Image processing method, image processing apparatus, and storage medium

Abstract

It is desired to visualize blood vessels based on volume data of a subject's eye.SOLUTION: An image processing method performed by a processor includes acquiring an image depicting a choroid, performing enhancement processing to enhance contrast of the acquired image, performing binarization processing on the image subjected to the enhancement processing, and extracting a region corresponding to a choroidal blood vessel in the choroid from the image subjected to the binarization processing.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to an image processing method, an image processing device, and a program. [Background technology]

[0002] U.S. Patent No. 10,238,281 discloses a technique for generating volume data of a subject's eye using an optical coherence tomography. Conventionally, it has been desired to visualize blood vessels based on the volume data of the subject's eye. Summary of the Invention

[0003] The first aspect is an image processing method performed by a processor, the image processing method including the steps of acquiring an image showing the choroid, performing an enhancement process to enhance the contrast of the acquired image, performing a binarization process on the enhanced image, and extracting an area in the choroid corresponding to choroidal blood vessels from the binarized image.

[0004] The second aspect is an image processing device including an image acquisition unit that acquires an image showing the choroid, an enhancement processing unit that performs enhancement processing that enhances the contrast of the acquired image, a binarization processing unit that performs binarization processing on the enhanced image, and a region extraction unit that extracts a region corresponding to choroidal blood vessels in the choroid from the binarized image.

[0005] The third aspect is a program for performing image processing, which causes a processor to perform the following steps: acquiring an image showing the choroid; performing an enhancement process to enhance the contrast of the acquired image; performing a binarization process on the enhanced image; and extracting an area in the choroid corresponding to the choroidal blood vessels from the binarized image. [Brief explanation of the drawings]

[0006] [Figure 1]1 is a schematic configuration diagram of an ophthalmologic system according to an embodiment. [Figure 2] 1 is a schematic configuration diagram of an ophthalmologic apparatus according to an embodiment. [Figure 3] FIG. 2 is a schematic configuration diagram of a server. [Figure 4] FIG. 2 is an explanatory diagram of functions realized by an image processing program in a CPU of a server. [Figure 5] 10 is a flowchart illustrating an example of the flow of image processing by the server. [Figure 6] 10 is a flowchart showing an example of the flow of an image formation process of choroidal blood vessels. [Figure 7] 10 is a flowchart showing an example of the flow of first image processing by first blood vessel extraction processing. [Figure 8] 10 is a flowchart showing an example of the flow of second image processing by second blood vessel extraction processing. [Figure 9] 10 is a flowchart showing an example of the flow of third image processing by third blood vessel extraction processing. [Figure 10] FIG. 1 is a schematic diagram showing the relationship between the eyeball and the positions of vortex veins. [Figure 11] FIG. 1 is a diagram showing the relationship between OCT volume data and an en-face image. [Figure 12] FIG. 1 is a diagram showing an example of a fundus image of choroidal blood vessels including vortex veins. [Figure 13] FIG. 1 is a conceptual diagram of a three-dimensional image of a vortex vein. [Figure 14] FIG. 10 is an explanatory diagram relating to contrast enhancement processing. [Figure 15] FIG. 10 is an explanatory diagram relating to a minute region connection process. [Figure 16] FIG. 10 is a diagram showing an example of a stereoscopic image of choroidal blood vessels around a vortex vein. [Figure 17] FIG. 10 is a diagram showing an example of a display screen using a stereoscopic image of vortex veins. DETAILED DESCRIPTION OF THE INVENTION

[0007] An ophthalmologic system 100 according to an embodiment of the present disclosure will be described below with reference to the drawings. FIG. 1 shows a schematic configuration of an ophthalmologic system 100. As shown in FIG. 1, the ophthalmologic system 100 includes an ophthalmologic apparatus 110, a server apparatus (hereinafter referred to as "server") 140, and a display device (hereinafter referred to as "viewer") 150. The ophthalmologic apparatus 110 acquires fundus images. The server 140 stores, in association with patient IDs, a plurality of fundus images obtained by photographing the funduses of a plurality of patients using the ophthalmologic apparatus 110 and axial lengths measured by an axial length measuring device (not shown). The viewer 150 displays the fundus images acquired by the server 140 and analysis results.

[0008] The server 140 is an example of the "image processing device" of the present disclosure.

[0009] The ophthalmologic apparatus 110, the server 140, and the viewer 150 are connected to each other via a network 130. The network 130 may be any network such as a LAN, a WAN, the Internet, or a wide area Ethernet network. For example, if the ophthalmologic system 100 is established in a single hospital, a LAN may be used as the network 130.

[0010] The viewer 150 is a client in a client-server system, and a plurality of viewers 150 are connected via a network. A plurality of servers 140 may also be connected via a network to ensure system redundancy. Alternatively, if the ophthalmic apparatus 110 has an image processing function and an image viewing function of the viewer 150, the ophthalmic apparatus 110 can acquire, process, and view fundus images in a standalone state. Alternatively, if the server 140 has an image viewing function of the viewer 150, the configuration of the ophthalmic apparatus 110 and the server 140 can acquire, process, and view fundus images.

[0011] In addition, other ophthalmic devices (examination devices for visual field measurement, intraocular pressure measurement, etc.) and diagnostic support devices that perform image analysis using AI (Artificial Intelligence) may be connected to the ophthalmic device 110, the server 140, and the viewer 150 via the network 130.

[0012] Next, the configuration of the ophthalmologic apparatus 110 will be described with reference to FIG.

[0013] For ease of explanation, Scanning Laser Ophthalmoscope will be referred to as "SLO" and Optical Coherence Tomography will be referred to as "OCT."

[0014] When the ophthalmologic apparatus 110 is placed on a horizontal plane, the horizontal direction is defined as the "X direction," the vertical direction relative to the horizontal plane is defined as the "Y direction," and the direction connecting the center of the pupil of the anterior segment of the subject's eye 12 and the center of the eyeball is defined as the "Z direction." Therefore, the X direction, Y direction, and Z direction are perpendicular to each other.

[0015] The ophthalmologic apparatus 110 includes an imaging device 14 and a control device 16. The imaging device 14 is equipped with an SLO unit 18 and an OCT unit 20, and acquires a fundus image of the subject's eye 12. Hereinafter, a two-dimensional fundus image acquired by the SLO unit 18 will be referred to as an SLO image. Also, a tomographic image or a front image (en-face image) of the retina created based on OCT data acquired by the OCT unit 20 will be referred to as an OCT image.

[0016] The control device 16 comprises a computer having a central processing unit (CPU) 16A, random access memory (RAM) 16B, read-only memory (ROM) 16C, and input / output (I / O) ports 16D.

