KR20190063126A - Retina and optic nerve function evaluation system and information output method thereof - Google Patents

Abstract

Provided are a retina and optic nerve function evaluation system and a retina and optic nerve function information output method. According to an embodiment, the retina and optic nerve function evaluation system comprises: a retinal image capturing unit for capturing an image of an eyeball to generate a retinal image; and a cell density distribution generating unit for identifying at least retinal ganglion cells (RGCs) and horizontal cells in the retinal image and generating a density distribution map of RGCs indicating the distribution of the RGCs. An objective and accurate retinal and optic nerve function evaluation system and the retina and optic nerve function information output method can be provided by directly measuring the RGCs and building an RGC map.

Description

[0001] Retina and optic nerve function evaluation system and information output method thereof [0002]

The present invention relates to a retinal and optic nerve function evaluation system and information output method thereof, and more particularly, to a retinal and optic nerve function evaluation system capable of constructing a density map of retinal ganglion cells, and a method of outputting information of retina and optic nerve function.

Central nervous cells are no longer able to divide and regenerate after a certain time after birth. Therefore, loss of visual-related central nervous system cells is not restored and causes progressive visual impairment, which in some cases leads to blindness. The loss of these neurons is associated with a variety of neurological deficits (congenital (macular degeneration, diabetic retinopathy) retinopathy, optic nerve disease (optic neuritis, ischemic optic neuropathy, Leber congenital darkness Etc.) and glaucoma. Among them, glaucoma is a progressive visual field disorder caused by persistent loss of retinal ganglion cells, which is the second leading cause of blindness in the world as a single disease.

Presently, the methods of examining the function of the retina and optic nerve are using visual field test and ocular optical tomography (OCT). The visual field test is a method of measuring the sensitivity of light, which can be identified at each point of the retina, using an automatic visual field system. Such a visual field test measurement method has problems such as objectivity, reproducibility between examinations is low, and it takes about 20 minutes or more when the binocular test is performed because the result may vary depending on the degree of cooperation of the subject.

In addition, the ocular optical tomography is a method of examining the appearance of the eyeball by tomography of the eyeball, and indirectly estimating the distribution of retinal ganglion cells based on the retinal layer thickness. These changes in retinal ganglion cell thickness can be observed in a state where the disappearance of the cells due to the disease progresses at a certain level. That is, the ocular optical tomography is not suitable for early diagnosis of disease.

Therefore, there is a demand for a system and a method capable of directly and objectively evaluating the functions of the retina and optic nerve, and diagnosing the disease early.

DISCLOSURE Technical Problem The present invention has been devised to solve the above-described problems, and it is an object of the present invention to provide a retinal ganglion cell (RGC) The present invention is directed to providing a method for providing a service to a user.

The retina and optic nerve function evaluation system according to an embodiment of the present invention includes a retina image photographing unit for photographing an eye to generate a retinal image; And a cell density distribution generation unit for identifying at least retinal ganglion cells and horizontal cells in the retinal image and generating a density distribution map of retinal ganglion cells indicating the distribution of the retinal ganglion cells.

In one embodiment, the cell density distribution generator may classify the retinal ganglion cells and the horizontal cells in consideration of the size and shape of the cells in the retinal image.

In one embodiment, the cell density distribution generator divides the retinal image into sub-pixels of a predetermined size, classifies cells larger than the sub-pixels into the retinal ganglion cells, Cells.

In one embodiment, the cell density distribution generator may select the horizontal cells by observing the shading change of the boundary region of the horizontal cells.

In one embodiment, the retinal image capturing unit may include a light irradiating unit for irradiating light to the eyeball, and an optical compensator for receiving light reflected from the eyeball and compensating for aberration of light generated in the eyeball.

In one embodiment, the data storage unit for storing the retinal image and the cell density distribution map of the retinal ganglion, and the cell density distribution map of the retinal ganglion are analyzed to generate retina and optic nerve function information And a function information generating unit.

A method of outputting retina and optic nerve function information according to an embodiment of the present invention includes: generating an image of a retina by photographing an eyeball; Identifying at least retinal ganglion cells and horizontal cells in the retinal image and generating a cell density distribution map indicating the distribution of the retinal ganglion cells; And analyzing the retinal image and the cell density distribution map to generate functional information.

In one embodiment, the retinal ganglion cells and the horizontal cells may be classified according to the size and shape of cells in the retinal image.

