Image processing method, image processing device, and program

Abstract

To analyze a choroid blood vessel in the periphery of an ocular choroidal vein.SOLUTION: An image processing method includes the steps for: specifying a first choroid blood vessel related to a first ocular choroidal vein or a second choroid blood vessel related to a second ocular choroidal vein from choroid blood vessels on a choroid blood vessel image on the basis of positional information on the first ocular choroidal vein and the second ocular choroidal vein; and obtaining an area or a volume of the choroid blood vessel related to the first ocular choroidal vein on the basis of pixels on the choroid blood vessel positioned in a fixed range including a position of the first ocular choroidal vein of the first choroid blood vessel.SELECTED DRAWING: Figure 7

Description

[Technical Field]

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

[0002] Conventionally, a technique for analyzing blood vessels in the choroid has been proposed (US Pat. No. 10,136,812). It is desirable to analyze the choroidal vessels around the vortex veins. Summary of the Invention

[0003] A first aspect of the technology of the present disclosure is image processing performed by a processor, including the steps of acquiring a choroidal vessel image, detecting a vortex vein position from the choroidal vessel image, identifying choroidal vessels associated with the vortex vein position, and determining the size of the choroidal vessels.

[0004] An image processing device according to a second aspect of the disclosed technique includes a memory and a processor connected to the memory, and the processor executes the steps of acquiring a choroidal vessel image, detecting a vortex vein position from the choroidal vessel image, identifying choroidal vessels associated with the vortex vein position, and calculating the size of the choroidal vessels.

[0005] A program according to a third aspect of the disclosed technique causes a computer to execute the steps of acquiring a choroidal vessel image, detecting a vortex vein position from the choroidal vessel image, identifying choroidal vessels associated with the vortex vein position, and calculating the size of the choroidal vessels. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a schematic configuration diagram of an ophthalmologic system according to an embodiment of the present invention. [Figure 2] 1 is a schematic configuration diagram of an ophthalmologic apparatus according to an embodiment of the present invention. [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 5A] FIG. 2 is an explanatory diagram of the equator of the eyeball. [Figure 5B] FIG. 1 is a schematic diagram showing a UWF-SLO image of a wide area of the fundus. [Figure 5C] FIG. 1 is a schematic diagram showing the positional relationship between the choroid and vortex veins in an eyeball. [Figure 6] 10 is a flowchart showing image processing by a server. [Figure 7] 7 is a flowchart showing the blood vessel area calculation process in step 606 of FIG. 6. [Figure 8A] FIG. 10 is a diagram showing a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. [Figure 8B] FIG. 10 is a diagram showing a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. [Figure 9A] FIG. 10 is a diagram showing a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. [Figure 9B] FIG. 10 is a diagram showing a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. [Figure 10] FIG. 10 is a diagram showing a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. [Figure 11] FIG. 10 is a diagram showing a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. [Figure 12A] FIG. 10 is a diagram showing an image of choroidal vessels in the area where VV3 is present. [Figure 12B] FIG. 10 is a diagram showing an image of choroidal vessels in the area where VV3 is present. [Figure 13] FIG. 10 is a diagram showing an image of choroidal vessels in the area where VV4 is present. [Figure 14] FIG. 1 is a schematic diagram showing a first display screen displayed on the viewer display. [Figure 15] FIG. 1 is a schematic diagram showing a second display screen that is displayed on the viewer display. DETAILED DESCRIPTION OF THE INVENTION

[0007] An ophthalmologic system 100 according to an embodiment of the present invention will be described below with reference to the drawings. FIG. 1 shows a schematic configuration of the ophthalmologic system 100. As shown in FIG. 1, the ophthalmologic system 100 includes an ophthalmologic apparatus 110, a server apparatus (hereinafter referred to as a "server") 140, and a display device (hereinafter referred to as a "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 and analysis results acquired by the server 140. The server 140 is an example of the "image processing device" of the technology of the present disclosure.

[0008] The ophthalmic apparatus 110, the server 140, and the viewer 150 are connected to each other via a network 130. The viewer 150 is a client in a client-server system, and multiple viewers 150 are connected via the network. Multiple servers 140 may also be connected via the network to ensure system redundancy. Alternatively, if the ophthalmic apparatus 110 has an image processing function and the viewer 150 has an image viewing function, the ophthalmic apparatus 110 can acquire, process, and view fundus images in a standalone state. Alternatively, if the server 140 has the viewer 150 has an image viewing function, the configuration of the ophthalmic apparatus 110 and the server 140 can acquire, process, and view fundus images.

[0009] 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.

[0010] Next, the configuration of the ophthalmologic apparatus 110 will be described with reference to FIG.

[0011] For ease of explanation, Scanning Laser Ophthalmoscope will be referred to as "SLO" and Optical Coherence Tomography will be referred to as "OCT."

[0012] When the ophthalmic 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.

[0013] 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 fundus 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.

[0014] The control device 16 comprises a computer having a CPU (Central Processing Unit) 16A, a RAM (Random Access Memory) 16B, a ROM (Read-Only Memory) 16C, and an input / output (I / O) port 16D.