[0017] The control device 16 includes an input / display device 16E connected to the CPU 16A via an I / O port 16D. The input / display device 16E has a graphic user interface that displays an image of the subject's eye 12 and receives various instructions from the user. An example of the graphic user interface is a touch panel display.

[0018] The control device 16 also includes an image processor 17 connected to an I / O port 16D. The image processor 17 generates an image of the subject's eye 12 based on data obtained by the photographing device 14. The control device 16 is connected to a network 130 via a communication interface (I / F) 16F.

[0019] As described above, in FIG. 2, the control device 16 of the ophthalmic apparatus 110 includes the input / display device 16E, but the present disclosure is not limited to this. For example, the control device 16 of the ophthalmic apparatus 110 may not include the input / display device 16E, but may include a separate input / display device that is physically independent from the ophthalmic apparatus 110. In this case, the display device includes an image processing unit that operates under the control of a display control unit 204 (see FIG. 4) of the CPU 16A of the control device 16. The image processing unit may display an SLO image or the like based on an image signal instructed to be output by the display control unit 204.

[0020] The image capturing device 14 operates under the control of the CPU 16A of the control device 16. The image capturing device 14 includes an SLO unit 18, an image capturing optical system 19, and an OCT unit 20. The image capturing optical system 19 includes an optical scanner 22 and a wide-angle optical system 30.

[0021] The optical scanner 22 performs two-dimensional scanning in the X and Y directions with the light emitted from the SLO unit 18. The optical scanner 22 may be any optical element that can deflect a light beam, such as a polygon mirror or a galvanometer mirror, or a combination thereof.

[0022] The wide-angle optical system 30 combines the light from the SLO unit 18 and the light from the OCT unit 20 .

[0023] The wide-angle optical system 30 may be a reflective optical system using a concave mirror such as an elliptical mirror, a refractive optical system using a wide-angle lens, or a catadioptric system combining concave mirrors and lenses. By using a wide-angle optical system using an elliptical mirror or a wide-angle lens, it becomes possible to photograph the retina in the peripheral part of the fundus as well as the center of the fundus.

[0024] When a system including an elliptical mirror is used, the system using the elliptical mirror described in International Publication WO2016 / 103484 or International Publication WO2016 / 103489 may be used. The disclosures of International Publication WO2016 / 103484 and International Publication WO2016 / 103489 are each incorporated herein by reference in their entirety.

[0025] The wide-angle optical system 30 enables observation of the fundus over a wide field of view (FOV) 12A. The FOV 12A indicates the range that can be photographed by the imaging device 14. The FOV 12A can be expressed as a field of view. In this embodiment, the field of view can be defined by an internal illumination angle and an external illumination angle. The external illumination angle is the illumination angle of the light beam irradiated from the ophthalmic device 110 to the subject's eye 12, determined with the pupil 27 as the reference. The internal illumination angle is the illumination angle of the light beam irradiated to the fundus, determined with the center O of the eyeball as the reference. The external illumination angle and the internal illumination angle correspond to each other. For example, if the external illumination angle is 120 degrees, the internal illumination angle corresponds to approximately 160 degrees. In this embodiment, the internal illumination angle is 200 degrees.

[0026] Here, an SLO fundus image captured at an internal illumination angle of 160 degrees or more is referred to as a UWF-SLO fundus image. UWF stands for Ultra Wide Field. The wide-angle optical system 30, which provides an ultra-wide field of view (FOV) of the fundus, can capture images of the area from the posterior pole of the fundus of the subject's eye 12 beyond the equator, enabling the capture of structures present in the peripheral area of ​​the fundus, such as vortex veins.

[0027] The ophthalmologic apparatus 110 can capture an image of an area 12A with an internal illumination angle of 200°, with the center O of the eyeball of the subject's eye 12 as the reference position. The internal illumination angle of 200° corresponds to an external illumination angle of 110° with the pupil of the subject's eye 12 as the reference. In other words, the wide-angle optical system 30 irradiates laser light from the pupil at an angle of view of an external illumination angle of 110°, and captures an image of a fundus area of ​​200° with an internal illumination angle.

[0028] The SLO system is realized by a control device 16, an SLO unit 18, and an imaging optical system 19 shown in Fig. 2. The SLO system includes a wide-angle optical system 30, and therefore enables fundus imaging with a wide FOV 12A.

[0029] The SLO unit 18 includes a light source 40 for B light (blue light), a light source 42 for G light (green light), a light source 44 for R light (red light), and a light source 46 for IR light (infrared light (e.g., near-infrared light)), as well as optical systems 48, 50, 52, 54, and 56 that reflect or transmit the light from the light sources 40, 42, 44, and 46 and guide them into a single optical path. The optical systems 48 and 56 are mirrors, and the optical systems 50, 52, and 54 are beam splitters. The B light is reflected by the optical system 48, passes through the optical system 50, and is reflected by the optical system 54; the G light is reflected by the optical systems 50 and 54; the R light is transmitted through the optical systems 52 and 54; and the IR light is reflected by the optical systems 52 and 56 and is each guided into a single optical path.

[0030] The SLO unit 18 is configured to be switchable between a light source that emits laser light of different wavelengths, or a combination of light sources that emit light, such as a mode that emits R light and G light and a mode that emits infrared light. In the example shown in FIG. 2 , four light sources are provided: a light source 40 for B light, a light source 42 for G light, a light source 44 for R light, and a light source 46 for IR light, but the present disclosure is not limited to this. For example, the SLO unit 18 may further include a light source of white light, and may emit light in various modes, such as a mode that emits G light, R light, and B light, or a mode that emits only white light.

[0031] Light incident on the photographing optical system 19 from the SLO unit 18 is scanned in the X and Y directions by the optical scanner 22. The scanning light passes through the wide-angle optical system 30 and the pupil 27 and is irradiated onto the fundus. The light reflected by the fundus passes through the wide-angle optical system 30 and the optical scanner 22 and is incident on the SLO unit 18.

[0032] The SLO unit 18 includes a beam splitter 64 that reflects B light and transmits all light except B light from the posterior segment (fundus) of the subject's eye 12, and a beam splitter 58 that reflects G light and transmits all light except G light from the light that has passed through the beam splitter 64. The SLO unit 18 includes a beam splitter 60 that reflects R light and transmits all light except R light from the light that has passed through the beam splitter 58. The SLO unit 18 includes a beam splitter 62 that reflects IR light from the light that has passed through the beam splitter 60. The SLO unit 18 includes a B light detecting element 70 that detects B light reflected by the beam splitter 64, a G light detecting element 72 that detects G light reflected by the beam splitter 58, an R light detecting element 74 that detects R light reflected by the beam splitter 60, and an IR light detecting element 76 that detects IR light reflected by the beam splitter 62.

