor300or400enhance the image to facilitate identification, visualization, and analytical capabilities with detailed images as shown inFIGS.9A-9B. In some embodiments, a non-polarized image906is enhanced with polarizing light900. In some embodiments the images are processed with a white balance filter as shown in images902,904. In some embodiments, the image is processed using a Reitnex filter as shown in images904and908. In some embodiments, an original image912is processed using a Retinex filter to achieve a halftone image914and a full-color image916which are then processed using a sandboxed computer vision model. For example, when original image912is analyzed by processor300or400, it may be considered to show a spider rather than a spot on the skin, whereas the enhancement shown in images914,916processor300or400can identify the spot as being part of skin.

In operation540, processor300or400designate quadrants and rings as shown inFIGS.81-8J.

In operation550, processor300or400generates a notification of whether a condition has been detected in the tissue according to the image. In some embodiments, the notification is the image with markings showing the identified portions that may be problematic. In some embodiments, the notification can be a message stating that a health condition is present.

In operation555, processor300or400provides notification to output devices315(FIG.3).

FIG.6outlines operations of a method for image analysis of a set of images to reverse engineer lesion characteristics, according to certain exemplary embodiments. In operation600, processor300or400obtains one or more images. The images can include images of a tissue, such as a skin lesion over a predetermined window of time. In operation605, processor300or400analyzes the image to identify a condition in a tissue, for example, identifying a lesion on skin. Operation605includes operations610,615,620,625,630,635,640,645and655are performed, on the images, which correspond to operations510,515,520,525,530,535,540,550ofFIG.5. Operation605furthermore includes operation650, in which processor300or400reverse engineers the images to detect whether an indication presented itself at a previous time or an earlier timepoint when an image was taken.

In the context of some embodiments of the present disclosure, by way of example and without limiting, terms such as ‘operating’ or ‘executing’ imply also capabilities, such as ‘operable’ or ‘executable’, respectively.

Conjugated terms such as, by way of example, ‘a thing property’ implies a property of the thing, unless otherwise clearly evident from the context thereof.

The terms ‘processor’ or ‘computer’, or system thereof, are used herein as ordinary context of the art, such as a general purpose processor or a micro-processor, RISC processor, or DSP, possibly including additional elements such as memory or communication ports. Optionally or additionally, the terms ‘processor’ or ‘computer’ or derivatives thereof denote an apparatus that is capable of carrying out a provided or an incorporated program and/or is capable of controlling and/or accessing data storage apparatus and/or other apparatus such as input and output ports. The terms ‘processor’ or ‘computer’ denote also a plurality of processors or computers connected, and/or linked and/or otherwise communicating, possibly sharing one or more other resources such as a memory.

The terms ‘software’, ‘program’, ‘software procedure’ or ‘procedure’ or ‘software code’ or ‘code’ or ‘application’ may be used interchangeably according to the context thereof, and denote one or more instructions or directives or circuitry for performing a sequence of operations that generally represent an algorithm and/or other process or method. The program is stored in or on a medium such as RAM, ROM, or disk, or embedded in a circuitry accessible and executable by an apparatus such as a processor or other circuitry.

The processor and program may constitute the same apparatus, at least partially, such as an array of electronic gates, such as FPGA or ASIC, designed to perform a programmed sequence of operations, optionally including or linked with a processor or other circuitry.

The term computerized apparatus or a computerized system or a similar term denotes an apparatus including one or more processors operable or operating according to one or more programs. As used herein, without limiting, a module represents a part of a system, such as a part of a program operating or interacting with one or more other parts on the same unit or on a different unit, or an electronic component or assembly for interacting with one or more other components.

As used herein, without limiting, a process represents a collection of operations for achieving a certain objective or an outcome.

As used herein, the term ‘server’ denotes a computerized apparatus providing data and/or operational service or services to one or more other apparatuses.

The term ‘configuring’ and/or ‘adapting’ for an objective, or a variation thereof, implies using at least a software and/or electronic circuit and/or auxiliary apparatus designed and/or implemented and/or operable or operative to achieve the objective.

In case electrical or electronic equipment is disclosed it is assumed that an appropriate power supply is used for the operation thereof.

The flowchart and block diagrams illustrate architecture, functionality or an operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosed subject matter. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of program code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, illustrated or described operations may occur in a different order or in combination or as concurrent operations instead of sequential operations to achieve the same or equivalent effect.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising” and/or “having” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein the term “configuring” and/or ‘adapting’ for an objective, or a variation thereof, implies using materials and/or components in a manner designed for and/or implemented and/or operable or operative to achieve the objective.

Unless otherwise specified, the terms ‘about’ or ‘close’ implies at or in a region of, or close to a location or a part of an object relative to other parts or regions of the object.

When a range of values is recited, it is merely for convenience or brevity and includes all the possible sub-ranges as well as individual numerical values within and about the boundary of that range. Any numeric value, unless otherwise specified, includes also practical close values enabling an embodiment or a method, and integral values do not exclude fractional values. A sub-range values and practical close values should be considered as specifically disclosed values.

As used herein, ellipsis ( . . . ) between two entities or values denotes an inclusive range of entities or values, respectively. For example, A . . . Z implies all the letters from A to Z, inclusively.

The terminology used herein should not be understood as limiting, unless otherwise specified, and is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosed subject matter. While certain embodiments of the disclosed subject matter have been illustrated and described, it will be clear that the disclosure is not limited to the embodiments described herein. Numerous modifications, changes, variations, substitutions and equivalents are not precluded.

Terms in the claims that follow should be interpreted, without limiting, as characterized or described in the specification.