# US 10,441,160 B2 · Method and system for classifying optic nerve head > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,441,160 B2 - Document ID: US10441160B2 - Status: Granted - Kind: B2 - Application number: 16/115,242 - Filed: 2018-08-28 - Published: 2018-12-20 - Granted: 2019-10-15 - Earliest claimed: 2016-11-22 - Assignees: Delphinium Clinic Ltd. - Applicants: Delphinium Clinic Ltd. - Inventors: Kate Coleman - Web page: https://patentgrep.com/patent/US10441160B2 ## CPC - A61B 3/0025: {characterised by electronic signal processing, e.g. eye models} - A61B 3/12: for looking at the eye fundus, e.g. ophthalmoscopes (A61B3/13 takes precedence) - A61B 3/1233: {for measuring blood flow, e.g. at the retina} - A61B 3/14: Arrangements specially adapted for eye photography - G06T 7/0012: {Biomedical image inspection} - G06T 7/0014: {using an image reference approach} - G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215) - G06V 40/193: {Preprocessing; Feature extraction} - G06V 40/197: {Matching; Classification} - G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30041: Eye; Retina; Ophthalmic - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - 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 ## Abstract Provided are a method and system for identifying and classifying the owner, age and health of an optic nerve head and its vasculature based on analysis of vector relationships of blood vessels and the neuroretinal rim within an image of the optic nerve head to each other. ## Claims ### Claim 1 A computer-implemented method of classifying the optic nerve head, the method comprising operating one or more processors to: segment an image of an optic nerve head from a photographic image of an eye; segment the image of the optic nerve head into multiple segments each containing blood vessels and neuroretinal rim fibres; extract features from the segmented images, the features describing relationships between the blood vessels themselves and between the blood vessels and the neuroretinal rim fibres in each of the segmented images, wherein the relationships between the vessels themselves and between the blood vessels and the neuroretinal rim comprise vectors mapped between points on the blood vessels and the neuroretinal rim in each of the segmented images; identify characteristics of the optic nerve head based on the extracted features; and classify the image of the optic nerve head based on the identified characteristics. ### Claim 2 (depends on 1) The method of claim 1, comprising segmenting an image of a non-dilated or dilated eye of a human or any other eye bearing species with an optic nerve to obtain an optic nerve head image. ### Claim 3 (depends on 1) The method of claim 1, wherein the segmenting of the image of an optic nerve head from a photographic image of an eye is performed with a deep neural network architecture using a fully convolutional network. ### Claim 4 (depends on 3) The method of claim 3, wherein the optic nerve head is located by classifying each pixel in the image. ### Claim 5 (depends on 1) The method of claim 1, wherein the segmenting of an image of an optic nerve head from a photographic image of an eye comprises rendering a geometric shape around the optic nerve head and cropping the image accordingly. ### Claim 6 (depends on 1) The method of claim 1, wherein the segmenting the image of the optic nerve head into multiple segments comprises using at least one of machine learning, deep neural networks, and a trained algorithm to automatically identify the blood vessels and neuroretinal rim fibers. ### Claim 7 (depends on 1) The method of claim 1, wherein the identifying characteristics of the optic nerve head comprises generating training sets for identifying the relationships between the vessels themselves and between the blood vessels and the neuroretinal rim. ### Claim 8 (depends on 1) The method of claim 1, comprising, for each segment: superimposing multiple concentric circles on the segment; determining intersection points of the circles with blood vessels and branches thereof and intersection points between the blood vessels and branches thereof and the neuroretinal rim fibres; mapping vectors between the intersection points; determining distances of the vectors; determining ratios of the vector distances; combining sequences and/or permutations of the ratios into an image representation; searching a lookup table for the closest representation to the image representation; and classifying the optic nerve head according to the closest representation found. ### Claim 9 (depends on 8) The method of claim 8, comprising returning an identity of the optic nerve head according to the closest representation found. ### Claim 10 (depends on 1) The method of claim 1, comprising using at least one of machine learning, deep neural networks, and a trained algorithm to automatically identify the blood vessels and optic nerve head neuroretinal rim as belonging to the individual eye image at that moment in time. ### Claim 11 (depends on 1) The method of claim 1, comprising classifying the optic nerve head image as being likely to be glaucomatous or healthy. ### Claim 12 (depends on 1) The method of claim 1, comprising classifying the optic nerve head image as being likely to belong to an adult or a child. ### Claim 13 (depends on 1) The method of claim 1, comprising identifying when the optic nerve head image changes. ### Claim 14 (depends on 13) The method of claim 13, comprising identifying changes to relationships within the optic nerve head image. ### Claim 15 A computing system configured for classifying the optic nerve head, the computing system comprising: a memory; and one or more processors configured to: segment an image of an optic nerve head from a photographic image of an eye; segment the image of the optic nerve head into multiple segments each containing blood vessels and neuroretinal rim fibers; extract features from the segmented images, the features describing relationships between the blood vessels themselves and between the blood vessels and the neuroretinal rim fibers in each of the segmented images, wherein the relationships between the vessels themselves and between the blood vessels and the neuroretinal rim comprise vectors mapped between points on the blood vessels and the neuroretinal rim in each of the segmented images; identify characteristics of the optic nerve head based on the extracted features; and classify the image of the optic nerve head based on the identified characteristics.