[0033] Light (reflected light reflected by the fundus) incident on the SLO unit 18 via the wide-angle optical system 30 and the optical scanner 22 is reflected by the beam splitter 64 and received by the B light detection element 70 in the case of B light, and is reflected by the beam splitter 58 and received by the G light detection element 72 in the case of G light. The incident light is transmitted through the beam splitter 58 in the case of R light, reflected by the beam splitter 60, and received by the R light detection element 74. The incident light is transmitted through the beam splitters 58 and 60 in the case of IR light, reflected by the beam splitter 62, and received by the IR light detection element 76. The image processor 17, which operates under the control of the CPU 16A, generates a UWF-SLO image using signals detected by the B light detection element 70, the G light detection element 72, the R light detection element 74, and the IR light detection element 76.

[0034] A UWF-SLO image generated using the signal detected by the B light detecting element 70 is referred to as a B-UWF-SLO image (B-color fundus image). A UWF-SLO image generated using the signal detected by the G light detecting element 72 is referred to as a G-UWF-SLO image (G-color fundus image). A UWF-SLO image generated using the signal detected by the R light detecting element 74 is referred to as an R-UWF-SLO image (R-color fundus image). A UWF-SLO image generated using the signal detected by the IR light detecting element 76 is referred to as an IR-UWF-SLO image (IR fundus image). UWF-SLO images include R-color fundus images, G-color fundus images, B-color fundus images, and even IR fundus images. Fluorescent UWF-SLO images captured using fluorescence are also included.

[0035] The control device 16 also controls the light sources 40, 42, and 44 to emit light simultaneously. By simultaneously photographing the fundus of the subject's eye 12 with B light, G light, and R light, a G-color fundus image, a R-color fundus image, and a B-color fundus image, each of which corresponds to a different position, are obtained. An RGB color fundus image is obtained from the G-color fundus image, the R-color fundus image, and the B-color fundus image. The control device 16 also controls the light sources 42 and 44 to emit light simultaneously, and by simultaneously photographing the fundus of the subject's eye 12 with G light and R light, a G-color fundus image and a R-color fundus image, each of which corresponds to a different position, are obtained. An RG color fundus image is obtained from the G-color fundus image and the R-color fundus image. A full-color fundus image may also be generated using the G-color fundus image, the R-color fundus image, and the B-color fundus image.

[0036] The wide-angle optical system 30 makes the field of view (FOV) of the fundus an ultra-wide angle, and can capture an image of the area from the posterior pole of the fundus of the subject's eye 12 beyond the equator.

[0037] The OCT system is realized by the control device 16, OCT unit 20, and imaging optical system 19 shown in FIG. 2. The OCT system includes a wide-angle optical system 30, which enables OCT imaging of the peripheral part of the fundus, similar to the above-described SLO fundus image capture. In other words, the wide-angle optical system 30, which provides an ultra-wide field of view (FOV) of the fundus, enables OCT imaging of the area from the posterior pole of the fundus of the subject's eye 12 beyond the equator 178. OCT data of structures present in the peripheral part of the fundus, such as vortex veins, can be acquired, and tomographic images of the vortex veins and the 3D structure of the vortex veins can be obtained by image processing the OCT data.

[0038] The OCT unit 20 includes a light source 20A, a sensor (detecting element) 20B, a first optical coupler 20C, a reference optical system 20D, a collimating lens 20E, and a second optical coupler 20F.

[0039] Light emitted from the light source 20A is branched by the first optical coupler 20C. One of the branched beams is collimated by the collimating lens 20E as measurement light and then enters the imaging optical system 19. The measurement light passes through the wide-angle optical system 30 and the pupil 27 and is irradiated onto the fundus. The measurement light reflected by the fundus passes through the wide-angle optical system 30 and enters the OCT unit 20, and then passes through the collimating lens 20E and the first optical coupler 20C and enters the second optical coupler 20F.

[0040] The other light beam emitted from the light source 20A and branched by the first optical coupler 20C is incident as reference light on the reference optical system 20D, passes through the reference optical system 20D, and then enters the second optical coupler 20F.

[0041] The light beams incident on the second optical coupler 20F, i.e., the measurement light beam reflected from the fundus and the reference light beam, interfere with each other to generate interference light. The interference light beam is received by the sensor 20B. The image processor 17, which operates under the control of the image processing unit 206 (see FIG. 4), generates OCT data detected by the sensor 20B. The image processor 17 can also generate OCT images, such as tomographic images and en-face images, based on the OCT data.

[0042] Here, the OCT unit 20 can scan a predetermined range (for example, a rectangular range of 6 mm x 6 mm) in one OCT imaging session. The predetermined range is not limited to 6 mm x 6 mm, but may be a square range of 12 mm x 12 mm or 23 mm x 23 mm, or a rectangular range such as 14 mm x 9 mm or 6 mm x 3.5 mm, or any other rectangular range. It may also be a circular range with a diameter of 6 mm, 12 mm, or 23 mm.

[0043] By using the wide-angle optical system 30, the ophthalmic apparatus 110 can scan the area 12A with an internal illumination angle of 200°. That is, by controlling the optical scanner 22, OCT imaging of a predetermined range including vortex veins is performed. The ophthalmic apparatus 110 can generate OCT data by the OCT imaging.

[0044] Therefore, the ophthalmologic apparatus 110 can generate OCT images, such as a tomographic image (B-scan image) of the fundus including the vortex vein, OCT volume data including the vortex vein, and an en-face image (a frontal image generated based on the OCT volume data) that is a cross section of the OCT volume data. Needless to say, the OCT image includes an OCT image of the center of the fundus (the posterior pole of the eyeball where the macula, optic disc, etc. are present).

[0045] The OCT data (or image data of the OCT image) is sent from the ophthalmic apparatus 110 to the server 140 via the communication interface 16F and stored in the storage device 254 described in FIG.

[0046] In this embodiment, the light source 20A is exemplified as a wavelength-swept type SS-OCT (Swept-Source OCT), but various types of OCT systems may also be used, such as SD-OCT (Spectral-Domain OCT) and TD-OCT (Time-Domain OCT).

[0047] Next, the configuration of the electrical system of the server 140 will be described with reference to Fig. 3. As shown in Fig. 3, the server 140 includes a computer main body 252. The computer main body 252 has a CPU 262, a RAM 266, a ROM 264, and an input / output (I / O) port 268. The input / output (I / O) port 268 is connected to a storage device 254, a display 256, a mouse 255M, a keyboard 255K, and a communication interface (I / F) 258. The storage device 254 is configured, for example, with a non-volatile memory. The input / output (I / O) port 268 is connected to the network 130 via the communication interface (I / F) 258. Therefore, the server 140 can communicate with the ophthalmologic apparatus 110 and the viewer 150.

