JP5777307B2 - Image processing apparatus, image processing method, and program. - Google Patents

Description

  The present invention relates to an image processing apparatus for displaying a tomographic image of an eye part, and more particularly to an image processing apparatus for displaying information related to a layer thickness of an eye part.

  An ophthalmic tomographic imaging apparatus such as an optical coherence tomography (OCT) can observe the state inside the retinal layer three-dimensionally. This tomographic imaging apparatus has attracted attention in recent years because it is useful for more accurately diagnosing diseases.

  In ophthalmologic diagnosis, there are cases where a volume image is used to grasp the state of the entire retinal layer and a high-quality two-dimensional tomographic image is used to grasp a layer that does not appear in a low-quality tomographic image. A volume image means a set of two-dimensional tomographic images.

  FIG. 3 shows a schematic diagram of a macular tomographic image of the retina taken by OCT. FIG. 3 shows each of the layers 1 to 10 in the retinal layer and one pixel column A (A-scan) parallel to the z-axis direction. The tomographic image is composed of a plurality of A-scans located on the same plane. For example, when such a tomographic image is input, if the thickness of the layer can be measured, it is possible to quantitatively diagnose the degree of progression of a disease such as glaucoma and the degree of recovery after treatment. In order to quantitatively measure the thickness of these layers, a technique is disclosed in which the boundary of each layer of the retina is detected from a tomographic image using a computer and the thickness of the layers is measured (see Patent Document 1).

JP 2008-073099 A

However, in Patent Document 1, there is no idea of associating thickness information between a plurality of layers. Therefore, it may be difficult to grasp the layer thickness distribution of a plurality of layers.
The present invention has been made in view of the above problems, and provides an image processing apparatus that easily grasps layer information of a plurality of layers by displaying a plurality of layer information in association with each other.

In order to achieve the above object, an image processing apparatus according to an aspect of the present invention provides:
An acquisition means for acquiring a plurality of layer thicknesses by detecting a plurality of layer boundaries from a tomographic image of the eye;
A layer thickness graph creating means for creating a layer thickness graph based on the layer thickness of each of the plurality of layers, the layer thickness graph being shown in the same graph with a layer boundary of one of the plurality of layers as a reference line When,
Display control means for displaying on the display means a tomographic image of the eye part in which the layer thickness graph and the lines indicating the detected plurality of layer boundaries are superimposed ;
The layer thickness graph created by the layer thickness graph creating means is characterized by being associated with a line indicating a layer boundary of the tomographic image of the eye part by a line color or a line type .

  According to the present invention, it is easy to grasp layer information of a plurality of layers by displaying the plurality of layer information in association with each other.

It is a figure which shows the structure of an image processing system. It is a flowchart which shows the flow of a process in an image processing apparatus. It is a schematic diagram of a macular tomographic image of the retina. It is a figure for demonstrating a layer thickness graph. It is a figure which shows an example of a display of an image processing apparatus. It is a figure which shows an example of a display of an image processing apparatus. It is a figure which shows the structure of an image processing system. It is a flowchart which shows the flow of a process in an image processing apparatus. It is a figure which shows an example of a display of an image processing apparatus.

[Example 1]
Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. The ophthalmologic apparatus according to the present embodiment is characterized in that the thicknesses of a plurality of layers are displayed with high visibility on a single graph with respect to a reference layer boundary.
Hereinafter, details of the image processing system including the image processing apparatus according to the present embodiment will be described.

  FIG. 1 is a diagram illustrating a configuration of an image processing system 100 including an image processing apparatus 110 according to the present embodiment. As shown in FIG. 1, the image processing system 100 is configured by connecting an image processing apparatus 110 to a tomographic imaging apparatus 120 via an interface.

  The tomographic imaging apparatus 120 is an apparatus that captures a tomographic image of the eye, and includes, for example, a time domain type OCT or a Fourier domain type OCT. Since the tomographic imaging apparatus 120 is a known apparatus, detailed description thereof is omitted.

The image processing apparatus 110 includes an image acquisition unit 111, a storage unit 112, a layer analysis unit 113, and a display control unit 114. The layer analysis unit 113 includes an acquisition unit 115, a layer thickness calculation unit 116, and a layer thickness graph creation unit 117. The image acquisition unit 111 acquires a tomographic image captured by the tomographic image capturing apparatus 120 and stores it in the storage unit 112. The acquisition unit 115 detects a layer from the tomographic image stored in the storage unit 112. The layer thickness calculation unit 116 calculates the layer thickness detected by the acquisition unit 115 , and the layer thickness graph creation unit 117 creates a layer thickness graph from the calculated layer thickness. Next, a processing procedure of the image processing apparatus 110 according to the present embodiment will be described with reference to FIG.

