JP2019187551A - Image processing device, image processing method, and program - Google Patents

Abstract

To identify an avascular region faster while improving robustness against noise in a fundus blood vessel image.SOLUTION: An image processing device comprises: pole determination means which determines a pole of an avascular region in a fundus blood vessel image of a subject's eye; coordinate conversion means which, on the basis of the determined pole, performs a coordinate conversion process to convert the fundus blood vessel image into a coordinate image different from an orthogonal coordinate image; and boundary determination means which determines a boundary of the avascular region in the different coordinate image.SELECTED DRAWING: Figure 5

Description

  The disclosed technology relates to an image processing apparatus, an image processing method, and a program.

  If a tomographic imaging apparatus for an eye such as an optical coherence tomography (OCT) is used, the state inside the retinal layer can be observed three-dimensionally. This tomographic imaging apparatus is widely used in ophthalmic practice because it is useful for more accurately diagnosing diseases. As a form of OCT, for example, there is TD-OCT (Time domain OCT) in which a broadband light source and a Michelson interferometer are combined. This is configured to measure the interference light with the backscattered light acquired by the signal arm by moving the position of the reference mirror at a constant speed to obtain the reflected light intensity distribution in the depth direction. However, since such TD-OCT requires mechanical scanning, high-speed image acquisition is difficult. Therefore, SS-OCT (Swept Source) that spectrally disperses temporally by using a broadband light source as a faster image acquisition method and using SD-OCT (Spectral domain OCT) that acquires an interference signal with a spectrometer or a high-speed wavelength swept light source. OCT) has been developed, and a tomographic image having a wider angle of view can be acquired.

  On the other hand, in ophthalmic practice, fundus blood vessel images are used to grasp the pathology of the fundus blood vessels.

  In order to capture the fundus blood vessel image, an invasive fluorescence fundus contrast examination has been performed so far. In recent years, OCT Angiography (hereinafter referred to as OCTA) technology for non-invasively drawing a fundus blood vessel in a three-dimensional manner using OCT has been used to create a fundus blood vessel image. In OCTA, the same position is scanned a plurality of times with measurement light, and the motion contrast obtained by the interaction between the displacement of red blood cells and the measurement light is imaged. FIG. 4 shows an example of OCTA imaging in which the main scanning direction is the horizontal (x-axis) direction and the B-scan is continuously performed r times at each position (yi; 1 ≦ i ≦ n) in the sub-scanning direction (y-axis direction). ing. In OCTA imaging, scanning a plurality of times at the same position is called cluster scanning, and a plurality of tomographic images obtained at the same position is called a cluster. It is known that when the motion contrast data is generated in cluster units and the number of tomographic images per cluster (the number of scans at substantially the same position) is increased, the contrast of the OCTA image is improved.

  Here, in order to diagnose a disease of diabetic retinopathy using an OCTA image, identification of an avascular region such as a foveal avascular region in a two-dimensional OCTA image and measurement of the area of the avascular region are performed. This is disclosed in Patent Document 1. At this time, the avascular region in the two-dimensional OCTA image is specified by performing a histogram analysis of the two-dimensional OCTA image.

Special table 2015-515894 gazette

  Here, when histogram analysis is used as in the prior art, the specification of an avascular region becomes sensitive to image noise. In addition, it is necessary to process the entire fundus blood vessel image two-dimensionally, which tends to be complicated and time consuming.

  An object of the disclosed technique is to specify an avascular region at a higher speed while improving robustness against noise in a fundus blood vessel image.

  The present invention is not limited to the above-described object, and is a function and effect derived from each configuration shown in the embodiment for carrying out the invention, which will be described later. It can be positioned as one.

One of the disclosed image processing apparatuses is
Pole determining means for determining the pole of the avascular region in the fundus blood vessel image of the eye to be examined;
Coordinate conversion means for executing a coordinate conversion process for converting the fundus blood vessel image into a coordinate image different from the orthogonal coordinate image based on the determined pole;
Boundary line determining means for determining a boundary line of the avascular region in the different coordinate image.

  According to one of the disclosed techniques, it is possible to identify an avascular region at a higher speed while improving robustness against noise in a fundus blood vessel image.