[0048] The ROM 264 or the storage device 254 stores an image processing program (FIGS. 5 to 9).

[0049] The ROM 264 or the storage device 254 is an example of a "memory" in the present disclosure. The CPU 262 is an example of a "processor" in the present disclosure. The image processing program is an example of a "program" in the present disclosure.

[0050] The server 140 stores each piece of data received from the ophthalmic apparatus 110 in the storage device 254 .

[0051] The following describes various functions that are realized by the CPU 262 of the server 140 executing the image processing program. As shown in Fig. 4, the image processing program executed by the CPU 262 has a display control function, an image processing function, and a processing function. By the CPU 262 executing the image processing program having these functions, the CPU 262 functions as the display control unit 204, the image processing unit 206, and the processing unit 208. The image processing unit 206 is an example of the "image acquisition unit," "emphasis processing unit," and "region extraction unit" of the present disclosure.

[0052] Next, a main flowchart of image processing by server 140 will be described with reference to Fig. 5. CPU 262 of server 140 executes an image processing program, thereby realizing the image processing (image processing method) shown in Fig. 5.

[0053] First, in step S10, the image processing unit 206 acquires a fundus image from the storage device 254. The fundus image includes data related to the vortex vein to be displayed three-dimensionally, based on a user's instruction.

[0054] Next, in step S20, the image processing unit 206 acquires, from the storage device 254, OCT volume data including the choroid corresponding to the fundus image.

[0055] In the next step S30, the image processing unit 206 extracts choroidal blood vessels based on the OCT volume data and executes image formation processing of the choroidal blood vessels (described in detail later) to generate a stereoscopic image (3D image) of the vortex vein blood vessels.

[0056] Once the stereoscopic image (3D image) of the vortex vein blood vessels is generated, in step S40, the processing unit 208 outputs the generated stereoscopic image (3D image) of the vortex vein blood vessels, specifically, stores it in the RAM 266 or the storage device 254, and terminates the image processing.

[0057] Here, a display screen (an example of a display screen is shown in FIG. 17, which will be described later) containing a three-dimensional image of vortex veins is generated by the display control unit 204 based on a user instruction. The generated display screen is output as an image signal by the processing unit 208 to the viewer 150. The display screen is displayed on the display of the viewer 150.

[0058] Next, the image formation process of choroidal blood vessels in step S30 for generating a stereoscopic image of vortex veins (VV) will be described in detail with reference to FIG.

[0059] FIG. 10 shows the positional relationship between the choroid 12M and the vortex veins 12V1 and V2 in the eyeball. In FIG. 10, the mesh-like pattern represents the choroidal blood vessels of the choroid 12M. The choroidal blood vessels circulate blood throughout the entire choroid. Blood flows out of the eyeball through multiple (usually four to six) vortex veins present in the subject's eye 12. FIG. 10 shows the superior vortex vein 12V1 and the inferior vortex vein 12V2 present on one side of the eyeball. Vortex veins are often present near the equator. Therefore, to photograph the vortex veins present in the subject's eye 12 and the choroidal blood vessels around the vortex veins, for example, an ophthalmic apparatus 110 capable of scanning at an internal illumination angle of 200° is used.

[0060] First, in step S10, the image processing unit 206 acquires a fundus image and identifies vortex veins (VV) to be displayed in three dimensions. Here, as an example, a UWF-SLO image is acquired from the storage device 254 as a UWF fundus image. Next, the image processing unit 206 creates a choroidal vessel image, which is a binarized image, from the acquired UWF-SLO image. Then, the image processing unit 206 identifies a region designated by the user as a vortex vein to be displayed in three dimensions.

[0061] Fig. 12 is a fundus image of choroidal blood vessels including vortex veins. The fundus image shown in Fig. 12 is an example of a choroidal blood vessel image, which is a binarized image created from a UWF-SLO image. As shown in Fig. 12, the choroidal blood vessel image is a binarized image in which pixels corresponding to choroidal blood vessels and vortex veins are white and pixels in other areas are black.

[0062] 12 is an image 302 showing the presence of choroidal blood vessels connected to a vortex vein. Image 302 shows a case where a vortex vein 310V1, which is an image of a superior vortex vein 12V1 included in a user-specified area 310A, is identified as a vortex vein (VV) to be displayed in three dimensions, and a region including choroidal blood vessels is identified.

[0063] A choroidal vascular image including vortex veins (VVs) is generated by processing image data of an R-UWF-SLO image (a red-color fundus image) captured with red light (laser light with a wavelength of 630 to 660 nm) and a G-UWF-SLO image (a green-color fundus image) captured with green light (laser light with a wavelength of 500 to 550 nm). Specifically, the choroidal vascular image is generated by extracting retinal blood vessels from the green fundus image, removing retinal blood vessels from the red fundus image, and performing image processing to enhance the choroidal blood vessels. Regarding the method for generating a choroidal vascular image, the disclosure of International Publication WO 2019 / 181981 is incorporated herein by reference in its entirety.

[0064] Although the above describes a case where a vortex vein to be displayed in three dimensions is identified by a user's instruction, the present disclosure is not limited to this. The position of the vortex vein to be displayed in three dimensions may be detected manually or automatically. For example, in the case of manual detection, the position indicated by the user's visual inspection of the displayed choroidal vessels may be detected. In the case of automatic detection, for example, the choroidal vessels may be extracted from a choroidal vessel image, the movement direction of each choroidal vessel (the direction of blood vessel travel) may be estimated, and the position of the vortex vein may be estimated based on the position where the choroidal vessels converge.

[0065] Next, in step S31 of FIG. 6, the image processing unit 206 extracts a region corresponding to the choroid from the OCT volume data 400 (see FIG. 11) acquired in step S20, and extracts (acquires) OCT volume data of the choroid based on the extracted region.

[0066] 11 , the OCT volume data 400 is OCT volume data 400 of a predetermined area, for example, a rectangular region of 6 mm × 6 mm, including the vortex vein VV, obtained by OCT imaging of one of the multiple vortex veins VV present in the subject's eye using the ophthalmic apparatus 110. N planes at different depths, from a first plane f401 to an Nth plane f40N, are set for the OCT volume data 400. The OCT volume data 400 may be obtained by OCT imaging of each of the multiple vortex veins VV present in the subject's eye using the ophthalmic apparatus 110.

[0067] In this embodiment, the OCT volume data 400 including vortex veins and choroidal blood vessels around the vortex veins will be described as an example of the OCT volume data 400D. In this case, the choroidal blood vessels refer to the vortex veins and the choroidal blood vessels around the vortex veins.