In one embodiment, the retinal image is divided into subpixels of a predetermined size, cells larger than the subpixel are classified into the retinal ganglion cells, and cells smaller than the subpixel are classified into the horizontal cells .

In an exemplary embodiment, the retinal image may be generated by irradiating light to the eyeball and compensating for aberration of light generated from the eyeball by receiving light reflected from the eyeball.

In one embodiment, the method may further include storing the retinal image and the cell density distribution map of the retinal ganglion.

The retinal and optic nerve function evaluation system according to an embodiment of the present invention can generate a retinal image by scanning the retina and generate a density distribution map of retinal ganglion cells of the retina based on the generated retinal image. In other words, it is possible to directly observe the retinal ganglion cell, and by observing the density distribution of the retinal ganglion cell, it is possible to make an objective evaluation about the degree of damage of the part in the occurrence of the retinal and optic nerve diseases, thereby performing the functional evaluation of the retina and the optic nerve .

The retinal image is generated by imaging the retina of the subject. The production process of the retina is transient and does not directly affect the human body. In addition, the retinal image can be precisely generated to visually distinguish retinal ganglion cells and other cells, and its density distribution map is automatically generated, and even if it does not require a high level of medical instruction or experience by a physician It is possible to easily provide retina and optic nerve function evaluation information to the subject.

1 is a view showing the structure of the retina and the optic nerve.
2 is a configuration diagram of a retina and optic nerve function evaluation system according to an embodiment of the present invention.
FIGS. 3 and 4 are exemplary views of a retinal image generated in the retinal image capturing unit.
5 is an exemplary view showing a density distribution map of retinal ganglion cells.
6 is a flowchart of a method of outputting function information of the retina and optic nerve according to an embodiment of the present invention.

The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. The various embodiments of the present invention are different but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with an embodiment. Furthermore, the position or arrangement of individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which the claims are entitled. In the drawings, like reference numerals refer to the same or similar functions in various aspects.

As used herein, terms used in the present specification are selected from the general terms that are currently widely used, while taking into consideration the functions, but these may vary depending on the intention or custom of the artisan or the emergence of new techniques. Also, in certain cases, there may be a term selected by the applicant at will, in which case the meaning will be described in the description part of the corresponding specification. Therefore, the terms used in the present specification should be interpreted based on the meaning of the term rather than on the name of a simple term, and on the contents throughout the specification.

1 is a view showing the structure of the retina and the optic nerve.

The human eye is a camera-like structure. The cornea and lens of the eye correspond to the camera lens, the iris to the iris, the retina to the film, and the optic nerve to the image transmission cable of the digital camera. External light is gathered through the cornea, the amount is controlled by the iris, and the thickness of the lens is regulated and the correct image is formed in the retina. In this case, if the cornea is damaged, the cataracts with blurred lens can be restored by the cornea graft. However, if the retina or optic nerve is damaged, the cataract can not be treated and the sight is lost. The retina is a thin layer of neurons that form the inner wall surrounding the inside of the eye. It converts the external light signal coming into the eye into an electrical signal and transmits it to the brain through the optic nerve.

As shown in FIG. 1, the structure of the retina is composed of retinal ganglion cells (RGC), bipolar cells, amacrine cells, and photoreceptor cells.

When external light enters the retina, the photoreceptor cell at the bottom of the retina converts the light signal into a bioelectrical signal that can be interpreted by the nervous system. This biomedical signal is transmitted to the retinal ganglion cell through the anode cell and the horizontal cell.

The optic nerve fibers that originate from the retinal ganglion cells gather from the optic nerve head to create an optic nerve. The optic nerve crosses the optic nerve fibers in the optic chiasm and is transmitted to the lateral geniculate nucleus. In the lateral dorsomedial nucleus, neurons in synapses are transmitted to the visual cortex through the optic nerve, and the visual sense is felt.

Retinal ganglion cells are cells that carry out important functions to transmit the bioelectrical signals generated by the photoreceptor cells to the optic nerve. Such loss of retinal ganglion cells is associated with retinal and optic nerve defects and can be observed in a variety of diseases such as glaucoma, optic neuropathy, optic neuritis, and diabetic retinopathy. Thus, understanding the overall number and damage of retinal ganglion cells may be an important way to perform normal functional assessment of the retina and optic nerve.