[0015] 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.

[0016] The control device 16 also includes an image processing device 17 connected to an I / O port 16D. The image processing device 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 16F.

[0017] 2, the control device 16 of the ophthalmic apparatus 110 includes the input / display device 16E, but the technology of 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 processor unit that operates under the control of the display control unit 204 of the CPU 16A of the control device 16. The image processing processor unit may display an SLO image or the like based on an image signal instructed to be output by the display control unit 204.

[0018] 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.

[0019] 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.

[0020] The wide-angle optical system 30 combines the light from the SLO unit 18 and the light from the OCT unit 20 .

[0021] 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.

[0022] 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.

[0023] 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 F, determined with the eyeball center O 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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. However, the technology of 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.

[0028] 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.

[0029] 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.

[0030] 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, reflected by the beam splitter 62, and received by the IR light detection element 76 in the case of IR light. The image processing device 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.

[0031] The control device 16 also controls the light sources 40, 42, 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 each other, 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 controls the light sources 42, 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 each other, are obtained. An RG color fundus image is obtained from the G-color fundus image and the R-color fundus image.

[0032] 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.

[0033] The equatorial region 178 will be explained using Figure 5A. The eyeball (subject's eye 12) is a spherical structure with a diameter of approximately 24 mm and an eyeball center 170. The line connecting the anterior pole 175 and posterior pole 176 is called the ocular axis 172, and the lines where a plane perpendicular to the ocular axis 172 intersects with the surface of the eyeball are called latitude lines, the longest of which is the equator 174. The part of the retina and choroid corresponding to the position of the equator 174 is called the equatorial region 178.

[0034] The ophthalmologic apparatus 110 can capture an image of an area with an internal illumination angle of 200°, with the eyeball center 170 of the subject's eye 12 as the reference position. Note that 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° at an internal illumination angle.

[0035] 5B shows a UWF-SLO image 179 obtained by imaging using an ophthalmic apparatus 110 capable of scanning at an internal illumination angle of 200°. As shown in FIG. 5B, an equator 178 corresponds to an internal illumination angle of 180°, and the area indicated by a dotted line 178a in the UWF-SLO image 179 corresponds to the equator 178. In this way, the ophthalmic apparatus 110 can image the fundus region extending from the posterior pole to beyond the equator 178.

[0036] FIG. 5C is a diagram showing the positional relationship between the choroid 12M and vortex veins 12V1 and V2 in the eyeball. In FIG. 5C, 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. 5C shows the superior vortex vein V1 and the inferior vortex vein V2 present on one side of the eyeball. Vortex veins are often present near the equator 178. Therefore, to photograph the vortex veins present in the subject's eye 12 and the choroidal blood vessels around the vortex veins, the ophthalmic apparatus 110 capable of scanning at the above-mentioned internal illumination angle of 200° is used.

[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 a first optical coupler 20C. One of the branched beams is collimated by a collimating lens 20E as measurement light and then enters the imaging optical system 19. The measurement light passes through a 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 processing device 17, which operates under the control of the image processing unit 206, generates OCT images such as tomographic images and en-face images based on the OCT data detected by the sensor 20B.

[0042] Here, OCT images obtained by capturing an internal illumination angle at a field of view of 160 degrees or more, or by scanning the peripheral area of the fundus, are referred to as UWF-OCT images.OCT images include tomographic images of the fundus captured by B-scan, stereoscopic images (3D images) based on OCT volume data, and enface images (2D images) that are cross sections of the OCT volume data.

[0043] Image data of the UWF-OCT image is sent from the ophthalmologic apparatus 110 to the server 140 via the communication interface 16F and stored in the storage device 254.

[0044] 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).

[0045] 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 unit 252. The computer main unit 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. The ROM 264 or the storage device 254 stores an image processing program shown in FIG. The ROM 264 or the storage device 254 is an example of a "memory" in the technology of the present disclosure. The CPU 262 is an example of a "processor" in the technology of the present disclosure. The image processing program is an example of a "program" in the technology of the present disclosure.

[0046] The server 140 stores each piece of data received from the ophthalmic apparatus 110 in the storage device 254 .

[0047] 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 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.

[0048] Next, the image processing by the server 140 will be described in detail with reference to Fig. 6. The image processing (image processing method) shown in the flowchart of Fig. 6 is realized by the CPU 262 of the server 140 executing an image processing program.

[0049] In step 600, the image processing unit 206 acquires a UWF-SLO image 179 such as that shown in Fig. 5B as a UWF fundus image from the storage device 254. In step 602, the image processing unit 206 creates (acquires) a choroidal vessel image, which is a binarized image, from the acquired UWF-SLO image as follows, and extracts choroidal vessels from the created choroidal vessel image.

[0050] First, a method for creating (acquiring) a choroidal vessel image will be described. The choroidal vessel image is a binarized image in which pixels corresponding to choroidal vessels and vortex veins are colored white and pixels in other regions are colored black.

[0051] A case will be described in which the choroidal vessel image is generated from a red fundus image and a green fundus image. First, information contained in the red fundus image and the green fundus image will be described.

