# US 2020/0401841 A1 · Apparatus for diagnosing glaucoma

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- Number: US 2020/0401841 A1
- Document ID: US20200401841A1
- Status: Published application
- Kind: A1
- Application number: 16/664,834
- Filed: 2019-10-26
- Published: 2020-12-24
- Earliest claimed: 2019-06-24
- Assignees: Samsung Sds Co., Ltd.
- Applicants: Samsung Sds Co., Ltd.
- Inventors: JoonSeok Lee; JoonHo Lee; MinYoung Lee; JiEun Song; SooAh Cho
- Web page: https://patentgrep.com/patent/US20200401841A1

## CPC

- G06T 7/0012: {Biomedical image inspection}
- A61B 3/12: for looking at the eye fundus, e.g. ophthalmoscopes (A61B3/13 takes precedence)
- A61B 5/7267: {involving training the classification device}
- G06T 7/0014: {using an image reference approach}
- G06V 10/242: {by image rotation, e.g. by 90 degrees}
- G06V 10/50: by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30041: Eye; Retina; Ophthalmic
- G06V 10/759: {Region-based matching}
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A glaucoma diagnosis apparatus according to an embodiment includes a fundus image processor configured to receive a fundus image and extract a first region of interest (ROI) and a second ROI from the received fundus image, an image classification neural network configured to learn the extracted first ROI and perform classification into a normal fundus image and a glaucoma fundus image on the basis of the learned first ROI, a vertical cup-to-disc ratio (vCDR) calculator configured to recognize an optic disc (OD) and an optic cup (OC) from the extracted second ROI and calculate a vCDR, and a determinator configured to aggregate a vCDR calculation result and an image classification result of the image classification neural network to determine whether glaucoma is present in the fundus image.

## Claims

### Claim at position 1

1: A glaucoma diagnosis apparatus comprising:
a fundus image processor configured to receive a fundus image and extract a first region of interest (ROI) and a second ROI from the received fundus image;
an image classification neural network configured to learn the extracted first ROI and perform classification into a normal fundus image and a glaucoma fundus image on the basis of the learned first ROI;
a vertical cup-to-disc ratio (vCDR) calculator configured to recognize an optic disc (OD) and an optic cup (OC) from the extracted second ROI and calculate a vCDR; and
a determinator configured to aggregate a vCDR calculation result and an image classification result of the image classification neural network to determine whether glaucoma is present in the fundus image.

### Claim at position 2 (depends on 1)

2: The glaucoma diagnosis apparatus of claim 1, wherein the fundus image processor comprises:
an optic disc detection module configured to detect an optic disc from the received fundus image;
an image rotation module configured to rotate the fundus image around center coordinates of the optic disc such that a slope between the center coordinates of the optic disc and a center of the fundus image is constant;
a first ROI extraction module configured to extract the first ROI from the rotated fundus image such that the detected optic disc is placed at an upper left end or an upper right end of the first ROI; and
a second ROI extraction module configured to extract the second ROI from the received fundus image such that the detected optic disc is placed at the center of the second ROI.

### Claim at position 3 (depends on 2)

3: The glaucoma diagnosis apparatus of claim 2, wherein the optic disc detection module further configured to detect the optic disc using an image obtained through a polar coordinate transformation on the fundus image.

### Claim at position 4 (depends on 2)

4: The glaucoma diagnosis apparatus of claim 2, wherein the fundus image processor further comprises a preprocessing module configured to resize the fundus image to a predetermined size before the optic disc is detected and horizontally flip the fundus image depending on whether the fundus image is a right fundus image or a left fundus image.

### Claim at position 5 (depends on 2)

5: The glaucoma diagnosis apparatus of claim 2, wherein the image rotation module further configured to set the center of the fundus image as an origin of a coordinate plane, calculate an angle between the center coordinates of the detected optic disc and a horizontal axis (an x-axis) of the coordinate plane, and rotate the fundus image around the origin such that the angle matches a predetermined reference angle.

### Claim at position 6 (depends on 5)

6: The glaucoma diagnosis apparatus of claim 5, wherein the reference angle is any one of 45 degrees or 135 degrees.

### Claim at position 7 (depends on 6)

7: The glaucoma diagnosis apparatus of claim 6, wherein the image rotation module further configured to augment the number of fundus images by flipping the fundus image according to a reference line connecting the origin of the rotated fundus image and the center coordinates of the optic disc or by additionally rotating the rotated fundus image within a predetermined additional rotation range around the origin of the rotated fundus image.

### Claim at position 8 (depends on 2)

8: The glaucoma diagnosis apparatus of claim 2, wherein the first ROI extraction module further configured to extract the ROI such that the center coordinates of the optic disc are spaced one-quarter of a side length from an upper left end or an upper right end of the first ROI.

### Claim at position 9 (depends on 2)

9: The glaucoma diagnosis apparatus of claim 2, wherein the fundus image processor further comprises a histogram matching module configured to, when the fundus image is a test image of a machine learning model, perform histogram matching on the first ROI of the test image on the basis of an average histogram of images included in a learning dataset of the machine learning model.

### Claim at position 10 (depends on 2)

10: The glaucoma diagnosis apparatus of claim 2, wherein the vCDR calculator comprises:
a first mask generation module configured to generate a first mask corresponding to the optic disc using an image obtained through a polar coordinate transformation on the second ROI;
a second mask generation module configured to extract a sub-region corresponding to the first mask from the second ROI and generate a second mask corresponding to the optic cup from the extracted sub-region; and
a calculation module configured to overlay the first mask and the second mask and calculate the vCDR.

### Claim at position 11 (depends on 10)

11: The glaucoma diagnosis apparatus of claim 10, wherein the first mask generation module further configured to augment the number of second ROIs by horizontally or vertically moving the center of the second ROI within a predetermined range or by horizontally shifting the image obtained through the polar coordinate transformation on the second ROI within a predetermined range.

### Claim at position 12 (depends on 11)

12: The glaucoma diagnosis apparatus of claim 11, wherein the calculation module further configured to calculate the vCDR using a result of combining the first mask and the second mask generated from an additional image generated by augmenting the same second ROI.

### Claim at position 13 (depends on 1)

13: The glaucoma diagnosis apparatus of claim 1, wherein the determinator further configured to determine whether glaucoma is present in the fundus image through logistic recession analysis on the image classification result and the vCDR calculation result.

### Claim at position 14 (depends on 13)

14: The glaucoma diagnosis apparatus of claim 13, wherein the determinator further configured to perform the logistic regression analysis by using first- to nth-order terms (here, n is a natural number of 2 or more) of the vCDR calculation result as an input value in addition to the image classification result.