The retinal and optic nerve function assessment system 10 according to an embodiment of the present invention can scan the retina and generate a density distribution map of retinal ganglion cells. In other words, it is possible to observe the retinal ganglion cell directly, and by confirming the density distribution thereof, it is possible to make an objective evaluation on the extent of damage of the retinal ganglion when the disease occurs, thereby performing functional evaluation of the retina and optic nerve . Hereinafter, the configuration of the present invention will be described in more detail.

FIG. 2 is a configuration diagram of a retina and optic nerve function evaluation system 10 according to an embodiment of the present invention, FIGS. 3 and 4 are views showing an example of a retinal image generated in a retina image capturing unit, Fig. 2 is a diagram showing a density distribution map of ganglion cells. Fig.

Referring to FIG. 2, the retina and optic nerve function evaluation system 10 according to an embodiment of the present invention includes a retinal image capturing unit 100 and a cell density distribution generating unit 110.

The retina image capturing unit 100 may capture an eyeball to generate a retinal image. The retina and the cells that make up it are transparent, making it difficult to shoot with general imaging means. The retinal image capturing unit 100 according to an exemplary embodiment of the present invention may include a differential interference microscope (DIC). Differential interference microscopy can divide light into two microscopic pathways and interpret the optical distance between two light as interference. Therefore, when a difference in optical distance occurs, imaging due to interference can be performed, so that image formation of a transparent cell can be possible.

The retina image photographing unit 100 includes a light irradiating unit 101 for irradiating light to the eyeball, an optical compensating unit 102 for compensating aberration of light generated in the eyeball by receiving the light reflected from the eyeball, And a light processing unit 103 including a CCD into which light is introduced.

The light irradiation unit 101 includes at least a light source for generating light for imaging, a polarizer for transmitting only the light polarized at a specific angle in the generated light source, a Wallstone prism for dispersing the polarized light into light of two paths, And an objective lens for converging the light. The light irradiating unit 101 can provide the light of the two paths to the retina, and the difference in irregularities and optical thickness of the cells can be observed as a difference in brightness and color using the interference of the light.

Here, the retinal image capturing unit 100 photographs an actual eyeball, and aberration may occur when light irradiated to the eyeball passes through the lens of the eyeball and the cornea. However, the aberration of the light may be compensated by the optical compensator 102 have. The optical compensation unit 102 may include a wavefront sensor and an optical compensation mirror. The wavefront sensor can sense the aberration of the light reflected from the eyeball and analyze the wave front distorted by the aberration. The optical compensation mirror can compensate the light based on the aberration detected by the wavefront sensor. The optical compensation mirror can be an adaptive mirror. That is, the optical compensation unit 102 is adapted with Adaptive Optics (AO), which compensates aberrations caused by the characteristics of the eyeball, thereby enabling imaging of the retinal nerve cells in vivo in the living body.

The light compensated by the light compensating unit 102 may be introduced into the CCD, and the retinal image RI may be generated in the light processing unit 103.

The retinal image capturing unit 100 may capture an image of the entire retina to generate a retinal image RI. However, the present invention is not limited thereto. The retina image capturing unit 100 may enlarge a specific region of the retina through magnification control, It is possible to move the observation region through the observation region. In addition, real-time observation can be performed and real-time shooting of the currently observed screen can be performed. In addition, the retinal image capturing unit 100 according to some embodiments may measure the degree of bending of the curved retina, and may correct the image to a flat state to generate a retinal image (RI).

The retinal image capturing unit 100 may provide the generated retinal image (RI) to the cell density distribution generating unit 110.

As shown in FIG. 3, the retinal image (RI) is composed of a long elongated optic nerve and a gray circle surrounded by a dark gray circle, such as an amacrine cell (AC), a dark gray ellipse Retinal ganglion cells (RGC) in shape and blood vessels passing between these cells can be displayed.

Horizontal cells of the retina, retinal ganglion cells, and blood vessels are morphologically different from each other in terms of their functions, as well as their basic shapes and running directions. The retinal image (RI) of the present invention can provide a sharp image enough to visually confirm the appearance difference of each cell of the retina.

As described above, since the distribution and density of retinal ganglion cells (RGC) are closely related to the retinal disease, the cell density distribution generation unit 110 generates the retinal ganglion cell (RGC) A cell density distribution map (DM) showing the distribution of cells can be generated.

The cell density distribution generating unit 110 may classify the retinal ganglion cell (RGC) and the horizontal cell (AC) in consideration of the size and shape of the cell in the retinal image (RI).

