WO2022010334A1

WO2022010334A1 - Structural damage determination method for eye disease and device therefor

Info

Publication number: WO2022010334A1
Application number: PCT/KR2021/010804
Authority: WO
Inventors: 이원준; 임한웅; 김형준
Original assignee: 한양대학교 산학협력단
Priority date: 2020-07-10
Filing date: 2021-08-13
Publication date: 2022-01-13
Also published as: KR20220007337A; KR102400623B1

Abstract

The present invention relates to a structural damage determination method for eye disease and a device therefor. The structural damage determination device for eye disease according to one embodiment of the present invention may comprise a processor and a memory electrically connected to the processor, wherein the memory may store instructions that, when executed, cause the processor to: register n normal eye images corresponding to a normal person according to a preconfigured method to generate n registered normal eye images (n is a natural number equal to or greater than 2); register a user's eye image to correspond to the registered normal eye images to generate a user's registered eye image; and compare the user's registered eye image with the registered normal eye images to determine a level of eye damage in the user's registered eye image. According to the present invention, the result from a comparison between a broad-spectrum optical coherence tomography image of a normal person and a broad-spectrum optical coherence tomography image of a patient can be displayed as a change in color, etc. and thus the structural damage level related to the patient's eye disease can be determined at a glance.

Description

Eye disease structural damage determination method and device

The present invention relates to a method and apparatus for determining structural damage of an eye disease using an eye image.

Glaucoma is an optic nerve disorder caused by an increase in intraocular pressure, resulting in visual field loss and visual impairment, and is a very dangerous and frequently occurring disease among ophthalmic diseases. Glaucoma, along with cataract and macular degeneration, is one of the three major blindness-causing ophthalmic diseases. Due to its chronic and irreversible nature, early detection of glaucoma can be delayed through treatment or surgery, and the therapeutic effect is also good.

Currently, the methods for diagnosing glaucoma include Scanning Laser Polarimetry (SLP) or Optical Coherence Tomography (OCT), Visual Field Test, and the depression rate of the optic disc (OD). There are various methods such as comparison.

In particular, in the case of optical tomography (OCT), in the past, the optic nerve papilla and the macula were photographed separately, and the results were analyzed separately. . However, recently, a broad-spectrum optical coherence tomography device that can simultaneously image the optic nerve and the macula has been developed. Therefore, a new method for determining structural damage of eye diseases using broad-spectrum optical coherence tomography (optical nerve and macula images taken simultaneously by a wide-range optical coherence tomography device) is required.

An object of the present invention is to provide a method and an apparatus for judging structural damage of an eye disease in a wide range using a broad-spectrum optical coherence tomography image.

According to an embodiment of the present invention, a processor; and a memory electrically connected to the processor, wherein, when the processor is executed, each of n normal eye images corresponding to a normal person is registered according to a preset method to generate n normal matched eye images, (provided that n is a natural number greater than or equal to 2), a user eye image is matched to correspond to the normal matched eye image to generate a user matched eye image, and by comparing the normal matched eye image with the user registered eye image, the user Disclosed is an apparatus for determining structural damage of an eye disease, storing instructions for determining a degree of eye damage in a matched eye image.

According to an embodiment, the memory detects a first optic nerve center and a first macular center of each of the n normal eye images, and obtains the normally matched eye image based on the first optic nerve center and the first macular center. The generated instructions may be stored, but the normal eye image may correspond to a broad-spectrum optical coherence tomography image.

According to an embodiment, the memory is an instruction for generating the n normal matched eye images by matching the first optic nerve center and the first macular center of each of the normal eye images to the preset reference optic nerve center and the reference macular center, respectively. can be saved

According to an embodiment, the memory generates a reference distance between the reference optic nerve center and the reference macular center, and a reference angle formed by a virtual line connecting the reference optic nerve center and the reference macular center and the border of the reference eye image and may store instructions for generating the n normally matched eye images using the reference distance and the reference angle.

According to an embodiment, the memory is an instruction for detecting a second optic nerve center and a second macula center of the user eye image, and generating the user-matched eye image based on the second optic nerve center and the second macula center However, the user eye image may correspond to a broad-spectrum optical coherence tomography image.

According to an embodiment, the memory generates a first user eye image by adjusting the size of the user eye image so that the distance between the second optic nerve center and the second macular center corresponds to a preset reference distance, and 2 generates a second user eye image by rotating the first user eye image so that an angle between a line connecting the 2 optic nerve center and the second macular center and a border of the user eye image corresponds to a preset reference angle; 2 It is possible to store instructions for generating the user-matched eye image by shifting the positions of the second optic nerve center and the second macular center in the user eye image to correspond to preset positions of the reference optic nerve center and the reference macular center.