[0052] The eye is structured such that the vitreous body is covered by multiple layers with different structures. These multiple layers, from the innermost on the vitreous body side to the outermost, include the retina, choroid, and sclera. R light passes through the retina and reaches the choroid. Therefore, the first fundus image (R-color fundus image) contains information about the blood vessels present in the retina (retinal blood vessels) and the blood vessels present in the choroid (choroidal blood vessels). In contrast, G light only reaches the retina. Therefore, the second fundus image (G-color fundus image) contains only information about the blood vessels present in the retina (retinal blood vessels).

[0053] The image processing unit 206 of the CPU 262 extracts retinal blood vessels from the second fundus image (green fundus image) by applying black hat filtering to the second fundus image. Next, the image processing unit 206 removes retinal blood vessels from the first fundus image (red fundus image) by inpainting using the retinal blood vessels extracted from the second fundus image (green fundus image). That is, the image processing unit 206 paints the retinal vascular structure of the first fundus image (red fundus image) with the same value as the surrounding pixels using the position information of the retinal blood vessels extracted from the second fundus image (green fundus image). Then, the image processing unit 206 enhances choroidal blood vessels in the first fundus image (red fundus image) by applying contrast limited adaptive histogram equalization (CLAHE) to the image data of the first fundus image (red fundus image) from which the retinal blood vessels have been removed. This results in a choroidal vessel image in which the background is represented by black pixels and the choroidal vessels are represented by white pixels. The generated choroidal vessel image is stored in the storage device 254.

[0054] Furthermore, although the choroidal blood vessel image is generated from the first fundus image (red fundus image) and the second fundus image (green fundus image), the image processing unit 206 may generate the choroidal blood vessel image using the first fundus image (red fundus image) or an IR fundus image captured with IR light.

[0055] The disclosure of International Publication WO2019 / 181981 regarding methods for generating choroidal vascular images is incorporated herein by reference in its entirety.

[0056] Next, a method for extracting choroidal blood vessels from a choroidal blood vessel image will be described. As described above, the choroidal vessel image is a binarized image in which pixels corresponding to choroidal vessels and vortex veins are white and pixels in other regions are black. Therefore, the image processing unit 206 extracts the choroidal vessels, including the vortex veins, by extracting white pixel areas from the choroidal vessel image. Information on the choroidal vessels is stored in the storage device 254. Note that a vortex vein (VV) is an outflow route for blood flowing into the choroid.

[0057] In step 604, the position (X, Y) of the vortex vein (VV) is detected as follows: The image processing unit 206 sets the movement direction (blood vessel running direction) of each choroidal blood vessel in the choroidal blood vessel image. Specifically, first, the image processing unit 206 performs the following process for each pixel in the choroidal blood vessel image. That is, for each pixel, the image processing unit 206 sets a region (cell) centered on the pixel and creates a histogram of the brightness gradient direction of each pixel in the cell. Next, the image processing unit 206 determines the gradient direction with the smallest count in the histogram for each cell as the movement direction of the pixel in that cell. This gradient direction corresponds to the blood vessel running direction. The reason why the gradient direction with the smallest count is determined to be the blood vessel running direction is as follows: The brightness gradient is small in the blood vessel running direction, while the brightness gradient is large in other directions (for example, there is a large difference in brightness between blood vessels and non-blood vessels). Therefore, when a histogram of the brightness gradient for each pixel is created, the count for the blood vessel running direction is small. Through the above processing, the direction of blood vessels at each pixel of the choroidal vessel image is determined.

[0058] The image processing unit 206 sets initial positions of M (natural number) × N (natural number) (= L) particles. Specifically, the image processing unit 206 sets a total of L initial positions, M in the vertical direction and N in the horizontal direction, at equal intervals on the choroidal blood vessel image.

[0059] The image processing unit 206 estimates (detects) the position of a vortex vein. Specifically, the image processing unit 206 performs the following process for each of the L positions. That is, the image processing unit 206 acquires the blood vessel running direction at the first position (any of the L positions), moves the particles a predetermined distance along the acquired blood vessel running direction, acquires the blood vessel running direction again at the moved position, and moves the particles a predetermined distance along the acquired blood vessel running direction. This process of moving the particles a predetermined distance along the blood vessel running direction is repeated a predetermined number of times. The above process is performed for all L positions. The point where a certain number of particles are gathered at that time is determined to be the position of a vortex vein. Another method for detecting vortex veins may be image processing that recognizes a position on a choroidal vessel image where a feature value of a radial pattern is equal to or greater than a predetermined value as a vortex vein, or detecting the vortex vein dilation from a choroidal vessel image to detect the position of a vortex vein. Regarding the method for detecting vortex veins, the disclosure of International Publication WO 2019 / 203309 is incorporated herein by reference in its entirety.

[0060] The storage device 254 stores vortex vein position information (such as the number of vortex veins and their coordinates on the choroidal vessel image).