[0068] Specifically, the image processing unit 206 extracts OCT volume data 400D of the region below the retinal pigment epithelium layer 400R (hereinafter referred to as the RPE layer) from OCT volume data scanned to include vortex veins and choroidal blood vessels surrounding the vortex veins in the OCT volume data 400 of the region where choroidal blood vessels are present.

[0069] First, the image processing unit 206 identifies the RPE layer 400R by performing image processing to identify the boundary surfaces of each layer on the OCT volume data 400. Alternatively, the most luminous layer in the OCT volume data may be identified as the RPE layer 400R.

[0070] The image processing unit 206 then extracts pixel data of the choroid region in a predetermined range deeper than the RPE layer 400R (a predetermined range of region farther than the RPE layer when viewed from the center of the eyeball) as OCT volume data 400D. Because OCT volume data in deep regions may not be uniform, the image processing unit 206 may extract, as OCT volume data 400D, the region from the RPE layer 400R to the bottom surface 400E obtained by the image processing described above that identifies the boundary surface, as shown in FIG. The region of the choroid in a predetermined range deeper than the RPE layer 400R is an example of the "choroidal portion" of the present disclosure.

[0071] Through the above processing, OCT volume data 400D for generating a stereoscopic image of the choroidal blood vessels is extracted.

[0072] Next, in step S32, the image processing unit 206 executes a first blood vessel extraction process (ampullary portion extraction) using the OCT volume data 400D. The first blood vessel extraction process is a process for extracting choroidal blood vessels (hereinafter referred to as ampullary portion) that form the ampullary portion, which is the first blood vessel. In the first blood vessel extraction process (ampullary portion extraction), the first image processing shown in FIG. 7 is executed.

[0073] In step S322, the image processing unit 206 performs binarization processing on the OCT volume data 400D as preprocessing for the first blood vessel extraction processing (dilation extraction). Specifically, by setting the binarization threshold to a predetermined threshold that leaves the vascular dilation, the vascular dilation in the OCT volume data D becomes black pixels and the other parts become white pixels.

[0074] Next, in step S324, the image processing unit 206 performs noise removal processing to remove noise regions from the binarized OCT volume data 400D. Specifically, the image processing unit 206 removes noise regions from the binarized OCT volume data 400D to extract first choroidal blood vessels, which are dilation regions, from the OCT volume data. This generates a first three-dimensional image. Noise regions may be isolated regions of black pixels or regions corresponding to thin blood vessels. To remove such noise regions, the image processing unit 206 performs median filtering, opening processing, contraction processing, or the like on the binarized OCT volume data 400D to remove the noise regions.

[0075] Furthermore, in step S326, the image processing unit 206 performs segmentation processing (image processing such as active contouring, graph cut, or U-net) on the OCT volume data from which the noise regions have been deleted in order to smooth the surface of the extracted bulging portion. This step S326 can be omitted. Note that "segmentation" here refers to image processing that performs binarization processing to separate the background and foreground of the image to be analyzed.

[0076] By performing such first blood vessel extraction processing, only the region of the dilation remains from the OCT volume data 400D, and a three-dimensional image 680B of the blood vessels of the dilation is generated as shown in Fig. 13. Image data of the three-dimensional image 680B of the blood vessels of the dilation is stored in the RAM 266 by the processing unit 208. The blood vessels of the ampulla shown in FIG. 13 are an example of the "first choroidal blood vessels" of the present disclosure, and the stereoscopic image 680B of the blood vessels of the ampulla is an example of the "first stereoscopic image" of the present disclosure.

[0077] In addition, in step S33 shown in FIG. 6, the image processing unit 206 executes a second blood vessel extraction process (thick blood vessel extraction) using the OCT volume data 400D. The second blood vessel extraction process is a process for extracting choroidal blood vessels (hereinafter referred to as thick blood vessels) that are thick linear second blood vessels extending from the dilation and exceed a predetermined threshold, i.e., a predetermined diameter. In the second blood vessel extraction process (thick blood vessel extraction), linear second blood vessels extending from the dilation are extracted. The thick blood vessels mainly indicate blood vessels located in the Haller layer. In the second blood vessel extraction process (thick blood vessel extraction), the second image processing shown in FIG. 8 is executed.

[0078] The predetermined threshold (i.e., predetermined diameter) can be a value that is predetermined so that blood vessels with a diameter of several hundred microns are left as thick blood vessels. The threshold value that is determined so that thin blood vessels are left as thick blood vessels, which will be described later, can be a value that is less than a diameter of several hundred microns that is determined so that blood vessels are left as thick blood vessels, or a value that is smaller than a value that is predetermined so that blood vessels are left as thick blood vessels. For example, a value that is predetermined so that blood vessels with a diameter of several tens of microns are left as thin blood vessels can be used.

[0079] First, in step S331 shown in Fig. 8, the image processing unit 206 executes image processing to perform pre-processing on the OCT volume data 400D. An example of the pre-processing is blurring processing to remove noise. The blurring processing can be a process that removes the influence of speckle noise and extracts linear blood vessels that accurately reflect the blood vessel shapes. Examples of speckle noise processing include Gaussian blurring processing.

[0080] In the next step S332, the image processing unit 206 performs line extraction processing (thick linear blood vessel extraction) on the pre-processed OCT volume data 400D to extract second choroidal blood vessels, which are thick linear portions, from the OCT volume data 400D.

[0081] Specifically, the image processing unit 206 performs image processing using, for example, an eigenvalue filter, a Gabor filter, or the like, and extracts linear blood vessel regions from the OCT volume data 400D. In the OCT volume data 400D, blood vessel regions are low-brightness pixels (dark pixels), and regions where low-brightness pixels are continuous remain as blood vessel portions.

[0082] In step S333, the image processing unit 206 performs binarization processing on the OCT volume data 400D. Specifically, by setting the binarization threshold to a predetermined threshold that leaves thick blood vessels, thick blood vessels in the OCT volume data D become black pixels and other parts become white pixels.

[0083] Furthermore, in step S334, the image processing unit 206 performs image processing such as a process to remove isolated areas that are not connected to surrounding blood vessels, a median filter process, an opening process, and a contraction process on the extracted and binarized linear blood vessel area to remove discrete micro-areas.

[0084] By the above image processing, a second stereoscopic image of the second choroidal vessels, which are thick blood vessels, is generated.

[0085] By performing the second blood vessel extraction process described above, only the regions of the thick blood vessels remain from the OCT volume data 400D, and a three-dimensional image 680L of the thick blood vessels is generated as shown in Fig. 13. The image data of the three-dimensional image 680L of the thick blood vessels is stored in the RAM 266 by the processing unit 208.