It is a block diagram which shows the structure of the image processing apparatus which concerns on 1st embodiment. It is a figure explaining the measurement optical system contained in the image processing system which concerns on 1st embodiment, and the tomographic imaging apparatus which comprises this image processing system. 3 is a flowchart of processing that can be executed by the image processing system according to the first embodiment. It is a figure explaining the scanning method of OCTA imaging in a first embodiment. (A) Example of fundus blood vessel image acquired in step S310, (B) Example of image enhancement result image in step S320, (C) Example of polar coordinates in step S341. It is an example of the result of the process performed by step S343 of 1st embodiment. It is an example of the result of the process performed by step S345 of 1st embodiment. It is a figure explaining the process performed by step S342 of 1st embodiment. It is a figure explaining the process performed by step S343 of 1st embodiment. It is a figure explaining the process performed by step S341 of 2nd embodiment. It is a flowchart of the process which the image processing system which concerns on 2nd embodiment can perform. It is a figure explaining the process performed by step S850 of 2nd embodiment. It is a figure explaining the process performed by step S900 of 2nd embodiment. It is a figure explaining the process performed by step S850 of 2nd embodiment. It is a figure explaining the process performed by step S850 of 2nd embodiment.

[First embodiment]
The first embodiment will be described below with reference to the drawings. The image processing apparatus according to the present embodiment is in a region of interest such as a foveal avascular zone (FAZ) or a non-perfusion area (NPA) with respect to an input fundus blood vessel image. The avascular region is specified. For example, the image processing apparatus according to the present embodiment performs coordinate conversion processing for setting (determining) a pole in an NPA, which is an example of an avascular region, and converting a fundus blood vessel image having orthogonal coordinates into a fundus blood vessel image having polar coordinates. Coordinate conversion means is provided. That is, the coordinate conversion means according to the present embodiment executes coordinate conversion processing for converting the fundus blood vessel image into a coordinate image different from the orthogonal coordinate image based on the determined pole. Here, the coordinate image different from the orthogonal coordinate image is, for example, a polar coordinate image, a spherical coordinate image (three-dimensional polar coordinate image), or a cylindrical coordinate image. The determined pole is the origin of polar coordinates. The image processing apparatus according to the present embodiment includes a boundary line determination unit that determines a boundary line of an avascular region in different coordinate images. This embodiment shows an example of a method for identifying the boundary line or region of an NPA by finding the first edge (end) starting from the pole in the NPA in the fundus blood vessel image expressed in polar coordinates. Hereinafter, an image processing system including the image processing apparatus according to the first embodiment will be described with reference to the drawings.

  FIG. 2 is a diagram illustrating a configuration of the image processing system 10 including the image processing apparatus 101 according to the present embodiment. As shown in FIG. 2, in the image processing system 10, an image processing apparatus 101 is connected to a tomographic imaging apparatus 100 (also referred to as OCT), an external storage unit 102, an input unit 103, and a display unit 104 via an interface. It is constituted by. The tomographic image capturing apparatus 100 is an apparatus that captures a tomographic image of the eye. In the present embodiment, SD-OCT is used as the tomographic imaging apparatus 100. For example, SS-OCT may be used. In FIG. 2A, a measurement optical system 100-1 is an optical system for acquiring an anterior ocular segment image, an SLO fundus image of a subject eye, and a tomographic image. The stage unit 100-2 enables the measurement optical system 100-1 to move back and forth and right and left. The base unit 100-3 incorporates a spectroscope described later. The image processing apparatus 101 is a computer that executes control of the stage unit 100-2, alignment operation control, tomographic image reconstruction, and the like. The external storage unit 102 stores a tomographic program, patient information, imaging data, fundus blood vessel images, past examination image data, measurement data such as avascular region information, and the like. The input unit 103 instructs the computer, and specifically includes a keyboard and a mouse. The display unit 104 includes a monitor, for example.