<Step S201>
In step S <b> 201, the image acquisition unit 111 acquires a tomographic image from the tomographic image capturing apparatus 120. Then, the tomographic image is stored in the storage unit 112.

<Step S202>
In step S202, the acquisition unit 115 detects each layer inside the retinal layer from each A-scan of the tomographic image acquired in step S201. The layer detection inside the retinal layer will be described with reference to FIG. FIG. 3 shows a macular tomographic image of the retina. 1 is inner boundary membrane (ILM), 2 is nerve fiber layer (NFL), 3 is ganglion cell layer (GCL), 4 is inner plexiform layer (IPL), 5 is inner granule layer (INL), 6 is outer reticular Layer (OPL), 7 is the outer granular layer (ONL), 8 is the outer boundary membrane (ELM), 9 is the photoreceptor inner-node outer joint (IS / OS), 10 is the retinal pigment epithelial layer (RPE) . The acquisition unit 115 detects at least one of the layers 1 to 10. Here, the acquisition unit 115 detecting a layer is the same as detecting the boundary between layers. For example, the nerve fiber layer 2 is detected by detecting the boundary between the inner boundary membrane 1 at the boundary between the vitreous body and the retina and the nerve fiber layer 2 / ganglion cell layer 3.

  Regarding detection of the retinal layer, first, an image is created by applying a median filter and a Sobel filter to a tomographic image (hereinafter referred to as a median image and a Sobel image). Next, a profile is created for each A-scan from the converted median image and Sobel image. The median image has a luminance value profile, and the Sobel image has a gradient profile. Then, a peak in the profile created from the Sobel image is detected. The retinal layer is detected by referring to the median image profile before and after the detected peak and between the peaks.

<Step S203>
In step S203, the layer thickness calculator 116 calculates the thickness of the retinal layer. This process will be described with reference to FIG. For example, it is assumed that the boundary between the inner boundary membrane 1 and the nerve fiber layer 2 / ganglion cell layer 3 is detected in step S202. In this case, the thickness of the nerve fiber layer 2 is calculated. The thickness of the layer can be calculated by obtaining the difference between the z coordinate of the inner boundary membrane 1 and the z coordinate of the nerve fiber layer 2 / ganglion cell layer 3 boundary at each x coordinate on the xz plane. Furthermore, the layer thickness calculation unit 116 may obtain not only the thickness but also the area and volume of the layer. The area of the nerve fiber layer 2 can be calculated by adding the layer thickness at each coordinate point on the x axis in one tomographic image. The volume of the nerve fiber layer 2 can be calculated by adding the obtained area in the y-axis direction. The calculation result obtained by the layer thickness calculation unit 116 is stored in the storage unit 112.

  Here, the calculation of the nerve fiber layer 2 has been described as an example, but the thickness, area, and volume of the other layers and the entire retinal layer can be obtained in the same manner.

<Step S204>
In step S204, the layer thickness graph creating unit 117 selects a layer for displaying the result of the thickness in the layer thickness graph. As a layer to be displayed in the layer thickness graph, a layer determined at initialization stored in the storage unit 113 may be selected, or a layer may be selected according to a disease name. As an example of a disease name acquisition method in the case of selecting a layer according to a disease name, a disease name is acquired based on an input tomographic image capturing pattern by associating a scan pattern at the time of imaging with a disease name. Or the disease name described in patient information is acquired. Alternatively, when the acquisition unit 115 detects a retinal layer from a tomographic image, there is a method of determining a disease name from an image feature or a shape feature of the retinal layer.

  In this embodiment, a case where a tomographic image of a glaucoma eye is input will be described.

<Step S205>
In step S205, the layer thickness graph creating unit 117 creates a layer thickness graph from the layer thickness obtained in step S203. This process will be described with reference to FIG. FIG. 4 is an example of creating a layer thickness graph of the retinal ganglion cell complex (GCC) in which three layers of the nerve fiber layer 2, the ganglion cell layer 3, and the inner plexiform layer 4 are totaled.