[0086] FIG. 16 shows an example of a stereoscopic image of the choroidal blood vessels around the vortex vein VV obtained by the image processing described above (FIG. 5). By performing the second blood vessel extraction process described above, only the thick blood vessel regions remain from the OCT volume data 400D, and a three-dimensional image 681L of the thick blood vessels is generated as shown in Fig. 16. Image data of this three-dimensional image 681L of the thick blood vessels is also stored in the RAM 266 by the processing unit 208. The linear blood vessels shown in FIGS. 13 and 16 are an example of the "second choroidal blood vessels" of the present disclosure, and the stereoscopic images 680L and 681L of the linear blood vessels are an example of the "second stereoscopic image" of the present disclosure.

[0087] The image processing unit 206 aligns the three-dimensional image 680B of the ampulla and the three-dimensional image 680L of the linear blood vessels and performs a logical OR operation on both images to synthesize the three-dimensional image 680L of the linear blood vessels and the three-dimensional image 680B of the ampulla. This makes it possible to generate a three-dimensional image 680M (FIG. 13) of the choroidal blood vessels, including vortex veins, which are thick blood vessels. In the process of extracting the thick blood vessels described above, thin blood vessels smaller than the predetermined diameter may be removed.

[0088] When observing vortex veins, it is important to observe not only the large blood vessels located in the Haller layer but also the small blood vessels located mainly in the Sattler layer. For example, analyzing the small blood vessels in the Sattler layer is effective for diagnosing pachychoroidal diseases. Therefore, the present disclosure includes a process for extracting choroidal vessels (hereinafter referred to as small blood vessels) that are thin, linear third blood vessels extending from the ampulla and have a diameter equal to or smaller than a predetermined threshold, i.e., a predetermined value.

[0089] Specifically, in step S34 shown in FIG. 6, the image processing unit 206 executes a third blood vessel extraction process (thin blood vessel extraction) using the OCT volume data 400D. The third blood vessel extraction process is a process for extracting choroidal blood vessels (hereinafter referred to as thin blood vessels) that are thin linear third blood vessels extending from the dilatation area and have a predetermined threshold value, i.e., a predetermined diameter or less. In the third blood vessel extraction process (thin blood vessel extraction), linear third blood vessels extending from the dilatation area are extracted. The thin blood vessels mainly indicate blood vessels located in the Sattler layer. In the third blood vessel extraction process (thin blood vessel extraction), the third image processing shown in FIG. 9 is executed.

[0090] In the process of extracting the third blood vessels, which are thin blood vessels, the image processing unit 206 performs preprocessing for thin blood vessels, including first preprocessing and second preprocessing, on the OCT volume data 400D. First, in step S341 shown in Fig. 9, image processing is executed to perform the first preprocessing on the OCT volume data 400D. An example of the first preprocessing is a blurring process similar to that in step S331 described above, which is an example of a process for removing noise.

[0091] In the next step S342, the image processing unit 206 executes image processing to perform second pre-processing on the OCT volume data 400D that has been subjected to the first pre-processing. Contrast enhancement processing is an example of the second pre-processing. Contrast enhancement processing is effective when extracting thin blood vessels. Contrast enhancement processing increases the contrast of the image compared to before processing, that is, increases the difference between light and dark. For example, the difference between the maximum and minimum values ​​of the degree of brightness (e.g., luminance) is increased by a predetermined value from the difference value before processing. The predetermined value can be set as appropriate. The contrast enhancement process is an example of the "enhancement process" of the present disclosure.

[0092] An example of an image related to the contrast enhancement process applied to the second pre-processing is shown in Fig. 14. In Fig. 14, an image of thin blood vessels is shown as a white image. Image G10 containing thin blood vessels in the OCT volume data 400D has lower contrast than images containing thick blood vessels, and when binarized after noise removal, the thin blood vessels may not be depicted, as shown in image G11. Therefore, when contrast enhancement processing is performed on image G10 containing thin blood vessels (image G12) and the image is binarized, the thin blood vessels appear as continuous lines, as shown in image G13, making it possible to reduce the separation of continuous thin blood vessels.

[0093] In step S342, the image processing unit 206 performs image processing using, for example, an eigenvalue filter, a Gabor filter, or the like, and is able to extract regions of linear blood vessels, which are thin blood vessels, from the OCT volume data 400D.

[0094] Next, the image processing unit 206 executes image processing to perform binarization on the contrast-enhanced OCT volume data 400D in step S343 shown in Fig. 9. Specifically, by setting the binarization threshold to a predetermined threshold that leaves thin blood vessels, thin blood vessels become black pixels and other parts become white pixels in the OCT volume data D.

[0095] Furthermore, in step S344, the image processing unit 206 removes discrete microregions from the binarized image (regions including thin blood vessels) in the same manner as in step S333 (FIG. 8). Here, for example, image processing such as removing speckle noise and isolated regions separated by a predetermined distance that are estimated to be discontinuous with the surrounding blood vessels is performed to remove the discrete microregions. Note that the removal of microregions can be performed by removing regions having a predetermined area or less. It is also possible to remove regions having a predetermined shape as the microregions. For example, it is possible to perform a process of approximating the discrete microregions to an ellipse, and remove regions where the approximated elliptical shape is equal to or smaller than a predetermined ellipticity as regions to be removed.

[0096] In the next step S345, the image processing unit 206 performs post-processing on the OCT volume data 400D from which the microregions have been removed, by performing microregion connection processing, thereby extracting third choroidal vessels, which are thin blood vessels in thin linear portions, from the OCT volume data 400D. Specifically, the image processing unit 206 performs image processing using, for example, morphology processing such as closing processing, to connect discretely detected thin blood vessels, thereby extracting third choroidal vessels, which are thin blood vessels, from the OCT volume data 400D. Specifically, the image processing unit 206 connects third choroidal vessels within a predetermined distance. The micro-region connection process is an example of the "connection process" of the present disclosure.

[0097] An example of an image related to the fine region connection processing is shown in Fig. 15. In Fig. 15, an image of a thin blood vessel is shown as a white image. Thin blood vessels may have a larger curvature than thick blood vessels. When the above-described line extraction process (step S332 shown in FIG. 8) is performed on an image including thin blood vessels with a larger curvature than the thick blood vessels, the thin blood vessels may not be extracted as a line structure. Therefore, when image G20 including thin blood vessels in the OCT volume data 400D is binarized, the thin blood vessels may not be depicted as having a large curvature, as shown in image G21. Therefore, by performing a fine region connection process on image G21, even thin blood vessels with a large curvature portion are displayed as a continuous line, as shown in image G22, making it possible to reduce the separation of continuous thin blood vessels.

