# US 10,441,159 B2 · System and method for classifying and quantifying age-related macular degeneration

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 10,441,159 B2
- Document ID: US10441159B2
- Status: Granted
- Kind: B2
- Application number: 15/339,146
- Filed: 2016-10-31
- Published: 2017-05-04
- Granted: 2019-10-15
- Earliest claimed: 2015-10-30
- Assignees: The Charles Stark Draper Laboratory, Inc.
- Applicants: The Charles Stark Draper Laboratory, Inc.
- Inventors: John M. Irvine; Richard Wood; Nathan Lowry; David Floyd
- Web page: https://patentgrep.com/patent/US10441159B2

## CPC

- A61B 3/0025: {characterised by electronic signal processing, e.g. eye models}
- A61B 3/102: {for optical coherence tomography [OCT]}
- G06F 18/2451: linear, e.g. hyperplane
- G06T 7/0012: {Biomedical image inspection}
- G06V 10/764: using classification, e.g. of video objects
- G06V 40/197: {Matching; Classification}
- G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT]
- G06T 2207/20076: Probabilistic image processing
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30041: Eye; Retina; Ophthalmic
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

The present disclosure describes a system and method to classify optical coherence tomography (OCT) images. The present system can classify OCT images without first segmenting the retina tissue. The system can generate one or more profiles from vertical transects through the OCT images. The system can identify image statistics based on the one or more profiles. The system's classifier can then classify the OCT images based on the identified image statistics.

## Claims

### Claim 1

A system comprising one or more processors and a memory storing processor executable instructions, wherein execution of the instructions stored in the memory cause the one or more processors to:
retrieve, from the memory, an optical coherence tomography (OCT) image of retina tissue;
determine a location of a fovea in the OCT image;
identify a transect from the OCT image a predetermined distance from the location of the fovea, wherein the transect comprises an array of intensity values through the retina tissue of the OCT image;
generate a profile for the transect, the profile for the transect comprising at least one statistic of the array of intensity values; and
classify the OCT image using the profile for the transect into one of a wet age-related macular degeneration (AMD) group, a dry AMD group, or a healthy group.

### Claim 2 (depends on 1)

The system of claim 1, wherein execution of the instructions stored in the memory cause the one or more processor to preprocess the OCT image with at least one of down-sampling, de-noising, filtering, or flattening.

### Claim 3 (depends on 2)

The system of claim 2, wherein the wherein execution of the instructions stored in the memory cause the one or more processors to filter the OCT image with a Frangi filter.

### Claim 4 (depends on 1)

The system of claim 1, wherein execution of the instructions stored in the memory cause the one or more processors to:
identify an outline of the of the retina tissue;
calculate a derivative of the outline; and
determine a location of a minimum of the derivative.

### Claim 5 (depends on 1)

The system of claim 1, wherein execution of the instructions stored in the memory cause the one or more processors to extract a plurality of transects from the OCT image.

### Claim 6 (depends on 5)

The system of claim 5, wherein execution of the instructions stored in the memory cause the one or more processors to generate the profile responsive to each of the plurality of transects.

### Claim 7 (depends on 5)

The system of claim 5, wherein execution of the instructions stored in the memory cause the one or more processors to generate a respective profile for each of the plurality of transects.

### Claim 8 (depends on 1)

The system of claim 1, wherein execution of the instructions stored in the memory cause the one or more processors to determine a number of times the profile crosses at least one threshold.

### Claim 9 (depends on 1)

The system of claim 1, wherein execution of the instructions stored in the memory cause the one or more processors to classify the OCT image with at least one of a linear discriminant analysis (LDA), a k-nearest neighbor based algorithm, or a support vector machine.

### Claim 10

A method comprising:
retrieving, from a memory, an optical coherence tomography (OCT) image of retina tissue;
determining a location of a fovea in the OCT image;
identifying a transect from the OCT image a predetermined distance from the location of the fovea, wherein the transect comprises an array of intensity values through the retina tissue of the OCT image;
generating a profile for the transect, the profile for the transect comprising at least one statistic of the array of intensity values; and
classifying the OCT image using the profile for the transect into one of a wet age-related macular degeneration (AMD) group, a dry AMD group, or a healthy group.

### Claim 11 (depends on 10)

The method of claim 10, further comprising preprocessing the OCT image with at least one of a down-sampling, a de-noising, a filtering, or a flattening algorithm.

### Claim 12 (depends on 11)

The method of claim 11, further comprising filtering the OCT image with a Frangi filter.

### Claim 13 (depends on 10)

The method of claim 10, further comprising:
identifying an outline of the of the retina tissue;
calculating a derivative of the outline; and
determining a location of a minimum of the derivative.

### Claim 14 (depends on 10)

The method of claim 10, further comprising extracting a plurality of transects from the OCT image.

### Claim 15 (depends on 14)

The method of claim 14, further comprising generating the profile responsive to each of the plurality of transects.

### Claim 16 (depends on 14)

The method of claim 14, further comprising generating a respective profile for each of the plurality of transects.

### Claim 17 (depends on 10)

The method of claim 10, further comprising determining a number of times the profile crosses at least one threshold.

### Claim 18 (depends on 10)

The method of claim 10, further comprising classifying the OCT image with at least one of a linear discriminant analysis (LDA), a k-nearest neighbor based algorithm, or a support vector machine.