According to an embodiment, the memory may store instructions for determining the degree of eye damage in the user-matched eye image by comparing the optic nerve thickness of each of the n normal matched eye images with the optic nerve thickness of the user-registered eye image.

According to an embodiment, the memory compares retinal nerve fiber layer (RNFL) values within a preset area of each of the n normal matched eye images with a retinal nerve fiber layer (RNFL) value within a preset area of the user matched eye image. Instructions for determining the degree of eye damage in the user-matched eye image may be stored.

According to an embodiment, the memory may store instructions for determining the degree of eye damage in the user-registered eye image by comparing the ganglion cell thickness of each of the n normal matched eye images with the ganglion cell thickness of the user-registered eye image. have.

According to an embodiment, the memory includes ganglion cell complex (GCC) values outside a preset area of each of the n normal matched eye images and a ganglion cell complex (GCC, Ganglion Cell Complex) values may be compared to store instructions for determining the degree of eye damage in the user-matched eye image.

According to an embodiment, the memory changes the color of the user-matched eye image according to the comparison result, and changes a portion in which the degree of eye damage is more severe than a preset first sub-ratio to a first color, and the eye damage A portion of which the degree is more severe than the preset second sub-ratio is changed to a second color, and the second sub-ratio may store instructions in which the eye damage rate is greater than that of the first sub-ratio.

According to the present invention, since the result of comparing the broad spectrum optical coherence tomography image of a normal person and the broad spectrum optical tomography image of a patient can be displayed as a change in color, etc., the degree of structural damage to the eye disease of the patient can be grasped at a glance.

In addition, according to the present invention, since the possibility that the doctor misses the patient's eye damage site is significantly lowered, it is possible to increase the diagnostic power of eye diseases and to become a stepping stone for effective eye treatment.

In order to more fully understand the drawings cited in the Detailed Description, a brief description of each drawing is provided.

1 is a block diagram of an apparatus for determining structural damage of an eye disease according to an embodiment of the present invention.

2 is a flowchart of a normal eye image processing method used in a method for determining structural damage of an eye disease according to an embodiment of the present invention.

3 is a flowchart for explaining a method of analyzing the degree of progression of an eye disease of the user according to an embodiment of the present invention.

4 and 5 are diagrams illustrating an operation of registering an eyeball image according to an embodiment of the present invention.

6 is a view for comparing a conventional image in which a partial optic nerve image and a partial macular image are connected with an ocular disease progression image according to an embodiment of the present invention.

Exemplary embodiments according to the technical spirit of the present invention are provided to more completely explain the technical spirit of the present invention to those of ordinary skill in the art, and the following embodiments are modified in various other forms may be, and the scope of the technical spirit of the present invention is not limited to the following embodiments. Rather, these embodiments are provided so as to more fully and complete the present disclosure, and to fully convey the technical spirit of the present invention to those skilled in the art.

Although the terms first, second, etc. are used herein to describe various members, regions, layers, regions, and/or components, these members, parts, regions, layers, regions, and/or components refer to these terms. It is self-evident that it should not be limited by These terms do not imply a specific order, upper and lower, or superiority, and are used only to distinguish one member, region, region, or component from another member, region, region, or component. Accordingly, the first member, region, region, or component to be described below may refer to the second member, region, region, or component without departing from the teachings of the present invention. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

Unless defined otherwise, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the concept of the present invention belongs, including technical and scientific terms. In addition, commonly used terms as defined in the dictionary should be construed as having a meaning consistent with their meaning in the context of the relevant technology, and unless explicitly defined herein, in an overly formal sense. shall not be interpreted.

As used herein, the term 'and/or' includes each and every combination of one or more of the recited elements.

Hereinafter, embodiments according to the technical spirit of the present invention will be described in detail with reference to the accompanying drawings.

Referring to FIG. 1 , an apparatus 100 for determining structural damage to eye diseases according to an embodiment of the present invention may be a device capable of generating a fundus image by photographing a user's eye and analyzing the fundus image have. In particular, the apparatus 100 for determining eye disease structural damage according to an embodiment of the present invention may be a wide-range optical coherence tomography apparatus capable of photographing a wide-area optical coherence tomography image. Alternatively, the apparatus 100 for determining eye disease structural damage may be a device capable of receiving and analyzing a fundus image generated by another device. The apparatus 100 for determining eye disease structural damage may include a processor 110 , a communication modem 120 , a memory 130 , an input device 140 , and/or a camera 150 .