[0061] In step 606, the image processing unit 206 executes a blood vessel area calculation process. Fig. 7 shows a flowchart illustrating details of the blood vessel area calculation process in step 606. In step 702 in Fig. 7, the image processing unit 206 reads each data item, namely, a choroidal blood vessel image (binary image) and vortex vein position information, from the storage device 254.

[0062] In step 704, the image processing unit 206 classifies each pixel on the choroidal vessels by determining which of the detected vortex veins (hereinafter referred to as "VVs") the pixel is associated with. The method for classifying each pixel on the choroidal vessels will be described below.

[0063] The classification method includes, first, a method of determining a boundary line for defining a region related to the VV in a choroidal vessel image and then classifying the image. Second, a method of determining boundary points on the choroidal vessels and then classifying the image. Third, a method of classification without determining boundary lines or boundary points. Alternatively, the choroidal vessel image may be displayed on the display 256 of the server 140, and an operator may use a mouse 255M or the like to set boundary lines or boundary points or associate pixels on the choroidal vessels with the VV. However, in this embodiment, the image processing unit 206 automatically classifies the pixels by performing image processing.

[0064] First, the first classification method, which determines the boundary line as described above and then performs classification, will be described. Specifically, the first classification method includes a method in which the boundary of the region related to each VV in the choroidal vessel image is determined uniquely (without overlap), and a method in which an overlap region is set in the region related to each VV.

[0065] A method for uniquely determining boundaries in the first classification method will be described. The image processing unit 206 determines regions corresponding to each of multiple VVs in a choroidal vessel image so that each region is adjacent to its neighboring region, i.e., so that no overlapping regions occur. FIGS. 8A and 8B show choroidal vessel images of a portion where adjacent VV1 and VV2 exist. As shown in FIG. 8A, the image processing unit 206 determines a single boundary line B11 for defining the region corresponding to VV1 and the region corresponding to VV2.

[0066] A method for determining one boundary line B12 is, for example, a graph cut processing method. Another method is the following: As shown in FIG. 8B, the image processing unit 206 calculates the linear distance from each VV for each pixel in the choroidal vessel image, determines the VV corresponding to the shortest linear distance from the calculated linear distances, and associates the determined VV with the pixel. The image processing unit 206 sets each pixel associated with the same VV to the same group. The image processing unit 206 determines one boundary line B12 that separates each group from the positions between pixels where adjacent pixels belong to different groups.

[0067] In step 704, the image processing unit 206 determines which VV (only one) among the multiple VVs each pixel on the choroidal vessels is associated with based on the boundary line B11 or B12.

[0068] A method for setting overlapping regions in regions related to each VV in the first classification method will be described. FIGS. 9A and 9B show choroidal vessel images of a portion where adjacent VV1 and VV2 exist. As shown in FIG. 9A, the image processing unit 206 determines two boundary lines B21 and B22 by combining active contour processing (Snakes algorithm or level set processing). In the example shown in FIG. 9A, the image processing unit 206 classifies, among the boundary lines B21 and B22, pixels on the choroidal vessels located on the VV2 side from the boundary line B21 close to VV1 as pixels related to VV2. The image processing unit 206 classifies, among the boundary lines B21 and B22, pixels on the choroidal vessels located on the VV1 side from the boundary line B22 close to VV2 as pixels related to VV1. The image processing unit 206 classifies, among the boundary lines B21 and B22, pixels on the choroidal vessels located on the VV1 side from the boundary line B22 close to VV2 as pixels related to VV1. Furthermore, two boundary lines B21 and B22 can also be determined by a method that combines graph cut processing and active contour processing.

[0069] In addition to the above-described method, the following method can also be used to set an overlap region in a region related to each VV. As shown in FIG. 9B , for each VV, the image processing unit 206 sets circles C1 and C2 with a predetermined radius centered on the VV, and sets the circumferences of the circles C1 and C2 as boundary lines. The image processing unit 206 classifies pixels on the choroidal vessels that do not belong to either circle C1 or circle C2, and, if circles C1 and C2 overlap, pixels on the choroidal vessels within the overlapping region as pixels located in an overlap region related to both VV1 and VV2. The image processing unit 206 classifies pixels on the choroidal vessels within circle C1, excluding the overlap region, as pixels related to VV1. The image processing unit 206 classifies pixels on the choroidal vessels within circle C2, excluding the overlap region, as pixels related to VV2.

[0070] Next, a method for determining boundary points on choroidal vessels and then classifying them in the second classification method will be described. FIG. 10 shows a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. The image processing unit 206 thins the choroidal vessels. For each pixel on the thinned choroidal vessels, the image processing unit 206 counts the number of pixels along the thinned choroidal vessels to each VV. The image processing unit 206 determines the VV corresponding to the smallest number of pixels and associates the determined VV with the pixel. The image processing unit 206 assigns pixels associated with the same VV to the same group. The image processing unit 206 determines positions between adjacent pixels on the thinned choroidal vessels that belong to different groups as boundary points P1 and P2. The image processing unit 206 classifies each pixel on the choroidal vessels by determining which VV (only one) of multiple VVs each pixel is associated with based on the boundary points P1 and P2.