(Configuration of tomographic imaging system)
The configuration of the measurement optical system and the spectroscope in the tomographic imaging apparatus 100 of the present embodiment will be described with reference to FIG. First, the inside of the measurement optical system 100-1 will be described. An objective lens 201 is installed facing the eye 200, and a first dichroic mirror 202 and a second dichroic mirror 203 are arranged on the optical axis. By these dichroic mirrors, the optical path 250 of the OCT optical system, the SLO optical system and the optical path 251 for the fixation lamp, and the optical path 252 for anterior eye observation are branched for each wavelength band.

  The optical path 251 for the SLO optical system and the fixation lamp has SLO scanning means 204, lenses 205 and 206, a mirror 207, a third dichroic mirror 208, an APD (Avalanche Photodiode) 209, an SLO light source 210, and a fixation lamp 211. ing. The mirror 207 is a prism on which a perforated mirror or a hollow mirror is deposited, and separates illumination light from the SLO light source 210 and return light from the eye to be examined. The third dichroic mirror 208 separates the optical path of the SLO light source 210 and the optical path of the fixation lamp 211 for each wavelength band. The SLO scanning means 204 scans the light emitted from the SLO light source 210 on the eye 200 to be examined, and includes an X scanner that scans in the X direction and a Y scanner that scans in the Y direction. In this embodiment, since the X scanner needs to perform high-speed scanning, it is a polygon mirror, and the Y scanner is a galvanometer mirror. The lens 205 is driven by a motor (not shown) for focusing the SLO optical system and the fixation lamp 211. The SLO light source 210 generates light having a wavelength near 780 nm. The APD 209 detects return light from the eye to be examined. The fixation lamp 211 generates visible light to promote fixation of the subject. The light emitted from the SLO light source 210 is reflected by the third dichroic mirror 208, passes through the mirror 207, passes through the lenses 206 and 205, and is scanned on the eye 200 by the SLO scanning unit 204. The return light from the eye 200 to be examined returns to the same path as the illumination light, and is then reflected by the mirror 207 and guided to the APD 209 to obtain an SLO fundus image. The light emitted from the fixation lamp 211 passes through the third dichroic mirror 208 and the mirror 207, passes through the lenses 206 and 205, and forms a predetermined shape on the eye 200 by the SLO scanning unit 204, Encourage the patient to fixate.

  In the optical path 252 for observing the anterior eye, lenses 212 and 213, a split prism 214, and a CCD 215 for observing the anterior eye part that detects infrared light are arranged. The CCD 215 has sensitivity at a wavelength of irradiation light for anterior ocular segment observation (not shown), specifically, around 970 nm. The split prism 214 is disposed at a position conjugate with the pupil of the eye 200 to be examined, and the distance in the Z-axis direction (optical axis direction) of the measurement optical system 100-1 with respect to the eye 200 is used as a split image of the anterior eye part. It can be detected.

  The optical path 250 of the OCT optical system constitutes the OCT optical system as described above, and is for taking a tomographic image of the eye 200 to be examined. More specifically, an interference signal for forming a tomographic image is obtained. The XY scanner 216 is for scanning light on the eye 200 and is shown as a single mirror in FIG. 2B, but is actually a galvanometer mirror that performs scanning in the XY biaxial directions. Among the lenses 217 and 218, the lens 217 is driven by a motor (not shown) in order to focus the light from the OCT light source 220 emitted from the fiber 224 connected to the optical coupler 219 on the eye 200 to be examined. By this focusing, the return light from the eye 200 is simultaneously incident on the tip of the fiber 224 in the form of a spot. Next, the configuration of the optical path from the OCT light source 220, the reference optical system, and the spectrometer will be described. 220 is an OCT light source, 221 is a reference mirror, 222 is a dispersion compensating glass, 223 is a lens, 219 is an optical coupler, 224 to 227 are connected to the optical coupler and integrated into a single mode, and 230 is a spectroscope. . These configurations constitute a Michelson interferometer. The light emitted from the OCT light source 220 is split into the measurement light on the optical fiber 224 side and the reference light on the optical fiber 226 side through the optical coupler 219 through the optical fiber 225. The measurement light is irradiated to the eye 200 to be observed through the above-mentioned OCT optical system optical path, and reaches the optical coupler 219 through the same optical path due to reflection and scattering by the eye 200 to be observed. On the other hand, the reference light reaches the reference mirror 221 and is reflected through the optical fiber 226, the lens 223, and the dispersion compensation glass 222 inserted to match the wavelength dispersion of the measurement light and the reference light. Then, it returns on the same optical path and reaches the optical coupler 219. The measurement light and the reference light are combined by the optical coupler 219 and become interference light. Here, interference occurs when the optical path length of the measurement light and the optical path length of the reference light are substantially the same. The reference mirror 221 is held so as to be adjustable in the optical axis direction by a motor and a drive mechanism (not shown), and the optical path length of the reference light can be adjusted to the optical path length of the measurement light. The interference light is guided to the spectroscope 230 via the optical fiber 227. The polarization adjusting units 228 and 229 are provided in the optical fibers 224 and 226, respectively, and perform polarization adjustment. These polarization adjusting units have several portions where the optical fiber is looped. By rotating the loop-shaped portion around the longitudinal direction of the fiber, the fiber can be twisted, and the polarization states of the measurement light and the reference light can be adjusted and matched. The spectroscope 230 includes lenses 232 and 234, a diffraction grating 233, and a line sensor 231. The interference light emitted from the optical fiber 227 becomes parallel light through the lens 234, and then is split by the diffraction grating 233 and imaged by the lens 232 on the line sensor 231.