The eye disease structural damage determination apparatus 100 compares the fundus images of several people (especially broad-spectrum optical coherence tomography images) with the fundus images of the user to analyze the degree of progression of the user's eye disease (eg, glaucoma). can That is, the memory 130 may store fundus images of several people, instructions and/or information for analyzing a user's fundus image, and the processor 110 accesses the memory 130 to provide the corresponding instructions and/or Alternatively, the progress of the user's eye disease may be analyzed by executing the information. Hereinafter, an analysis operation of the apparatus 100 for determining eye disease structural damage will be described in detail with reference to FIGS. 2 to 6 .

Each of the steps to be described below may be steps performed by the processor 110 executing instructions stored in the memory 130 . However, for the convenience of understanding and description, the processor 110 is described as a subject performing each step.

Before describing the method for determining structural damage of eye disease according to an embodiment of the present invention with reference to FIG. 2 , terms to be used below are defined.

First, the eyeball image may be an image of the user's eyeball generated through optical coherence tomography (OCT). In particular, the ocular image may be a 'wide-range optical coherence tomography image'. Therefore, the ocular image may include both the optic nerve papilla and the macula.

Also, the normal eye image may be an eye image corresponding to the eye of a normal person without eye disease.

In addition, the user eye image may be an eye image that is a target for determining the structural damage of an eye disease.

In step S210 , the first normal eye image and the nth normal eye image may be collected and stored in the memory 130 (where n is a natural number equal to or greater than 2). For example, the camera 150 may be a camera capable of optical coherence tomography (OCT). The processor 110 may store, in the memory 130 , n eye images (ie, n normal eye images) designated as having no eye disease among the eye images captured by the camera 150 . That is, the administrator may operate the input device 140 to separate and designate that there is no eye disease among the eye images captured by the camera 150 , and the processor 110 stores the separately designated eye images in the memory 130 . It can be saved as eye images. As another example, the processor 110 may store the first normal eye image to the nth normal eye image received from the external device through the communication modem 120 in the memory 130 .

In step S220 , the processor 110 may match each of the n normal eye images collected according to a preset method based on the macula and the optic nerve disc. This is because the position of the optic nerve and/or the position of the macula are different for each person. This will be described with reference to FIGS. 4 to 5 .

First, the processor 110 may detect an optic nerve center and/or a macular center from each of the n normal eye images. For example, the manager may operate the input device 140 to designate the optic nerve center and/or macular center in each normal eye image, and the processor 110 detects the point designated by the manager as the optic nerve center and/or macular center You can do it. For another example, the processor 110 may recognize the optic nerve in the normal eye image, automatically detect the center of the part where the recognized optic nerve is gathered as the optic nerve center, recognize the ganglion cell layer of the macula, and , it may be possible to automatically detect the center of gravity of the recognized ganglion cell layer as the center of the macula.

Also, the processor 110 may register n normal eye images using the reference optic nerve center and/or the reference macular center. Here, the reference optic nerve center and/or the reference macular center may be any preset point. For example, the processor 110 may designate any one of n normal eye images as a reference eye image. The processor 110 may designate the first generated normal eye image among the n normal eye images as the reference eye image, or may designate a normal eye image corresponding to the designation of the administrator as the reference eye image. The processor 110 may detect an optic nerve center and/or a macular center included in the reference eye image. An operation of the processor 110 detecting the optic nerve center and/or the macular center in the reference eye image may be similar to that described above. In addition, the processor 110 may detect the optic nerve center of the reference eye image as the reference optic nerve center and the macular center of the reference eye image as the reference macular center.

Meanwhile, the reference eye image is not any one of the n normal eye images, but may be an arbitrary eye image or a virtual eye image. Likewise in this case, the processor 110 may detect the reference optic nerve center and/or the reference macular center from the reference eye image.

4A is an example of a reference eye image 400, and the reference eye image 400 having a rectangular shape may include a reference optic nerve center (A) and a reference macular center (B).

In addition, the processor 110 may match the optic nerve center of each of the n normal eye images to the reference optic nerve center, and may match each macular center to the reference macular center.