[0071] Next, a third classification method without determining boundary lines and boundary points will be described. FIG. 11 shows a choroidal vessel image of a portion where adjacent VV1 and VV2 exist. The image processing unit 206 thins the choroidal vessels. For each pixel on the thinned choroidal vessels, the image processing unit 206 counts the number of pixels along the thinned choroidal vessels to each VV1 or VV2. The image processing unit 206 classifies pixels counted along the thinned choroidal vessels from any VV1 or VV2 as pixels overlapping with each VV1 or VV2. The image processing unit 206 classifies pixels counted along the thinned choroidal vessels by less than a predetermined number of pixels as pixels corresponding to a VV traced by less than the predetermined number of pixels.

[0072] When any of the above classification processes is completed, the blood vessel area calculation process proceeds to step 706.

[0073] In step 706, the image processing unit 206 initializes a variable n, which identifies each of the detected VVs, to 0, and in step 708, the image processing unit 206 increments the variable n by one.

[0074] In step 710, the image processing unit 206 extracts choroidal blood vessels that are connected to (connected to) VVn identified by the variable n, i.e., that are connected to VVn-communicating vessels. FIGS. 12A and 12B show choroidal blood vessel images of a portion where VVn (e.g., VV3 (n=3)) is present. The image processing unit 206 may extract all pixels of the choroidal blood vessels connected to VVn (=3). However, as shown in FIG. 12A, the image processing unit 206 first extracts only a portion of pixels classified as pixels corresponding to VVn (=3) from among the pixels of the choroidal blood vessels connected to VVn (=3) in the choroidal blood vessel image. As shown in FIG. 12A, the image processing unit 206 extracts choroidal blood vessels (only the classified pixels) connected from the position of VVn (=3) as VV-communicating vessels. Alternatively, the image processing unit 206 may extract the choroidal vessels (only the above classified pixels) that connect within a certain range (circle C3 with a radius of a certain length) from the position of VV3 as VVn communicating vessels, as shown in Figure 12B.

[0075] In step 712, the image processing unit 206 extracts (identifies) only the choroidal blood vessels surrounding the VVn among the VVn communicating vessels as the VVn-surrounding vessels. Fig. 13 shows a choroidal blood vessel image of a portion where a VVn (e.g., VV4 (n=4)) is present. The image processing unit 206 eliminates blood vessel portions beyond a certain range (circle C4 with a certain radius) from the VVn communicating vessels, and extracts the remaining blood vessel portions as the VVn-surrounding vessels. The choroidal vessels around the VVn (the vessels around the VVn) are an example of "choroidal vessels associated with the location of the vortex vein" in the technology of the present disclosure. The choroidal vessels around the VVn (the vessels around the VVn) are connected to the VVn and are an example of "choroidal vessels connected to the vortex vein" in the technology of the present disclosure.

[0076] In step 714, the image processor 206 calculates the area of the blood vessels surrounding the VVn. For example, The image processing unit 206 reads the area of the fundus corresponding to each pixel of the VVn peripheral blood vessels and calculates the area of the VVn peripheral blood vessels by adding the read areas for each pixel of the VVn peripheral blood vessels. The following value is used for the area of the fundus corresponding to a pixel. A patient's eyeball model is created in advance by correcting a standard eyeball model based on the patient's axial length. The storage device 254 stores the area on the patient's eyeball model corresponding to each pixel of the choroidal blood vessel image. In step 714, the image processing unit 206 reads and uses the area corresponding to the pixel stored in the storage device 254.

[0077] In step 716, the image processing unit 206 determines whether the variable n is equal to the total number N of detected VVs. If it is not determined that the variable n is equal to the total number N, there are VVs whose surrounding blood vessel areas have not been calculated, and therefore the blood vessel area calculation process returns to step 708, and the above processes (steps 708 to 716) are repeated.

[0078] If it is determined that the variable n is equal to the total number N, the area of the surrounding blood vessels has been calculated for all VVs, so the blood vessel area calculation process (step 606 in FIG. 6) ends and image processing proceeds to step 608.

[0079] In step 608, the image processing unit 206 executes the analysis process, which will be described below.

[0080] The image processing unit 206 calculates statistical values of the vascular areas calculated for all VVs. The statistical values include, for example, the average value and standard deviation of the vascular areas calculated for all VVs, and the maximum and minimum values of the vascular areas calculated for all VVs.

[0081] The statistical values include the average value, standard deviation, maximum value, and minimum value of the calculated vascular area for each quadrant. The image processing unit 206 detects watersheds in the choroidal vascular network and determines quadrants based on the detected watersheds. The watersheds are areas in the choroidal vascular image where the density of choroidal vessels is lower than in other areas (e.g., curves LX and LY (see also the choroidal vascular image display field 544 in FIG. 14)).

[0082] The statistical values include comparisons of the mean, standard deviation, maximum, and minimum values of the vascular area between quadrants. The comparison values are the differences, standard deviations, maximum, and minimum values of the above values (mean, standard deviation, maximum, and minimum) between each quadrant. The statistical values include the VV central distance and VV central angle below. Specifically, these values are calculated as follows: A graph is created that represents each position on the choroidal vessel image using polar coordinates (distance and angle from the center of the choroidal vessel image), and at least one of the center positions of the VVs (VV1 to VV4) and the weighted center positions is determined as the central position. The distance from the center of the graph to the central position (VV central distance) and the angle of the central position (VV central angle) are calculated.