  Next, the periphery of the OCT light source 220 will be described. The OCT light source 220 is an SLD (Super Luminescent Diode) which is a typical low coherent light source. The center wavelength is 855 nm and the wavelength bandwidth is about 100 nm. Here, the bandwidth is an important parameter because it affects the resolution of the obtained tomographic image in the optical axis direction. As the type of light source, SLD is selected here, but it is sufficient that low-coherent light can be emitted, and ASE (Amplified Spontaneous Emission) or the like can be used. Near-infrared light is suitable for the center wavelength in view of measuring the eye. Moreover, since the center wavelength affects the lateral resolution of the obtained tomographic image, it is desirable that the center wavelength be as short as possible. For both reasons, the center wavelength was 855 nm. In this embodiment, a Michelson interferometer is used as an interferometer, but a Mach-Zehnder interferometer may be used. It is desirable to use a Mach-Zehnder interferometer when the light amount difference is large and a Michelson interferometer when the light amount difference is relatively small according to the light amount difference between the measurement light and the reference light.

(Configuration of image processing apparatus)
The configuration of the image processing apparatus 101 of this embodiment will be described with reference to FIG. The image processing apparatus 101 is a personal computer (PC) connected to the tomographic imaging apparatus 100, and includes an image acquisition unit 101-01, a storage unit 101-02, an imaging control unit 101-03, an image processing unit 101-04, A display control unit 101-05 is provided. In the image processing apparatus 101, the arithmetic processing unit CPU executes software modules that implement the image acquisition unit 101-01, the imaging control unit 101-03, the image processing unit 101-04, and the display control unit 101-05. Realize the function. The present invention is not limited to this. For example, the image processing unit 101-04 may be realized by dedicated hardware such as an ASIC, or the display control unit 101-05 may be a dedicated processor such as GPU different from the CPU. It may be realized by using. Further, the connection between the tomographic imaging apparatus 100 and the image processing apparatus 101 may be configured via a network.

  The image acquisition unit 101-01 acquires signal data of an SLO fundus image or tomographic image captured by the tomographic image capturing apparatus 100. The image acquisition unit 101-01 includes a tomographic image generation unit 101-11 and a motion contrast data generation unit 101-12. The tomographic image generation unit 101-11 acquires signal data (interference signal) of a tomographic image captured by the tomographic imaging apparatus 100, generates a tomographic image by signal processing, and stores the generated tomographic image in the storage unit 101-02. To store. The imaging control unit 101-03 performs imaging control for the tomographic imaging apparatus 100. The imaging control includes instructing the tomographic imaging apparatus 100 regarding the setting of imaging parameters and instructing the start or end of imaging.