The cell density distribution generation unit 110 divides the retinal image RI into subpixels of a predetermined size, classifies cells larger than the subpixel as retinal ganglion cells RGC, AC).

In addition, the cell density distribution generation unit 110 may first select the horizontal cell (AC) by observing the shading change in the cell boundary region. In other words, the horizontal cell (AC) can be selected in consideration of the fact that the border, which is the border region of the horizontal cell (AC), is displayed darker than the center region. The cell density distribution generation unit 110 may select cells of a specific size or larger from the remaining cells except the horizontal cell (AC) as retinal ganglion cells (RGC).

The cell density distribution generation unit 110 can select the retinal ganglion cells (RGC) and horizontal cells (AC) from the provided retinal image (RI) and determine the position and number of each cell.

The cell density distribution generation unit 110 may receive a plurality of retinal image images RI from the retinal image pickup unit 100. [ The retinal image capturing unit 100 may divide the retina into a plurality of regions, and each region may be photographed. One retinal image RI provided by the cell density distribution generating unit 110 may be an image obtained by photographing a specific region of the retina Lt; / RTI >

The cell density distribution generation unit 110 may select horizontal cells (AC) and retinal ganglion cells (RGC) included in each of the plurality of retinal image images RI provided. A plurality of retinal image images (RI) having been sorted can be combined into one image representing the entire retina, and based on this, a density distribution map of retinal ganglion cells showing the distribution of retinal ganglion cells (RGC) . The retinal image images RI may have a predetermined sequence number and may be combined into one image according to the order of the numbers. However, the present invention is not limited to this, and the retinal image images RI may have a continuous structure (e.g., ) May be checked and combined into one image. However, the present invention is not limited to this. The retinal image RI may be an image of the entire retina, and the cell density distribution generator 110 may utilize the retinal image RI provided by the retinal image pickup unit 100 To generate a density distribution map of retinal ganglion cells.

In addition, the cell density distribution generating unit 110 according to some embodiments may select normal retinal ganglion cells and abnormal retinal ganglion cells (RGC ') from a retinal image (RI). Here, FIG. 4 is a diagram showing a retinal image (RI) including the abnormal retinal ganglion cell (RGC '). Normal retinal ganglion cells do not have abnormalities such as bubbles in the central part of the cell, but abnormality such as air bubbles which occur during apoptosis is observed in abnormal retinal ganglion cells. Such abnormalities such as bubbles can occur in the process of RGC death due to various causes. The cell density distribution generation unit 110 can distinguish the abnormal RGC from the normal RGC, and the selected abnormal RGC cells can be deducted from the number of retinal ganglion cells. However, the present invention is not limited thereto, and the abnormal retinal ganglion cells may be separately reflected in the density distribution map of retinal ganglion cells.

FIG. 5 is a diagram showing a density distribution map of retinal ganglion cells. FIG. As shown in FIG. 5, the density of retinal ganglion cells can be expressed by a constant value in each region, but is not limited thereto. In some embodiments, the density distribution map of the retinal ganglion cells can distinguish retinal ganglion cells from other cells as the color difference of each pixel in a state in which the retinal region is divided into subpixels. Here, the density of retinal ganglion cells can be represented by the color density of the pixels. The density distribution map of retinal ganglion cells can easily grasp the number of retinal ganglion cells (RGCs) by location, and the number of such cells can be calculated by a direct method, which can provide a higher accuracy than an indirect method of diagnosis.

The density distribution map DM and the retinal image RI of the generated retinal ganglion cells can be stored in the data storage unit 120. The retinal and optic nerve function evaluation system 10 may generate a retinal image image (RI) and a density distribution map of ganglion cells according to a predetermined time period, and the generated data may be stored in the data storage unit 120 . Illustratively, once a year, periodic screening can be performed to generate a retinal image (RI) and a density distribution map of retinal ganglion cells, and the generated data can be stored in the data storage unit 120. The data can be used as a reference for future screening as well as current screening data.

The retina and optic nerve function evaluation system 10 may further include a function information generation unit 130. The function information generation unit 130 may include a density distribution map DM of the retinal ganglion cells and a retinal image RI And may be provided in the data storage unit 120 and may be analyzed to generate retina and optic nerve function information. The retina and optic nerve function information may be information that can directly identify the subject's current retina and optic nerve status. Retina and optic nerve function information may include the number of retinal ganglion cells present in the subject, the density distribution of retinal ganglion cells, and the number of abnormal retinal ganglion cells, and whether current cell numbers and density distributions are within normal ranges You can tell.