  In glaucoma, the thickness of the ganglion cell layer 3 and the nerve fiber layer 2 decreases as ganglion cells die. Therefore, by evaluating the layer thickness using the retinal ganglion cell complex that is the total of the three layers of the nerve fiber layer 2, the ganglion cell layer 3, and the inner plexiform layer 4, the progress of glaucoma, the effect of treatment, etc. See. Among these three layers, the ganglion cell layer 3 has the largest change in layer thickness, then the nerve fiber layer 2, and finally the inner plexiform layer 4. Therefore, by displaying the change in the total thickness and the change in the thickness of each layer at the same time, it is possible to see the correlation of the change in the thickness of each layer.

  In the layer thickness graph creation processing, there are processing for associating a layer with a layer thickness graph in a tomographic image and processing for selecting a reference layer. This will be described with reference to FIG. 2B and FIG.

<Step S250>
In step S250, when creating one layer thickness graph including a plurality of layer thicknesses for the same eye, it is necessary to make it easy to understand which layer of the tomographic image corresponds to the layer thickness in the layer thickness graph. There is. Therefore, the layer thickness graph is created by making the color of the layer boundary line superimposed on the tomographic image the same as the color of the line of the layer thickness graph. The layer thickness graph creating unit 117 refers to the layer boundary line color table stored in the storage unit 112 and determines the line color. For example, in the layer border color table, the inner border membrane 1 is red, the border between the nerve fiber layer 2 / ganglion cell layer 3 is orange, the border between the ganglion cell layer 3 / inner plexiform layer 4 is blue, It is assumed that the boundary line between the inner mesh layer 4 and the inner granular layer 5 is stored as yellow. In that case, in the layer thickness graphs of FIGS. 4A to 4C, B1 is red, B2 is orange, B3 is blue, and B4 is yellow.

<Step S251>
In step S251, a reference layer for displaying a plurality of layer thicknesses is selected. As the reference layer, a predetermined layer can be selected.

  In FIGS. 4A and 4B, layer thickness graphs are created above and below a boundary located in a plurality of layers as a reference. FIGS. 4A and 4B are examples when the ganglion cell layer 3 is detected in step S202.

  FIG. 4A shows the boundary between the nerve fiber layer 2 / ganglion cell layer 3 as a reference (B2), the layer thickness of the nerve fiber layer 2 above the reference line, the ganglion cell layer 3 below the reference, A layer thickness graph of the inner plexiform layer 4 is created to represent the layer thickness of the entire retinal ganglion cell complex. Here, the thickness of the retinal ganglion cell complex and the thickness of each of the layers 2 to 4 are displayed, but focusing on the nerve fiber layer 2 and the ganglion cell layer 3, the nerve fiber layer 2 and the ganglion cell layer 3 are displayed. It is good also as a graph.

  FIG. 4B shows the boundary between the nerve fiber layer 2 / ganglion cell layer 3 as a reference line (B2), and the thickness of the nerve fiber layer 2 above the reference line and the thickness of the ganglion cell layer 3 below the reference. This is an example created as a graph. In FIG. 4B, N2 and N3 indicate a normal eye database of layer thicknesses obtained by measuring the layer thickness with a plurality of normal eyes. Here, N2 represents a normal eye database of the thickness of the nerve fiber layer 2, and N3 represents a normal eye database of the thickness of the ganglion cell layer 3. The reason why the normal eye database has a band shape is that the variation in the layer thickness when the layer thicknesses of a plurality of normal eyes are measured is displayed in a band shape. That is, if the layer thickness graph does not fall within this band-shaped range, it indicates that the layer thickness is out of the normal value.

  FIG. 4C illustrates an example in which a layer thickness graph is created on the basis of a layer boundary located at the deepest part of the layer to be displayed (positive direction of the z coordinate).

  FIG. 4C is an example when the ganglion cell layer 3 is not detected in step S202. The boundary between the inner mesh layer 4 / inner granule layer 5 is taken as a reference line (B4), and a layer thickness graph of the nerve fiber layer 2, the ganglion cell layer 3 + the inner mesh layer 4 is created on the reference. Although not shown here, even when the layer thickness graph is displayed only on one side of the reference as shown in FIG. 4C, the layer thickness graph including the normal eye database of the layer thickness as shown in FIG. 4B. May be created.