  The image processing unit 101-04 includes an alignment unit 101-41, a synthesis unit 101-42, a correction unit 101-43, an image feature acquisition unit 101-44, a projection unit 101-45, and an analysis unit 101-46. The image acquisition unit 101-01 and the synthesis unit 101-42 described above are an example of an acquisition unit according to the present invention. The synthesizing unit 101-42 synthesizes the plurality of motion contrast data generated by the motion contrast data generating unit 101-12 based on the alignment parameter obtained by the alignment unit 101-41, and generates a synthesized motion contrast image. . The correction unit 101-43 performs a process of suppressing projection artifacts generated in the motion contrast image two-dimensionally or three-dimensionally (projection artifacts will be described in S314). The image feature acquisition unit 101-44 acquires the layer boundary of the retina and choroid, the position of the fovea and the center of the optic disc from the tomographic image. The projection unit 101-45 projects a motion contrast image in a depth range based on the position of the layer boundary acquired by the image feature acquisition unit 101-44, and generates a motion contrast front image. That is, the projection unit 101-45 generates a motion contrast front image using at least a part of data in the depth range in the three-dimensional motion contrast image. The analysis unit 101-46 includes an enhancement unit 101-461, an extraction unit 101-462, a measurement unit 101-463, and a coordinate conversion unit 101-464, and performs an avascular region specification and measurement process from the motion contrast front image. . The emphasizing unit 101-461 performs blood vessel emphasis processing. In addition, the extraction unit 101-462 performs boundary line extraction of an NPA that is an example of an avascular region based on the blood vessel emphasized image. Furthermore, the measurement units 101 to 463 as an example of a generation unit generate an extracted NPA mask image. In addition, the measurement unit 101-463 calculates measurement values such as the area and volume of the NPA. The coordinate conversion unit 101-464 performs coordinate conversion of the motion contrast front image. An example of coordinate conversion here is conversion from orthogonal coordinates to polar coordinates, or from polar coordinates to orthogonal coordinates.

  The external storage unit 102 includes information on the eye to be examined (patient name, age, sex, etc.), captured images (tomographic images and SLO images / OCTA images), composite images, imaging parameters, avascular region information, measurement values, The parameters set by the operator are stored in association with each other. The input unit 103 is, for example, a mouse, a keyboard, a touch operation screen, and the like, and the operator gives an instruction to the image processing apparatus 101 and the tomographic imaging apparatus 100 via the input unit 103. Note that the configuration of the image processing apparatus 101 according to the present invention does not necessarily include all the configurations described above. For example, the alignment unit 101-41, the synthesis unit 101-42, the correction unit 101-43, and the like are omitted. Also good.

  Next, a processing procedure of the image processing apparatus 101 according to the present embodiment will be described with reference to FIG. FIG. 3 is a flowchart showing a flow of operation processing of the entire system in the present embodiment. In the present invention, the image enhancement processing in step S320 is not an essential process and may be omitted.

<Step 310>
In step S310, the image processing unit 101-04 acquires a motion contrast image that is a fundus blood vessel image. The image processing unit 101-04 may acquire a fundus blood vessel image already stored in the external storage unit 102. In the present embodiment, the image processing unit 101-04 controls the measurement optical system 100-1 to acquire the acquired tomographic image. An example of generating and acquiring a motion contrast image is shown. Details of these processes will be described later. Or in this embodiment, it is not limited to this acquisition method, Other methods may be sufficient if a fundus blood vessel image is acquired. Alternatively, a motion contrast image as shown in FIG. 5A will be described as an example of the fundus blood vessel image in the present embodiment. However, the present invention is not limited to this, and other fundus blood vessel images such as a fluorescein angiographic image, indocyanine green ( inocyanine green (ICG) may be a fundus blood vessel image such as an angiographic image, an OCT angiographic image, or the like.