For example, the processor 110 may generate the distance between the reference optic nerve center (A) and the reference macular center (B) as the reference distance. Also, the processor 110 may generate an angle between the imaginary line connecting the reference optic nerve center A and the reference macular center B and the edge of the reference eye image 400 as the reference angle θ. In addition, the processor 110 is configured so that the distance between the optic nerve center and the macular center in any normal eye image (eg, the mth normal eye image, where m is a natural number less than or equal to n) corresponds to the reference distance so that the mth normal eye image corresponds to the reference distance. It is possible to increase or decrease the size of the eye image (hereinafter referred to as 'the m-1 normal eye image'). In addition, the processor 110 is configured so that the angle between the imaginary line connecting the center of the optic nerve and the center of the macula in the m-1 th normal eye image and the rim of the m-1 th normal eye image corresponds to the reference angle θ. 1 The normal eye image can be rotated (hereinafter referred to as 'the m-2 normal eye image'). Accordingly, the m-2 th normal eye image and the reference eye image can be matched with the center of the optic nerve and the center of the macula.

The above-described operation of matching the n normal eye images of the processor 110 is merely exemplary. Accordingly, the method for the processor 110 to match all n normal eye images is not limited to the above-described operation and may vary.

4B is an example of an m-th normal eye image 400 - 1 , wherein the size of the m-th normal eye image is as small as vertical (α) and as small as horizontal (β). In addition, the case where the angle between the imaginary line connecting the optic nerve center (A') and the macular center (B') of the mth normal eye image and the edge of the mth normal eye image 400-1 is θ' is exemplified. .

Accordingly, the processor 110 increases the size of the mth normal eye image 400-1 so that the distance between the optic nerve center (A') and the macular center (B') corresponds to the reference distance, as illustrated in FIG. 5 , By rotating the mth normal eye image 400-1 by the difference between θ' and θ, the mth normal eye image 400-2 matched with the reference eye image may be generated.

Referring back to FIG. 2 , in step S230 , the processor 110 may generate distribution information on the thickness of the optic nerve and/or the thickness of the ganglion cell layer in the eyes of normal people using the matched n normal eye images.

For example, the processor 110 may generate n pieces of optic nerve thickness information and/or ganglion cell layer thickness information corresponding to an arbitrary point of the eye (meaning a point based on a reference eye image). Here, an arbitrary point may mean a point corresponding to each pixel of the reference eye image. Alternatively, an arbitrary point may mean an area including a plurality of adjacent pixels of the reference eye image. Accordingly, the processor 110 may generate information on the optic nerve thickness and/or ganglion cell layer thickness of n normal people corresponding to the first pixel (ie, the first point) of the n matched reference eye images. .

Meanwhile, the processor 110 may generate a retinal nerve fiber layer (RNFL) value as information about the optic nerve thickness in a preset region in the matched normal eye image. In addition, the processor 110 may generate a ganglion cell complex (GCC, Ganglion Cell Complex) value as information about the thickness of ganglion cells of the macula outside the preset area. Here, the preset region may mean a preset region centered on the optic disc of the reference eye image. For example, the preset region may mean a circle having a radius r with the optic nerve papilla of the reference eye image as a central point. For another example, the preset area may be a square having the optic nerve head of the reference eye image as the center of gravity. Accordingly, the shape and/or size of the preset area does not limit the scope of the present invention.

In step S240, when a new normal eye image (n+1 normal eye image) is captured through the camera 150 or received through the communication modem 120, the processor 110 matches it with the reference eye image and then the optic nerve thickness and/or update information about the ganglion cell layer thickness. This is because the information of the new n+1th normal eye image must be reflected.

According to the above-described method, the processor 110 may store the normal eye image for comparison with the user's eye image into a database in the memory 130 .

In step S310, when the user's eye image is generated, the processor 110 may match the user's eye image to the reference eye image according to a preset method (step S320). Here, the user's eye image may be a 'wide-range optical coherence tomography image' of the user captured by the camera 150 . Therefore, the user's eye image may include both the optic nerve papilla and the macula for the user's eye. Of course, the user's eye image may be an image received from an external device through the communication modem 120 .

In addition, the processor 110 may match the user eye image with the reference eye image in the same or similar method as that of S220 of FIG. 2 . That is, the processor 110 may detect the center of the optic nerve and/or the center of the macula in the user's eye image. In order to distinguish the optic nerve center and/or macular center of the normal eye image from the optic nerve center and/or macular center of the user's eye image, the former is referred to as the first optic nerve center and/or the first macular center, and the latter is referred to as the second optic nerve center and/or the second macular center. The processor 110 may increase or decrease the size of the user's eye image so that the detected distance between the second optic nerve center and the second macula center corresponds to the reference distance. The user's eyeball image whose size is changed by the operation is referred to as a first user's eyeball image.