[0098] Furthermore, in step S346, the image processing unit 206 performs segmentation processing (image processing such as active contouring, graph cut, or U-net) on the OCT volume data to which the fine regions are connected in order to smooth the surfaces of the extracted thin blood vessels. That is, the image to be analyzed is processed to separate the background from the foreground.

[0099] By the above image processing, a third three-dimensional image of the third choroidal vessels, which are thin blood vessels, is generated.

[0100] By performing the third blood vessel extraction process described above, only the thin blood vessel regions remain from the OCT volume data 400D, and a three-dimensional image 681S of the thin blood vessels shown in Fig. 16 is generated. The image data of the three-dimensional image 681S of the thin blood vessels is stored in the RAM 266 by the processing unit 208. The linear blood vessels, which are thin blood vessels, shown in FIG. 16 are an example of the "third choroidal blood vessels" of the present disclosure, and the three-dimensional image 681S of the thin blood vessels is an example of the "third three-dimensional image" of the present disclosure.

[0101] The processing of steps S32, S33, and S34 is not limited to the processing order described above, and any one of the processing may be executed first, or the processing may be executed simultaneously in parallel.

[0102] After the processing of steps S32, S33, and S34 is completed, in step S35, the image processing unit 206 reads out the three-dimensional image of the ampulla, the three-dimensional image of the large blood vessels, and the three-dimensional image of the small blood vessels from the RAM 266. Then, by aligning these three-dimensional images and calculating the logical sum of each image, the three-dimensional image of the ampulla, the three-dimensional image of the large blood vessels, and the three-dimensional image of the small blood vessels are synthesized. As a result, a three-dimensional image 681M (see FIG. 16 ) of the choroidal blood vessels including the vortex veins is generated. Image data of the three-dimensional image 681M is stored in the RAM 266 or the storage device 254 by the processing unit 208. The stereoscopic image 681M of the choroidal vessels including the vortex veins is an example of the "stereoscopic image of the choroidal vessels" of the present disclosure.

[0103] The following describes a display screen for displaying the generated stereoscopic image (3D image) of choroidal vessels including vortex veins. The display screen is generated by the display control unit 204 of the server 140 based on a user's instruction, and is output as an image signal to the viewer 150 by the processing unit 208. The viewer 150 displays the display screen on a display based on the image signal.

[0104] A display screen 500A is shown in Figure 17. As shown in Figure 17, the display screen 500A has an information area 502 and an image display area 504A. The image display area 504A includes a comment field 506 that displays the patient's medical history.

[0105] The information area 502 has a patient ID display field 512, a patient name display field 514, an age display field 516, a visual acuity display field 518, a right eye / left eye display field 520, and an axial length display field 522. In each display area from the patient ID display field 512 to the axial length display field 522, the viewer 150 displays the respective information based on the information received from the server 140.

[0106] The image display area 504A is an area that mainly displays an image of the eye to be examined, etc. The image display area 504A is provided with the following display fields, specifically, a UWF fundus image display field 542 and a choroidal blood vessel stereoscopic image display field 548. Although not shown in the figure, the image display area 504A can also display an OCT volume data conceptual diagram display field and a tomographic image display field 546 in a superimposed manner.

[0107] A comment field 506 included in the image display area 504A displays the patient's medical history and functions as a remarks field in which the user, an ophthalmologist, can arbitrarily input the results of his or her observations and diagnosis.

[0108] The UWF fundus image display field 542 displays a UWF-SLO fundus image 542B captured by the ophthalmologic apparatus 110 of the fundus of the subject's eye. A range 542A indicating the position where the OCT volume data was acquired is superimposed on the UWF-SLO fundus image 542B. If there are multiple OCT volume data associated with the UWF-SLO image, the multiple ranges may be superimposed and the user may select one position from the multiple ranges. FIG. 17 shows that the range including the vortex vein in the upper right corner of the UWF-SLO image was scanned.

[0109] The choroidal vessel stereoscopic image display field 548 displays a stereoscopic image (3D image) 548B of the choroidal vessels obtained by image processing the OCT volume data. The stereoscopic image 548B can be rotated around three axes by a user operation. Furthermore, the choroidal vessel stereoscopic image 548B can display an image of the second choroidal vessels (a stereoscopic image of the thick blood vessels) and an image of the third choroidal vessels (a stereoscopic image of the thin blood vessels) extending from the ampulla 548X in different display formats. In FIG. 17, a stereoscopic image 548L of the thick blood vessels extending from the ampulla 548X is shown by a solid line, and a stereoscopic image 548S of the thin blood vessels is shown by a dotted line. Furthermore, the stereoscopic image 548L of the thick blood vessels and the stereoscopic image 548S of the thin blood vessels may be displayed in different colors, or the background (fill-in) of the image may be different.

[0110] Furthermore, in the choroidal vessel stereoscopic image display field 548, it is possible to superimpose layers obtained by segmentation processing on the above-mentioned OCT volume data. Fig. 17 shows an example in which the layer boundary 548T is displayed as a long-dotted line. This boundary 548T can be used as a guide for identifying the Harrah layer and the Satra layer.

[0111] The image display area 504A of the display screen 500A allows a user to view a stereoscopic image of the choroidal vessels, including both large and small blood vessels. By scanning an area including a vortex vein, the vortex vein and the surrounding choroidal vessels, including both large and small blood vessels, can be displayed in a stereoscopic image, allowing the user to obtain more information for diagnosis.

[0112] Furthermore, the image display area 504A allows the user to grasp the position of the OCT volume data on the UWF-SLO image.

[0113] Furthermore, the image display area 504A allows the user to arbitrarily select a cross section of the stereoscopic image, and by displaying a tomographic image, the user can obtain detailed information about the choroidal blood vessels.

[0114] Furthermore, the three-dimensional display of choroidal blood vessels according to this embodiment can display the choroidal blood vessels three-dimensionally without using OCT-A (OCT-angiography). A three-dimensional image of the choroidal blood vessels can be generated without performing complex, computationally intensive processing such as subtracting OCT volume data to obtain motion contrast. While OCT-A requires multiple OCT volume data sets taken at different times to obtain the subtraction, this embodiment can generate a three-dimensional image of the choroidal blood vessels based on a single OCT volume data set without performing processing to extract motion contrast.

[0115] As described above, in this embodiment, vortex veins and choroidal vessels including thick and thin blood vessels in the vicinity thereof are extracted based on OCT volume data including the choroid, and three-dimensional images of each choroidal vessel are generated, making it possible to three-dimensionally visualize the choroid including thick and thin blood vessels.

[0116] Furthermore, in this embodiment, a three-dimensional image of choroidal blood vessels, including thick and thin blood vessels, is generated based on OCT volume data without using OCT-A (OCT-angiography). Therefore, in this embodiment, a three-dimensional image of choroidal blood vessels, including thick and thin blood vessels, can be generated without performing complex, computationally intensive processing such as subtracting OCT volume data and extracting motion contrast, thereby reducing the amount of calculation.