In addition, the retina and optic nerve function information may be generated by comparing the previously generated retinal image and the density distribution map of the retinal ganglion cell with the current one. That is, the retina and optic nerve function information can be generated by performing a regular checkup once a year, and can be provided to compare and analyze the state of the subject.

In addition, the density distribution map of retinal ganglion cells according to some embodiments may separately display abnormal retinal ganglion cells. Information related to these abnormal retinal ganglion cells can be included in the retina and optic nerve function information, and provides an increased number of abnormal retinal ganglion cells to the subject, thereby enabling early diagnosis of the disease.

Hereinafter, a method of outputting function information of the retina and optic nerve according to an embodiment of the present invention will be described.

6 is a flowchart of a method of outputting function information of the retina and optic nerve according to an embodiment of the present invention. Here, the method of outputting retina and optic nerve function information according to an embodiment of the present invention may be performed through the above-described retina and optic nerve function evaluation system 10. Thus, Figures 1 to 5 can be referred to, and overlapping descriptions will be omitted.

Referring to FIG. 6, the method of outputting retina and optic nerve function information according to an embodiment of the present invention includes generating a retinal image by photographing an eyeball (S100), displaying a retinal ganglion cell density distribution map (S110), and generating the function information (S120).

First, a retinal image is generated (S100).

The generation of the retinal image RI may be performed in the retinal image capturing unit 100. [ The retinal image capturing unit 100 may be composed of a differential interference microscope (DIC). Since the imaging due to the interference can be performed according to the occurrence of the optical distance difference, it is possible to image the transparent cells. In addition, the retinal image capturing unit 100 may include a wavefront sensor and an optical compensation mirror. The wavefront sensor can sense the aberration of the light reflected from the eyeball and analyze the wave front distorted by the aberration. The optical compensation mirror can compensate the light based on the aberration detected by the wavefront sensor. The optical compensation mirror can be an adaptive mirror. That is, the retinal image capturing unit 100 is adapted with adaptive optics (AO), which compensates aberrations caused by the nature of the eyeball and enables imaging of the retinal nerve cells in vivo in the living body, It is possible to generate a retinal image (RI) in which retinal cells are displayed.

Then, a retinal ganglion cell density distribution is generated (S110).

Retinal ganglion cells (RGC) and horizontal cells (AC) can be distinguished in the retinal image (RI) considering the size and shape of the cells. The retinal image (RI) can be divided into subpixels of a certain size, cells larger than subpixels can be classified as retinal ganglion cells (RGC), and cells smaller than subpixels can be classified as horizontal cells (AC) . Also, considering that the border, which is the border region of the horizontal cell (AC), is displayed darker than the center region, the horizontal cell (AC) can be selected first. That is, cells larger than a certain size can be selected as retinal ganglion cells (RGC) in the remaining cells except for the selected horizontal cells (AC).

Here, the retinal image RI may be an image of a specific region, and may be provided in plurality. Each retinal imaging image (RI) can be screened for horizontal cells, retinal ganglion cells. A plurality of retinal image images (RI) having been sorted can be combined into one image representing the entire retina, and based on this, a density distribution map of retinal ganglion cells showing the distribution of retinal ganglion cells (RGC) . However, the present invention is not limited thereto. The retinal image RI may be an image of the entire retina, and the density distribution map of the retinal ganglion cells may be obtained using the retinal image RI provided by the retinal image pickup unit 100 . The density distribution map (DM) of the retinal ganglion cells can be generated in the cell density distribution generation unit 110.

Further, in some embodiments, the cell density distribution generation unit 110 can distinguish normal retinal ganglion cells from abnormal retinal ganglion cells. The selected abnormal retinal ganglion cells may be deducted from the number of normal retinal ganglion cells, but the present invention is not limited thereto. The abnormal retinal ganglion cells may be separately reflected in the density distribution map of retinal ganglion cells.

Then, function information is generated (S120).

The function information may be information that can directly provide the subject's current retina and optic nerve state. Retina and optic nerve function information can be generated by analyzing the retinal image (RI) and the density distribution map (DM) of retinal ganglion cells. Retinal and optic nerve function information may include at least the number of retinal ganglion cells in the current patient, the density distribution of retinal ganglion cells and the number of abnormal retinal ganglion cells, Or not. The retinal and optic nerve function information is generated in the function information generating unit 130. The function information generating unit 130 may receive the density distribution map DM of the retinal ganglion cells and the retinal image RI from the data storage unit 120 and analyze the information to generate the retina and optic nerve function information have.