[0083] The image processing unit 206 calculates the difference between the calculated statistical value and the corresponding statistical value stored in a normal eye database stored in advance in the storage device 254 .

[0084] The image processing unit 206 detects the positions of the optic disc and the macula from the UWF fundus image. The image processing unit 206 calculates the distance between the optic disc and each VV, the distance between the macula and each VV, the angle between the line connecting the optic disc and the macula and the line connecting the macula and each VV, and the angle between the line connecting the optic disc and the macula and the line connecting the optic disc and each VV.

[0085] The image processing unit 206 calculates the center positions of all VVs as the center positions, and the center positions of each VV weighted by the blood vessel area.

[0086] In step 608, the image processing unit 206 creates display screen data for displaying the calculated values. FIG. 14 shows a first display screen 500A. As shown in FIG. 14, the first display screen 500A has an information area 502 and an image display area 504A. 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. The viewer 150 displays the respective information in each display area, from the patient ID display field 512 to the axial length display field 522, based on information received from the server 140.

[0087] The image display area 504A is an area for displaying a fundus image, etc. The image display area 504A has the following display fields: a comment field 530, a UWF fundus image display field 542, a choroidal vessel image display field 544, a first vessel area display field 526, and a second vessel area display field 528.

[0088] The comment field 530 is a remarks field in which the user, an ophthalmologist, can arbitrarily input the results of his / her observation or diagnosis.

[0089] In the UWF fundus image display field 542, a circle (◯) centered on the position of each VV (VV1 to VV4) is displayed on the UWF fundus image, and at least one of the center of gravity position and the weighted center of gravity position is displayed as the center position; in the example shown in Figure 14, a circular area (●) centered on the weighted center of gravity position is displayed.

[0090] In the choroidal vessel image display field 544, curves LX and LY indicating each watershed, VV communicating vessels, and circles C4 (C41 to C44) for setting the vessels surrounding the VVn are displayed on the choroidal vessel image.

[0091] In the first vascular area display field 526, a bar graph showing the vascular area corresponding to each VV, the average value of the vascular area: XX [μm], and the standard deviation: ●● [μm] are displayed. "XX" displays the specific value of the average value of the vascular area. "●●" displays the specific value of the standard deviation.

[0092] In the second blood vessel area display field 528, a graph showing each position of the choroidal blood vessel image in polar coordinates (distance and angle from the center of the choroidal blood vessel image) displays a circle with the position of each VV as its center and an area corresponding to the blood vessel area, and at least one of the center position and the weighted center position as its center position. In the example shown in Figure 14, a circular area (●) is displayed with the weighted center position as its center. The second vascular area display field 528 displays the distance (VV center distance): △△ [μm] of the center position (e.g., weighted center of gravity position) from the center of the graph, and the angle (VV center angle): ▲▲ [deg] of the center position. △△ [μm] displays the specific value of the VV center distance. ▲▲ [deg] displays the specific value of the VV center angle.

[0093] When the creation of the display screen data is completed as described above, the processing of step 608 in FIG. 6 ends, and in step 610, the image processing unit 206 outputs (stores) each value calculated in step 608 and the display screen data to the storage device 254 in correspondence with the patient ID.

[0094] When an ophthalmologist diagnoses a patient, the viewer 150, in accordance with an operation by the ophthalmologist, specifies a patient ID and instructs the server 140 to transmit each piece of data stored in the storage device 254 corresponding to the patient ID. The server 140 transmits each piece of data stored in the storage device 254 corresponding to the patient ID to the viewer 150. The viewer 150 displays a first display screen 500A shown in Fig. 14 on the display based on each piece of received data.

[0095] As described above, in this embodiment, the blood vessel area is calculated. If the choroidal vessels have a disease, the blood vessel area calculated for the VV corresponding to the choroidal vessels will be larger. Therefore, an ophthalmologist or the like can determine whether or not the choroidal vessels of the VV have a disease from the blood vessel area of the VV. For example, in the example shown in FIG. 14, the blood vessel area of VV3 is larger than the blood vessel areas of the other VVs. Therefore, an ophthalmologist or the like can determine whether or not the choroidal vessels of VV3 have a disease.

[0096] In addition, in this embodiment, the unweighted center of gravity position is calculated, and the weighted center of gravity position is also calculated as the center position. For example, if a disease in which blood flow is concentrated in one area occurs, the corresponding VV expands, the blood vessel area increases, and when the VV center point is calculated using the blood vessel area as a weight, the weighted center of gravity position shifts from the unweighted center of gravity position toward the VV where the blood vessel area has increased. Therefore, an ophthalmologist or the like can determine whether a disease in which blood flow is concentrated in one area has occurred from the weighted center of gravity position and the unweighted center of gravity position.

