# US 10,499,845 B2 · Method and device for analysing an image > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,499,845 B2 - Document ID: US10499845B2 - Status: Granted - Kind: B2 - Application number: 15/507,107 - Filed: 2015-08-25 - Published: 2017-08-17 - Granted: 2019-12-10 - Earliest claimed: 2014-08-25 - Assignees: Singapore University of Technology and Design; National Skin Centre (Singapore) PTE LTD - Applicants: Singapore University of Technology and Design; National Skin Centre (Singapore) Pte Ltd - Inventors: Thanh-Toan Do; Yiren Zhou; Victor Pomponiu; Ngai-Man Cheung; Dawn Chin Ing Koh; Tan Suat Hoon - Web page: https://patentgrep.com/patent/US10499845B2 ## CPC - A61B 5/444: {Evaluating skin marks, e.g. mole, nevi, tumour, scar} - A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens} - A61B 5/1032: {Determining colour of tissue for diagnostic purposes} - A61B 5/6898: {Portable consumer electronic devices, e.g. music players, telephones, tablet computers} - A61B 5/726: {using Wavelet transforms} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06T 7/90: Determination of colour characteristics - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/28: Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns - G06V 10/46: Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features (colour feature extraction G06V10/56) - G06V 10/56: relating to colour - G06T 2207/10024: Color image - G06T 2207/20016: Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform - G06T 2207/20021: Dividing image into blocks, subimages or windows - G06T 2207/30088: Skin; Dermal - G06T 2207/30096: Tumor; Lesion - G06V 2201/032: of protuberances, polyps nodules, etc. - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A method for analyzing an image of a lesion on the skin of a subject including (a) identifying the lesion in the image by differentiating the lesion from the skin; (b) segmenting the image; and (c) selecting a feature of the image and comparing the selected feature to a library of predetermined parameters of the feature. The feature of the lesion belongs to any one selected from the group: color, border, asymmetry and texture of the image. ## Claims ### Claim 1 A method for analysing analyzing an image of a lesion on the skin of a subject, the method comprising: (a) identifying the lesion in the image by differentiating the lesion from the skin; (b) segmenting the image; and (c) selecting a feature of the image and comparing the selected feature to a library of pre-determined parameters of the feature, wherein the feature of the lesion belongs to any one selected from the group: colour, border, asymmetry and texture of the image, (d) quantifying the color variation and border irregularity of the image of the lesion, wherein the irregularity of the border is determined by: (a) providing lines along the border; (b) determining the angles between two adjacent lines; and (c) determining the average and variance of the angles, wherein the number of lines chosen is any number 8, 12, 16, 20, 24 or 28. ### Claim 2 (depends on 1) The method according to claim 1, wherein the image is processed prior to identifying the lesion in the image. ### Claim 3 (depends on 2) The method according to claim 2, wherein processing comprises down-sampling the image. ### Claim 4 (depends on 3) The method according to claim 3, wherein segmenting the image further comprises a first segmenting and a second segmenting, the first segmenting is a coarse segmentation to determine an uncertain region on the downsampled image and the second segmenting refines the uncertain region to obtain segment boundary details. ### Claim 5 (depends on 4) The method according to claim 4, wherein the uncertain region is +/−2 pixels around the coarse segmentation region boundary. ### Claim 6 (depends on 4) The method according to claim 4, wherein the second segmenting is carried out using a MST-based algorithm. ### Claim 7 (depends on 1) The method according to claim 1, wherein the group is further divided into sub-groups and the feature is selected by comparing the feature to other features belonging to other sub-groups and other features within the same sub-group. ### Claim 8 (depends on 1) The method according to claim 1, wherein the lesion in the image is identified by comparing a colour of the skin to a library of pre-determined colours. ### Claim 9 (depends on 1) The method according to claim 1, wherein segmenting the lesion further comprising removing segments of the lesion that are connected to a skin boundary. ### Claim 10 (depends on 1) The method according to claim 1, wherein segmenting the image is a result of two segmentations, (a) a minimal intra-class-variance thresholding algorithm to locate smoothly-changing borders; and (b) a minimal-spanning-tree based algorithm to locate abruptly-changing borders. ### Claim 11 (depends on 1) The method according to claim 1, wherein segmenting is carried out by a region-based method. ### Claim 12 (depends on 1) The method according to claim 1, wherein the color variation is quantified by: (a) dividing image into N-partitions, each partitions further divided into M-subparts; (b) calculating an average pixel value for each subpart and assigning a vector to the subpart; and (c) determining a maximum distance between the vectors, wherein a value of N is any value 4, 8, 12 or 16; and a value of M is any value 2, 4 or 8. ### Claim 13 (depends on 1) The method according to claim 1, wherein the lesion is present in a tissue having a dermal-epidermal junction and an epidermal layer. ### Claim 14 (depends on 1) The method according to claim 1, wherein the lesion is an acral lentiginous melanoma. ### Claim 15 (depends on 1) The method according to claim 1, wherein the method further comprising acquiring the image on a computing device and the analysis is carried out on the same computing device. ### Claim 16 A device for analyzing an image of an object and evaluating the risk or likelihood of a disease or condition, the system comprising: (a) an image capturing device for capturing the image of an object; and (b) a processor for executing a set of instructions stored in the device for analyzing the image, the set of instructions includes a library of algorithms stored in the device to carry out a method comprising: identifying the lesion in the image by differentiating the lesion from the skin; segmenting the image; and selecting a feature of the image and comparing the selected feature to a library of pre-determined parameters of the feature, wherein the feature of the lesion belongs to any one selected from the group: color, border, asymmetry and texture of the image, quantifying the color variation and border irregularity of the image of the lesion, wherein the irregularity of the border is determined by: (a) providing lines along the border; (b) determining the angles between two adjacent lines; and (c) determining the average and variance of the angles, wherein the number of lines chosen is any number 8, 12, 16, 20, 24 or 28. ### Claim 17 (depends on 16) The device according to claim 16, wherein the object is a lesion on a patient's body. ### Claim 18 (depends on 16) The device according to claim 16, wherein the disease is melanoma. ### Claim 19 (depends on 16) The device according to claim 16, wherein the device further comprising a graphical user interface for indicating to a user the results of the analysis. ### Claim 20 A non-transitory computer-readable medium including executable instructions to carry out a method for analyzing an image of a lesion on the skin of a subject, the method comprising: (a) identifying the lesion in the image by differentiating the lesion from the skin; (b) segmenting the image; and (c) selecting a feature of the image and comparing the selected feature to a library of pre-determined parameters of the feature, wherein the feature of the lesion belongs to any one selected from the group: colour, border, asymmetry and texture of the image, (d) quantifying the color variation and border irregularity of the image of the lesion, wherein the irregularity of the border is determined by: (a) providing lines along the border; (b) determining the angles between two adjacent lines; and (c) determining the average and variance of the angles, wherein the number of lines chosen is any number 8, 12, 16, 20, 24 or 28.