In addition, the retina and optic nerve function information may include data obtained by comparing the density map of the retinal ganglion cell and the retinal ganglion cell obtained in the past with the current one. That is, the retina and optic nerve function information may be generated once a year by performing periodic screening, etc., and may be provided to compare and analyze the subject's retina and optic nerve state.

In addition, the distribution map of retinal ganglion cells according to some embodiments may separately display abnormal retinal ganglion cells. Information related to these abnormal retinal ganglion cells can be included in the retina and optic nerve function information, and provides an increased number of abnormal retinal ganglion cells to enable early diagnosis of retinal and optic nerve diseases.

The retina and optic nerve function information output method according to an embodiment of the present invention can provide information on the retina and the optic nerve state to the subject by analyzing the retinal image and the density distribution map of the retinal ganglion cell.

The retinal image is generated by imaging the retina of the subject. The production process of the retina is transient and does not directly affect the human body. In addition, the retinal image can be precisely generated to visually distinguish retinal ganglion cells and other cells, and its density distribution map is automatically generated, and even if it does not require a high level of medical instruction or experience by a physician The subject can easily provide information on the retinal disease.

The operation by the method of outputting the retinal and optic nerve function information according to the embodiments described above can be at least partially implemented in a computer program and recorded on a computer readable recording medium. A program for realizing an operation by a method of outputting retina and optic nerve function information according to the embodiments is recorded and a computer-readable recording medium includes all kinds of recording devices for storing data that can be read by a computer do. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. The computer readable recording medium may also be distributed over a networked computer system so that computer readable code is stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment may be easily understood by those skilled in the art to which this embodiment belongs.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments or constructions, It will be understood that the invention can be variously modified and changed without departing from the spirit and scope of the invention.

10: Retina and optic nerve function evaluation system
100: retinal image capturing unit
110: cell density distribution generating unit
120: Data storage unit
130:

Claims (11)

A retinal image capturing unit for capturing an eyeball to generate a retinal image; And
And a cell density distribution generating unit for identifying at least the retinal ganglion cells and horizontal cells in the retinal image and generating a density distribution map of retinal ganglion cells indicating the distribution of the retinal ganglion cells. The method according to claim 1,
Wherein the cell density distribution generating unit classifies the retinal ganglion cells and the horizontal cells in consideration of the size and shape of cells in the retinal image. 3. The method of claim 2,
The cell density distribution generator divides the retinal image into sub-pixels of a predetermined size, classifies cells larger than the sub-pixels into the retinal ganglion cells, and classifies cells smaller than the sub-pixels into the horizontal cells. Optic nerve function evaluation system. The method of claim 3,
Wherein the cell density distribution generation unit selects a horizontal cell by observing a change in shading in a boundary region of the horizontal cell. The method according to claim 1,
The retina image capturing unit includes:
A light irradiating unit for irradiating light to the eyeball; and an optical compensator for receiving light reflected from the eyeball and compensating for aberration of light generated in the eyeball. The method according to claim 1,
A data storage unit for storing the retinal image and the cell density distribution map of the retinal ganglion;
And a function information generating unit for generating retina and optic nerve function information by analyzing the retinal image and the cell density distribution map of the retinal ganglion. Capturing an eyeball to generate a retinal image;
Identifying at least retinal ganglion cells and horizontal cells in the retinal image and generating a cell density distribution map indicating the distribution of the retinal ganglion cells; And
And analyzing the retinal image and the cell density distribution map to generate functional information. 8. The method of claim 7,
Wherein the retinal ganglion cells and the horizontal cells are classified according to the size and shape of cells in the retinal image. 9. The method of claim 8,
The retinal image is divided into subpixels of a predetermined size,
And classifying cells larger than the subpixel as the retinal ganglion cells and classifying cells smaller than the subpixel as the horizontal cells. 8. The method of claim 7,
Wherein the retinal image is generated by irradiating light to the eyeball and compensating for aberration of light generated in the eyeball by receiving light reflected from the eyeball. 8. The method of claim 7,
Further comprising the step of storing the retinal image and the cell density distribution map of the retinal ganglion.

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