[0097] In the embodiment described above, the position of the vortex vein (VV) is detected as a position (X, Y) on the choroidal blood vessel image. The technology of the present disclosure is not limited to this. For example, an eyeball model in which a standard eyeball model is corrected using a stored axial length corresponding to a patient ID may be obtained, and a choroidal blood vessel image may be mapped onto the obtained eyeball model. The position of the vortex vein (VV) may then be detected as a position (X, Y, Z) on the eyeball model onto which the choroidal blood vessel image is mapped. In step 608 of FIG. 6, each value is calculated using the eyeball model onto which the choroidal blood vessel image is mapped.

[0098] For example, the position vn=(xn, yn, zn) of each VV is calculated.

[0099] The center position vcenter for all VVs is expressed as a vector indicated by vcenter=Rx(xcenter, ycenter, zcenter), where R is the radius of the eyeball model corrected by the axial length of the subject's eye.

[0100] Here, xcenter is calculated by the formula shown in Equation 1.

[0101]

number

[0102] xcenter is a normalized version of xc, and xc is calculated by taking the weighted average of x.

[0103] wn is a weight related to the blood vessel area. The method is not limited to calculating a weighted average value in this way, and the m-th power average value or the like may be used.

[0104] yc and zc are calculated in the same way as xc. ycenter and zcenter are calculated in the same way as xcenter.

[0105] Each vector extending from the center of the eyeball model toward the VV is weighted by the surface area of the blood vessel, and the weighted vectors are combined to calculate a vector extending from the center of the eyeball model toward the center point of the VV.

[0106] Fig. 15 shows a second display screen 500B when each value is calculated using an eyeball model in step 608 of Fig. 6. As shown in Fig. 15, the second display screen 500B is substantially similar to the first display screen 500A, and therefore only differences will be described.

[0107] The second display screen 500B has an eyeball model display field 532 instead of at least one of the first blood vessel area display field 526 and the second blood vessel area display field 528 in the first display screen 500A. Note that in the example shown in Fig. 15, the eyeball model display field 532 is provided instead of the second blood vessel area display field 528. The eyeball model display field 532 displays vectors to each VV (VV1 to VV4) and vectors to at least one of the center of gravity position and the weighted center of gravity position. In the example shown in Fig. 15, a vector to the weighted center of gravity position is displayed.

[0108] In the above-described embodiment, the vascular area is calculated using a choroidal vascular image obtained from a UWF fundus image. However, the technology of the present disclosure is not limited to this. For example, the vascular volume may be calculated using a stereoscopic image (three-dimensional image) based on OCT volume data. In this case, in step 608, the vascular volume is used instead of the vascular area. For example, the weighting uses the vascular volume.

[0109] In the above-described examples, image processing is performed using a software configuration that utilizes a computer. However, the technology of the present disclosure is not limited to this. For example, instead of using a software configuration that utilizes a computer, image processing may be performed using only a hardware configuration such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). Part of the image processing may be performed using a software configuration, and the remaining part may be performed using a hardware configuration.

[0110] As such, the technology of 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 technologies.

[0111] (First Technology) an acquisition unit for acquiring a choroidal blood vessel image; a detection unit that detects a vortex vein position from the choroidal vessel image; an identification unit that identifies choroidal blood vessels associated with the vortex vein location; A calculation unit that calculates the size of the choroidal blood vessels; An image processing device comprising:

[0112] (Second Technology) An acquisition unit acquires a choroidal vessel image; a step of detecting a vortex vein position from the choroidal vessel image by a detection unit; An identification unit identifies choroidal blood vessels associated with the vortex vein location; A calculation unit calculates the size of the choroidal vessels; Image processing methods.

[0113] The image processing unit 206 is an example of an "acquisition unit," a "detection unit," a "identification unit," and a "calculation unit" of the technology of the present disclosure.

[0114] Based on the above disclosure, the following technology is proposed. (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 On the computer, acquiring a choroidal vessel image; detecting a vortex vein position from the choroidal vessel image; identifying choroidal vessels associated with the vortex vein location; determining the size of the choroidal vessels; Execute Computer program products.

[0115] Server 140 is an example of a "computer program product" of the disclosed technology.

[0116] The image processing described above is merely an example, and it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be changed, without departing from the spirit of the invention.

[0117] The disclosure of Japanese Patent Application No. 2020-073123 is incorporated herein by reference in its entirety. All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

Claims

1. Image processing performed by a processor, acquiring a choroidal vessel image; detecting a first vortex vein and a second vortex vein from the choroidal vessel image; Identifying a first choroidal vessel associated with the first vortex vein or a second choroidal vessel associated with the second vortex vein from the choroidal vessels on the choroidal vessel image based on the position information of the first vortex vein and the second vortex vein; An image processing method comprising:

2. the identifying step includes identifying the first choroidal vessel associated with the first vortex vein or the second choroidal vessel associated with the second vortex vein from choroidal vessels connected to both the first vortex vein and the second vortex vein based on position information of the first vortex vein and the second vortex vein. The image processing method according to claim 1 .