[0117] In the above embodiment, the image processing (FIG. 5) is performed by the server 140, but the present disclosure is not limited to this and may be performed by the ophthalmic device 110, the viewer 150, or an additional image processing device further provided on the network 130.

[0118] In the present disclosure, each component (device, etc.) may be present in one or more instances, unless a contradiction arises.

[0119] In the above-described examples, image processing is performed using a software configuration that utilizes a computer. However, the present disclosure is not limited to this configuration, and at least a portion of the processing may be performed using a hardware configuration. Furthermore, while the above description uses a CPU as an example of a general-purpose processor, the term "processor" refers to a processor in a broad sense and includes general-purpose processors (e.g., a CPU (Central Processing Unit), etc.) and dedicated processors (e.g., a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a programmable logic device, etc.). Therefore, image processing may be performed solely using a hardware configuration, or a portion of the image processing may be performed using a software configuration and the remaining processing may be performed using a hardware configuration.

[0120] Furthermore, the operations of the above-mentioned processors may not only be performed by a single processor, but may also be performed by multiple processors working together, or may be performed by multiple processors located in physically separate locations working together.

[0121] Furthermore, in order to cause a computer to execute the above-described processing, a program in which the above-described processing is written in computer-processable code may be stored on a storage medium such as an optical disk and distributed.

[0122] As such, the present disclosure includes both cases in which image processing is realized by a software configuration using a computer and cases in which it is not realized, and therefore includes the following techniques.

[0123] (First Technology) an acquisition unit for acquiring OCT volume data including the choroid; a generating unit that extracts choroidal vessels having a diameter greater than a predetermined diameter and choroidal vessels having a diameter equal to or smaller than a predetermined diameter based on the OCT volume data and generates a stereoscopic image of the choroidal vessels; An image processing device comprising:

[0124] (Second Technology) An acquisition unit acquires OCT volume data including the choroid; a generating unit extracting choroidal vessels having a diameter greater than a predetermined diameter and choroidal vessels having a diameter equal to or smaller than a predetermined diameter based on the OCT volume data, and generating a stereoscopic image of the choroidal vessels; An image processing method comprising: The image processing unit 206 is an example of the "acquisition unit" and the "generation unit" of the present disclosure. Based on the above disclosure, the following technology is proposed.

[0125] (Third Technology) 1. A computer program product for image processing, comprising: the computer program product comprises a computer-readable storage medium that is not itself a transitory signal; The computer-readable storage medium stores a program, The program The processor acquiring OCT volume data including the choroid; extracting choroidal vessels having a diameter greater than a predetermined diameter and choroidal vessels having a diameter equal to or smaller than a predetermined diameter based on the OCT volume data, and generating a stereoscopic image of the choroidal vessels; Let the Computer program products. Server 140 is an example of a "computer program product" of the present disclosure.

[0126] While the technology of the present disclosure has been described above using embodiments, the image processing described above is merely an example, and the technical scope of the present disclosure is not limited to the scope described in the above embodiments. Therefore, various modifications or improvements can be made to the above embodiments, such as deleting unnecessary processes, adding new processes, or changing the order of processes, without departing from the spirit of the present disclosure, and such modifications or improvements are also included in the technical scope of the present disclosure.

[0127] The disclosure of Japanese Patent Application No. 2022-066635 is incorporated herein by reference in its entirety. All documents, patent applications, and technical standards described herein are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually indicated to be incorporated by reference.

Claims

1. An image processing method performed by a processor, comprising: acquiring an image showing the choroid; performing an enhancement process to enhance the contrast of the acquired image; performing a binarization process on the enhanced image; extracting an area corresponding to choroidal blood vessels in the choroid from the binarized image; An image processing method comprising:

2. The step of extracting a region corresponding to the choroidal blood vessels includes: performing a connection process for connecting the extracted regions; The image processing method according to claim 1 .

3. The step of performing the linking process includes: performing an expansion process for expanding each of the extracted first regions and second regions, and connecting the ends of the first regions and the second regions that intersect with each other as a result of the expansion; The image processing method according to claim 2 .

4. The step of performing the linking process includes: extending each of the extracted first and second regions in a longitudinal direction, and connecting the ends of the first and second regions that intersect with each other through the extensions; The image processing method according to claim 2 .

5. The step of extracting a region corresponding to the choroidal blood vessels includes: removing the discretely extracted regions within the image; The image processing method according to claim 1 , comprising:

6. generating a choroidal vessel image based on the extracted region corresponding to the choroidal vessels; The image processing method according to any one of claims 1 to 5.

7. The step of generating a choroidal vessel image includes: generating a first choroidal vessel image based on the first region extracted from the acquired image by a first step including the steps of performing the enhancement processing, the binarization processing, and extracting a region corresponding to the choroidal vessels; generating a second choroidal vessel image based on a second region corresponding to another choroidal vessel having a diameter different from that of the choroidal vessel extracted from the image by a second step different from the first step; generating the choroidal vessel image by combining the first choroidal vessel image and the second choroidal vessel image; The image processing method of claim 6 , comprising:

8. The step of generating a choroidal vessel image includes: generating the first choroidal vessel image; generating the second choroidal vessel image; generating a third choroidal vessel image based on a third region corresponding to a choroidal vessel different from the choroidal vessel and the other choroidal vessel extracted from the image by a third step different from the first step and the second step; generating the choroidal vessel image by combining the first choroidal vessel image, the second choroidal vessel image, and the third choroidal vessel image; The image processing method of claim 7 , comprising:

9. generating a stereoscopic image of the choroidal vessels based on the plurality of choroidal vessel images; The image processing method according to any one of claims 1 to 8.

10. generating a stereoscopic image of choroidal vessels based on the first choroidal vessel image and the second choroidal vessel image; The image processing method according to claim 8.

11. The image processing method according to claim 1 , wherein the step of acquiring the image includes scanning an area of ​​the fundus that includes at least a vortex vein.

12. an image acquisition unit that acquires an image showing the choroid; an enhancement processing unit that performs enhancement processing to enhance the contrast of the acquired image; a binarization processing unit that performs binarization processing on the enhanced image; a region extraction unit that extracts a region corresponding to choroidal blood vessels in the choroid from the binarized image; An image processing device comprising:

13. A program for performing image processing, The processor acquiring an image showing the choroid; performing an enhancement process to enhance the contrast of the acquired image; performing a binarization process on the enhanced image; extracting an area corresponding to choroidal blood vessels in the choroid from the binarized image; A program that processes the following.

Images