  In addition, although the example in the case of creating the layer thickness graph of the nerve fiber layer 2, the ganglion cell layer 3, and the inner plexiform layer 4 in Step S205 to Step S251 is shown, it is not limited to these layers. A layer thickness graph can be created in the same manner for the other layers. For example, age-related macular degeneration is a disease in which an abnormality occurs in the macula due to an aging phenomenon, and the retina is partially uneven. Therefore, in order to quantify the degree of the disease, how much the shape has changed from the original shape of the retina is measured. At that time, a layer thickness graph is created by measuring the thicknesses of the inner boundary membrane 1 and the retinal pigment epithelium layer 10 with reference to the estimated position of the retinal pigment epithelium layer (not shown). Here, the estimated position of the retinal pigment epithelium layer is an estimate of the shape of the retinal pigment epithelium layer when it is not age-related macular degeneration from the shape of the retinal pigment epithelium layer 10 detected by the acquisition unit 115. As an estimation method, the shape is estimated by an arbitrary function (for example, a quadratic curve) from the detected coordinate point of the retinal pigment epithelium layer 10 using an M estimation method or the like. Alternatively, when there is shape data of the retinal pigment epithelium layer prior to age-related macular degeneration in the same eye to be examined, the shape may be estimated using the past data. Alternatively, when there is general retinal pigment epithelial layer shape data created from a plurality of normal eyes, the general data may be used to estimate the shape.

<Step S206>
In step S206, the display control unit 114 displays the tomographic image, the layer detection result detected by the acquisition unit 115, and the layer thickness graph created by the layer thickness graph creation unit 117 on a display unit (not shown). This process will be described with reference to FIG. FIG. 5 shows an example in which the tomographic image 501 and the layer thickness graph 502 are displayed on the display unit. In FIG. 5, B <b> 1 ′ to B <b> 4 ′ are obtained by superimposing and displaying on the tomographic image 501 the result of the acquisition unit 115 detecting the boundary of each layer. FIG. 5A determines the color of the layer boundary line with reference to the layer boundary color table stored in the storage unit 112 when the layer boundary lines B1 ′ to B4 ′ are superimposed on the tomographic image 501. To do. Therefore, in the present embodiment, the layer boundary lines superimposed and displayed on the tomographic image 501 are red for B1 ′, orange for B2 ′, blue for B3 ′, and yellow for B4 ′. FIG. 5B shows an example in which the entire layer displayed in the layer thickness graph 502 is colored to take a correspondence. In this embodiment, the nerve fiber layer 2 is orange, the ganglion cell layer 3 is blue, and the inner plexiform layer 4 is yellow. When displaying the color on the entire retinal layer, the transparency is set and the display is translucent. The transparency can be changed arbitrarily.

  FIG. 6 shows a display example of a tomographic image 601, a layer thickness graph 602, and layer boundary lines B <b> 1 ′ to B <b> 4 ′ when a correspondence relationship other than colors is taken. FIG. 6A is a display example when the layer boundary lines B1 ′ to B4 ′ superimposed on the tomographic image and the types of the layer B1 to B4 of the layer thickness graph (solid line, dotted line, single point difference line, etc.) are aligned. . FIG. 6B is an example in which the name of the corresponding layer is displayed in the layer thickness graph 602. In this case, the name of the layer corresponding to the inside of the layer thickness graph is displayed, but the name of the boundary line of the layer may be displayed beside the graph line. Any name may be used as long as it can determine the actual retinal layer, such as a nerve fiber layer, NFL, and naver fiber layer.

  In addition, although not shown, association may be performed by connecting the graph line of the layer thickness displayed in the layer thickness graph and the layer in the tomographic image with a line.

  Note that only one method of correspondence between the layers and the layer thickness graphs shown in FIGS. 5 to 6 may be used, or a plurality of methods may be used in combination. As an example of combining a plurality of layers, the layer boundary line and the layer thickness graph line may have the same color and a layer name may be displayed inside the layer thickness graph.

<Step S207>
In step S207, an instruction acquisition unit (not shown) acquires an instruction from the outside as to whether or not the tomographic image processing by the image processing apparatus 110 is to be terminated. This instruction is input by an operator using a user interface (not shown). When an instruction to end the process is acquired, the image processing apparatus 110 ends the process. On the other hand, when the layer thickness to be displayed is changed without ending the process, the process returns to step S204 to create another layer thickness graph and display the result.
Thus, the processing of the image processing apparatus 110 is performed.

  According to the configuration described above, the thicknesses of a plurality of layers are displayed on one graph with high visibility with respect to the reference layer boundary. Therefore, the correlation of the thickness change of each layer can be seen by displaying the change of the whole layer thickness and the thickness change of each layer simultaneously.