In addition, the processor 110 may rotate the first user's eye image so that the angle between the imaginary line connecting the second optic nerve center and the second macula center and the edge of the user's eye image corresponds to the reference angle. . An image generated by being rotated by the motion is referred to as a second user's eye image.

In addition, the processor 110 may shift the second optic nerve center and the second macular center of the second user's eye image to correspond to positions of the reference optic nerve center and the reference macular center. The image (user eye image matched to the reference eye image) generated by the operation is referred to as a user-matched eye image.

In operation S330, the processor 110 may generate information on the thickness of the optic nerve and/or the thickness of the ganglion cell layer of the user-matched eye image. The operation of the processor 110 generating information on the optic nerve thickness and/or the ganglion cell layer thickness of the user-matched eye image may be the same or similar to the operation described in step S230 of FIG. 2 . That is, for example, the processor 110 may generate a retinal nerve fiber layer (RNFL) value as information on the thickness of the optic nerve within a preset region of the user-matched eye image, and the thickness of ganglion cells of the macula outside the preset region. As information about the ganglion cell complex (GCC, Ganglion Cell Complex) value can be generated.

In step S340, the processor 110 generates information on the user-matched eye image (information on the optic nerve thickness and/or ganglion cell layer thickness) and n normal eye images stored in the memory 130 in advance (optic nerve thickness and/or ganglion). Information on cell layer thickness) can be compared and displayed in a preset color according to the degree of eye damage rate.

For example, the processor 110 may compare the first user optic nerve thickness information corresponding to the first pixel of the user-matched eye image and the n pieces of first normal optic nerve thickness information corresponding to the first pixel of the normal eye image. As a result of the comparison, if the first user optic nerve thickness information is less than or equal to a first sub-ratio (eg, 5%) of the n pieces of first normal optic nerve thickness information, the processor 110 controls the color of the first pixel of the user-matched eye image. It can be changed to 1 color (eg, as a color of a dark region in FIG. 6 , such as yellow). That is, when the thickness of the first user optic nerve is thinner than the lower 5% of the thickness of the n first normal optic nerves, the processor 110 may change the color of the first pixel of the user-matched eye image to the first color.

Also, As a result of the comparison, if the first user optic nerve thickness information is less than or equal to the second sub-ratio (eg, 1%) of the n pieces of first normal optic nerve thickness information, the processor 110 controls the color of the first pixel of the user-matched eye image. It can be changed to 2 colors (eg, red, etc. as the color of the darkest region in FIG. 6 ). That is, when the thickness of the first user's optic nerve is thinner than the lower 1% of the thickness of the n first normal optic nerves, the processor 110 may change the color of the first pixel of the user-matched eye image to the second color. Here, the first pixel may be a pixel within a preset area.

For another example, the processor 110 may store information on the second user ganglion cell layer thickness corresponding to the second pixel of the user-matched eye image and the n second normal ganglion cell layer thicknesses corresponding to the second pixel of the normal eye image. information can be compared. As a result of comparison, if the second user ganglion cell layer thickness information is less than or equal to the first sub-ratio (eg, 5%) of the n pieces of second normal ganglion cell layer thickness information, the processor 110 controls the color of the second pixel of the user-matched eye image. may be changed to a first color (eg, as a color of a dark region of FIG. 6 , such as yellow). Also, As a result of comparison, if the second user ganglion cell layer thickness information is less than or equal to the second sub-ratio (eg, 1%) of the n pieces of second normal optic nerve thickness information, the processor 110 selects the color of the second pixel of the user-matched eye image. It may be changed to a second color (eg, red, etc. as a color of the darkest region of FIG. 6 ). Here, the second pixel may be a pixel outside the preset area.

Accordingly, when the user's eye image is displayed, it can be intuitively recognized that the portion marked with the second color is a portion where the optic nerve is damaged more than the first lower normal eye images.

6 (a) is an image in which the conventional individually generated partial optic nerve image and the macula partial image are connected, and is an image in which the optic nerve and/or ganglion cell layer is damaged visually. Also, FIG. 6(b) is an image in which the optic nerve and/or the ganglion cell layer is visually marked using a broad-spectrum optical coherence tomography image according to the present invention.

In (a) of FIG. 6 , the damaged area of the optic nerve and the like could only be partially displayed, so that the degree of structural damage of the ophthalmic disease could not be clearly understood. However, according to the present invention, since the degree of damage to the optic nerve of an ophthalmic disease can be compared and displayed as a whole with a normal person (particularly, a normal person with some damage to the optic nerve), the degree of structural damage to the eye disease of the patient can be grasped at a glance.