3. the identifying step includes identifying the first choroidal vessel associated with the first vortex vein or the second choroidal vessel associated with the second vortex vein by segmenting a choroidal vessel connected to both the first vortex vein and the second vortex vein based on position information for the first vortex vein and the second vortex vein. The image processing method according to claim 2 .

4. the step of identifying identifies the first choroidal vessel or the second choroidal vessel from the choroidal vessels in the choroidal vessel image by identifying pixels on the choroidal vessels as pixels of the first choroidal vessel or the second choroidal vessel; The image processing method according to any one of claims 1 to 3.

5. the step of identifying includes identifying the first choroidal vessel or the second choroidal vessel from the choroidal vessels based on a linear distance between each pixel on the choroidal vessel in the choroidal vessel image and the first vortex vein and a linear distance between each pixel on the choroidal vessel and the first vortex vein and the second vortex vein; The image processing method according to claim 4.

6. The step of identifying includes setting a boundary line based on a linear distance between each pixel on the choroidal vessel and the first vortex vein and between each pixel on the choroidal vessel and the second vortex vein, and identifying the choroidal vessel as the first choroidal vessel or the second choroidal vessel based on the boundary line. The image processing method according to claim 4.

7. the step of identifying includes setting a boundary line between the first vortex vein and the second vortex vein based on the direction in which the choroidal vessels run, and identifying the choroidal vessels in the choroidal vessel image as the first choroidal vessel or the second choroidal vessel based on the boundary line. The image processing method according to claim 4.

8. The identifying step includes counting the number of pixels along the choroidal vessel to the first vortex vein and the second vortex vein for each pixel on the choroidal vessel, and identifying the choroidal vessel as the first choroidal vessel or the second choroidal vessel based on the number of pixels measured. The image processing method according to claim 4.

9. The step of identifying includes identifying, as the first choroidal vessel, a pixel on the choroidal vessel where a measurement result up to the first vortex vein is smaller than a measurement result up to the second vortex vein. The image processing method according to claim 8.

10. The identifying step extracts pixels on the choroidal vessels connected to the first vortex vein from the first choroidal vessel, and extracts pixels on the choroidal vessels connected to the second vortex vein from the second choroidal vessel. The image processing method according to any one of claims 1 to 9.

11. Further comprising determining an area or volume of a choroidal vessel associated with the first vortex vein based on the first choroidal vessel. The image processing method according to any one of claims 1 to 10.

12. The step of calculating the area or volume calculates the area or volume of the choroidal vessels associated with the first vortex vein based on pixels on the choroidal vessels located within a certain range including the position of the first vortex vein. The image processing method according to claim 11.

13. a display step of superimposing the area or volume of the choroidal vessels associated with the first vortex vein calculated in the step of calculating the area or volume on the choroidal vessels associated with the first vortex vein used in calculating the area or volume, 13. The image processing method according to claim 11 or 12.

14. The step of acquiring a choroidal vessel image includes acquiring a three-dimensional image of the choroidal vessels made of OCT volume data, The step of calculating the area or volume calculates the volume of the choroidal blood vessels. The image processing method according to any one of claims 11 to 13.

15. The step of identifying includes counting the number of pixels along the choroidal vessel to the first vortex vein and the second vortex vein for each pixel on the choroidal vessel, and identifying pixels on the choroidal vessel where the number of pixels to the first vortex vein and the second vortex vein is less than a predetermined number as pixels of the first choroidal vessel and the second choroidal vessel. The image processing method according to any one of claims 4 to 14.

16. determining an area or volume of the choroidal vessel associated with the second vortex vein based on the second choroidal vessel; Calculating a center position between the first vortex vein and the second vortex vein weighted based on the area or volume of the choroidal vessels associated with the first vortex vein and the second vortex vein; The image processing method according to any one of claims 11 to 15, further comprising:

17. an image acquisition unit for acquiring a choroidal blood vessel image; a detection unit that detects a first vortex vein and a second vortex vein from the choroidal vessel image; an identification unit that identifies a first choroidal vessel associated with the first vortex vein or a second choroidal vessel associated with the second vortex vein from the choroidal vessels on the choroidal vessel image based on position information of the first vortex vein and the second vortex vein; An image processing device comprising:

18. the identifying unit identifies a first choroidal vessel associated with the first vortex vein or a second choroidal vessel associated with the second vortex vein by dividing a choroidal vessel connected to both the first vortex vein and the second vortex vein based on position information on the first vortex vein and the second vortex vein. The image processing device according to claim 17.

19. On the computer, a procedure for acquiring a choroidal vascular image; detecting a first vortex vein and a second vortex vein from the choroidal vessel image; a step of identifying a first choroidal vessel associated with the first vortex vein or a second choroidal vessel associated with the second vortex vein from the choroidal vessels on the choroidal vessel image based on position information of the first vortex vein and the second vortex vein; A program for executing a process including:

20. the identifying step includes identifying a first choroidal vessel associated with the first vortex vein or a second choroidal vessel associated with the second vortex vein by segmenting a blood vessel connected to both the first vortex vein and the second vortex vein based on position information for the first vortex vein and the second vortex vein.

20. The program of claim 19.

Images