[Example 2]
In the first embodiment, the method of displaying a plurality of layers on one graph with high visibility for the same eye has been described. This embodiment is characterized in that a plurality of layer thicknesses of different eyes are displayed on one graph.

  FIG. 7 is a diagram illustrating a configuration of an image processing system 700 including the image processing apparatus 710 according to the present embodiment. As shown in FIG. 7, the image processing apparatus 710 includes an image acquisition unit 111, a storage unit 712, a layer analysis unit 713, a display control unit 714, an acquisition unit 115, a layer thickness calculation unit 116, and a layer thickness graph creation unit 717. Prepare. Of these, those having the same functions as those of the first embodiment are not described here.

  The layer thickness graph creating unit 717 creates a layer thickness graph in which a plurality of layer thicknesses of different eyes are combined into one graph. The different eyes are, for example, the right eye and the left eye, and the past and present of the same eye. In this embodiment, the case of the right eye and the left eye will be described.

  Hereinafter, the processing procedure of the image processing apparatus 710 of this embodiment will be described with reference to FIGS. 8 and 9. Since steps other than step S805, step S850, and step S806 are the same as in the first embodiment, description thereof is omitted.

<Step S805>
In step S805, the layer thickness graph creating unit 717 creates a layer thickness graph from the layer thickness obtained in step S803. In this embodiment, an example of creating a layer thickness graph of the nerve fiber layer 2 and the ganglion cell layer 3 will be described.

<Step S850>
When creating one layer thickness graph including a plurality of layer thicknesses of different eyes in step S850, it is necessary to make it easy to understand which layer of the tomographic image corresponds to the layer thickness in the layer thickness graph. is there. Therefore, the layer thickness graph is created by making the color of the layer boundary line superimposed on the tomographic image the same as the color of the graph line of the layer thickness graph. The layer thickness graph creation unit 717 determines a line color with reference to the layer boundary color table 1 and the layer boundary color table 2 stored in the storage unit 712. For example, in the layer boundary color table 1, the inner boundary film 1 is red, the boundary line between the nerve fiber layer 2 / ganglion cell layer 3 is orange, and the boundary line between the ganglion cell layer 3 / inner plexiform layer 4 is blue. Is stored as. In the layer boundary color table 2, the inner boundary film 1 is pink, the boundary line between the nerve fiber layer 2 / ganglion cell layer 3 is a yellow color, and the boundary line between the ganglion cell layer 3 / inner plexiform layer 4 is Stored as light blue. In this case, the layer boundary color table 1 is applied to the right eye graph line, and the layer boundary color table 2 is applied to the left eye graph line. The color combinations are not limited to those described here. In the layer boundary color tables 1 and 2, the colors indicating the same boundary line may have a complementary color relationship. Alternatively, the layer boundary color table 1 may be a primary color, and the layer boundary color table 2 may be a pastel color.

  Further, instead of matching the color of the layer boundary line and the color of the graph line of the layer thickness graph, the types of the layer boundary line and the graph line may be aligned. For example, when the layer boundary line type 1 is stored as a solid line and the layer boundary line type 2 is stored as a dotted line in the storage unit 712, the right eye graph line is a solid line and the left eye graph line is a dotted line.

When the graph line of the layer thickness of the right eye and the left eye is made into one layer thickness graph, the layer structure of the right eye and the left eye is symmetric, so the layer thickness of one eye is the center of x Swap the coordinates at the center. In this embodiment, the thickness of the left eye is switched. For example, the range of x coordinates detected the thickness of the layer if the 0 to n, the thickness Th ₀ of _{x 0,} replacing the thickness Th _n for _{x n.} Similarly, replacing the thickness Th ₁ of _{x 1,} the thickness _{Th n-1} of _{x n-1.} By doing so, it is possible to compare the thicknesses of the symmetrical layers with the same coordinates. When comparing the past and present of the same eye, the above processing is not performed.

<Step S806>
In step S806, the display control unit 714 displays the tomographic image, the layer detection result detected by the acquisition unit 115, and the layer thickness graph created by the layer thickness graph creation unit 717 on a display unit (not shown).