Meanwhile, a circle 600 indicated by a dotted line in FIG. 6B illustrates the “preset region” described with reference to FIGS. 2 and 3 . The processor can generate a retinal nerve fiber layer (RNFL) value as information about the thickness of the optic nerve inside the circle 600, and outside the circle 600, as information about the thickness of ganglion cells of the macula, the ganglion cell complex (GCC, Ganglion Cell Complex) value.

As mentioned above, although the present invention has been described in detail with reference to a preferred embodiment, the present invention is not limited to the above embodiment, and various modifications and changes can be made by those skilled in the art within the technical spirit and scope of the present invention. This is possible.

Claims

processor; and

a memory electrically coupled to the processor;

The memory, when the processor executes,

Registering each of n normal eye images corresponding to a normal person according to a preset method to generate n normal matched eye images (provided that n is a natural number equal to or greater than 2),

A user eye image is matched to the normal matched eye image to generate a user matched eye image,

and storing instructions for determining the degree of eye damage in the user-registered eye image by comparing the normal matched eye image with the user-registered eye image.
According to claim 1,

The memory is

Detecting the first optic nerve center and the first macular center of each of the n normal eye images,

generating the normally matched eye image based on the first optic nerve center and the first macular center

Save the instructions,

The normal eye image corresponds to a broad-spectrum optical coherence tomography image, an eye disease structural damage determination device.
3. The method of claim 2,

The memory is

generating the n normal matched eye images by matching the first optic nerve center and the first macular center of each of the normal eye images to the preset reference optic nerve center and the reference macular center, respectively

Storing the instructions, an eye disease structural damage determination device.
4. The method of claim 3,

The memory is

generating a reference distance between the reference optic nerve center and the reference macular center,

generating a reference angle formed by an imaginary line connecting the reference optic nerve center and the reference macular center and the border of the reference eye image,

generating the n normally matched eye images using the reference distance and the reference angle

Storing the instructions, an eye disease structural damage determination device.
3. The method of claim 2,

The memory is

detecting a second optic nerve center and a second macular center of the user's eye image,

generating the user-matched eye image based on the second optic nerve center and the second macular center

Save the instructions,

The user eye image corresponds to a broad-spectrum optical coherence tomography image, an eye disease structural damage determination device.
6. The method of claim 5,

The memory is

generating a first user eye image by adjusting the size of the user eye image so that the distance between the second optic nerve center and the second macular center corresponds to a preset reference distance;

A second user eye image is generated by rotating the first user eye image so that an angle between the line connecting the second optic nerve center and the second macular center and the edge of the user eye image corresponds to a preset reference angle,

generating the user-matched eye image by shifting the positions of the second optic nerve center and the second macular center in the second user eye image to correspond to preset positions of the reference optic nerve center and the reference macular center

Storing the instructions, an eye disease structural damage determination device.
According to claim 1,

The memory is

and storing instructions for determining the degree of eye damage in the user-matched eye image by comparing the optic nerve thickness of each of the n normal matched eye images with the optic nerve thickness of the user-matched eye image.
8. The method of claim 7,

The memory is

The degree of eye damage in the user-registered eye image by comparing retinal nerve fiber layer (RNFL) values in a preset area of each of the n normal matched eye images with retinal nerve fiber layer (RNFL) values in a preset area of the user-registered eye image. Storing the instructions to determine, eye disease structural damage determination device.
According to claim 1,

The memory is

and storing instructions for determining the degree of eye damage in the user-matched eye image by comparing the ganglion cell thickness of each of the n normal matched eye images with the ganglion cell thickness of the user-registered eye image.
10. The method of claim 9,

By comparing the ganglion cell complex (GCC) values outside the preset region of each of the n normal matched eye images with the ganglion cell complex (GCC, Ganglion Cell Complex) values outside the preset region of the user-matched eye image, the An apparatus for determining structural damage to an eye disease that stores instructions for determining the degree of eye damage in the user-matched eye image.
11. The method according to any one of claims 7 to 10,

The memory is

Change the color of the user-matched eye image according to the comparison result,

The portion in which the degree of eye damage is more severe than the preset first sub-ratio is changed to the first color,

The portion in which the degree of eye damage is more severe than the preset second sub-ratio is changed to a second color,

The second sub-ratio is that the eye damage rate is greater than the first sub-ratio, storing instructions, eye disease structural damage determination device.