  This process will be described with reference to FIG. FIG. 9 shows an example of displaying a right-eye tomographic image 901, a left-eye tomographic image 902, and a layer thickness graph 903 on the display unit. In FIG. 9, Br <b> 1 ′ to Br <b> 3 ′ and B <b> 1 ′ to B <b> 3 ′ are obtained by superimposing and displaying on the tomographic images 901 and 902 the results of the acquisition unit 115 detecting the boundaries between the layers. When layer boundaries Br1 ′ to Br3 ′ and Bl1 ′ to Bl3 ′ are superimposed on the tomographic images 901 and 902, the layer boundary color table 1 and the layer boundary color table 2 stored in the storage unit 712 are referred to. To determine the layer border color. Therefore, in the case of the present embodiment, the colors of the layer boundary lines superimposed and displayed on the tomographic image 901 are red for Br1 ′, orange for Br2 ′, and blue for Br3 ′, and are superimposed on the tomographic image 902. As for the color of the boundary line, B11 'is pink, B12' is a mountain blow color, and B13 'is light blue.

  In addition, the layer boundary lines Br1 ′ to Br3 ′, Bl1 ′ to Bl3 ′ and the layer thickness graph lines Br1 to Br3, Bl1 to Bl3 (solid line, dotted line, one-point difference line, etc.) are not associated with colors. May be displayed together. For example, the layer boundary lines Br1 'to Br3' and the layer thickness graph lines Br1 to Br3 are solid lines, and the layer boundary lines Bl1 'to Bl3' and the layer thickness graph lines Bl1 to Bl3 are dotted lines.

  According to the configuration described above, a plurality of layer thicknesses of different eyes with respect to a reference layer boundary are displayed on one graph with high visibility. Therefore, the correlation of the thickness change of each layer can be seen by displaying simultaneously the change of the whole layer thickness of different eyes and the thickness change of each layer.

(Other embodiments)
Each of the above embodiments implements the present invention as an image processing apparatus. However, the embodiment of the present invention is not limited only to the image processing apparatus. The present invention can also be realized as software that runs on a computer. The CPU of the image processing apparatus controls the entire computer using computer programs and data stored in RAM and ROM. In addition, the execution of software corresponding to each unit of the image processing apparatus is controlled to realize the function of each unit.

DESCRIPTION OF SYMBOLS 100 Image processing system 110 Image processing apparatus 111 Image acquisition part 112 Storage part 113 Layer analysis part 114 Display control part 115 Acquisition part 116 Layer thickness calculation part 117 Layer thickness graph creation part 700 Image processing system 710 Image processing apparatus 712 Storage part 713 Layer Analysis unit 714 Display control unit 717 Layer thickness graph creation unit

Claims (6)

An acquisition means for acquiring a plurality of layer thicknesses by detecting a plurality of layer boundaries from a tomographic image of the eye;
A layer thickness graph creating means for creating a layer thickness graph based on the layer thickness of each of the plurality of layers, the layer thickness graph being shown in the same graph with a layer boundary of one of the plurality of layers as a reference line When,
Display control means for displaying on the display means a tomographic image of the eye part in which the layer thickness graph and the lines indicating the detected plurality of layer boundaries are superimposed ;
The layer thickness graph created by the layer thickness graph creating means is associated with a line indicating a layer boundary of the tomographic image of the eye part by a line color or a line type .   The layer thickness graph creating means includes a single graph above and below the reference line, a plurality of graphs above and below the reference line, a plurality of graphs above the reference line, and a plurality of graphs below the reference line. The image processing apparatus according to claim 1, wherein: Before KisoAtsu graph generator is according to claim 2, characterized in that to create each layer thickness graph based on the thickness of the plurality of layers obtained from each of the tomographic images above and below the reference line Image processing device. The image processing apparatus according to any one of claims 1 to 3 , wherein the layer thickness graph creating unit creates the layer thickness graph including a layer thickness of a normal eye represented by a band shape. . An acquisition step in which the acquisition unit acquires the layer thickness of each of the plurality of layers by detecting a plurality of layer boundaries from the tomographic image of the eye,
Based on the layer thickness of each of the plurality of layers, the layer thickness graph creating means creates a layer thickness graph showing the layer thickness of each of the plurality of layers in the same graph with one layer boundary as the reference line. Creating process,
A display control step of causing the display control means to display on the display means a tomographic image of the eye part in which the layer thickness graph and the lines indicating the detected plurality of layer boundaries are superimposed ;
An image processing method, wherein the layer thickness graph created by the layer thickness graph creating means is associated with a line indicating a layer boundary of the tomographic image of the eye part by a line color or a line type . A program for causing a computer to execute the image processing method according to claim 5 .