<Step 320>
In step S320, the enhancement unit 101-461 performs image enhancement of the fundus blood vessel image while minimizing the influence of noise. A specific description of this process is as follows.
1) Apply a Gaussian Low Pass Filter (LPF) of a narrow window size to the fundus blood vessel image acquired in step S310 to generate a Light-Filtered OCTA image (LF-OCTA image). Here, the narrow window size is 3 pixels.
2) Apply a wide window size Gaussian Low Pass Filter (LPF) to the fundus blood vessel image acquired in step S310 to generate a Strong-Filtered OCTA image (SF-OCTA image). Here, the width window size is 85 pixels.
3) LF-OCTA is divided by SF-OCTA to generate a Relative Variation OCTA (RV-OCTA) image.
4) Binarization of RV-OCTA is performed using a specific threshold RL (ratio limit). In other words, the enhancement unit 101-461 executes enhancement processing for enhancing the blood vessel structure in the fundus blood vessel image by performing binarization processing on the fundus blood vessel image. Here, the threshold value RL is 1. If each RV-OCTA pixel value is less than or equal to RL, the pixel is set to zero. Otherwise, the pixel is set to 1.
5) Apply a morphological filter to the binarized image. Here, an open-close process of kernel size = 3 is performed. This process reduces noise on the image and improves the continuity of the vascular structure. FIG. 5B shows an example of the processing result of this step.

<Step 330>
In step S330, the image processing unit 101-04, which is an example of a pole determining unit, determines the pole of the avascular region in the fundus blood vessel image. Here, as a method for determining the pole, a method by a user operation (not shown) acquired by the input unit 103 is used. Specifically, when the image processing apparatus 101 displays an OCTA image on the display unit 104 and the user selects one point of the NPA in the OCTA image using a mouse, a keyboard, a touch screen, or other input device, the operation is performed by the input unit. 103 obtains and sends the selected information to the image processing apparatus 101. However, the present embodiment is not limited to this method, and a method of automatically determining the pole based on the fundus blood vessel image or the analysis result of the fundus image may be used. For example,
-Perform structural analysis of OCTA images and detect fovea based on the analysis results.
-A strong low pass filter is applied to the OCTA image, and a region of minimum intensity is detected based on the analysis result.
-Detection is performed using analysis results of other modality fundus images (eg, SLO and fundus camera images).
For example, one point in the detected area may be determined as a pole.

<Step 340>
In step S340, the image processing unit 101-04, which is an example of the area specifying unit, specifies the NPA area including the pole using the pole determined in step S330. At this time, the determined pole is the origin of polar coordinates. The NPA region is specified by determining the boundary line of the NPA region based on the determined pole. In step S330, the image processing unit 101-04, which is an example of a position determining unit, determines the position of the avascular region in the fundus blood vessel image in accordance with an instruction from the examiner with respect to the fundus blood vessel image of the eye to be examined. Also good. At this time, in step S340, the image processing unit 101-04, which is an example of a boundary line determination unit, may determine the boundary line of the avascular region based on the determined position. At this time, the determined position corresponds to a position for determining the boundary line. Details of these processes will be described later.

<Step 350>
In step S350, the image processing unit 101-04 sends the NPA information specified in step S340 to the storage unit 101-02, and the storage unit 101-02 stores the NPA information. Further, in step S350, the storage unit 101-02 sends the stored NPA information and fundus blood vessel image to the display unit 104, and displays the NPA information superimposed on the fundus blood vessel image. That is, the display control unit 101-05 causes the display unit 104 to display information indicating the specified NPA region in a state of being superimposed on the fundus blood vessel image. In addition, the display control unit 101-05 causes the display unit 104 to display information indicating the boundary line of the determined NPA region while being superimposed on the fundus blood vessel image. The information is preferably, for example, a line indicating the outer frame of the region or a color indicating the inside of the region, but may be any display as long as the region can be identified. At this time, the display control unit 101-05 may display the measurement value related to the NPA region on the display unit 104 in a state of being superimposed on the fundus blood vessel image. Further, it is preferable that the position of the information indicating the boundary line of the determined NPA area can be changed in accordance with an instruction from the examiner. As a result, convenience for the examiner is improved, and diagnostic efficiency is also improved.

  The above steps are performed, and the processing procedure of the image processing apparatus 101 in this embodiment is completed.

  Next, a specific processing procedure for obtaining a motion contrast image that is a fundus blood vessel image in step S310 of the present embodiment will be described with reference to FIG. In the present invention, the composite motion contrast image generation process in step S314 is not an essential process and may be omitted.

<Step 311>
In step S <b> 311, the image control unit 101-03 sets an OCTA image capturing condition instructed to the tomographic image capturing apparatus 100 by operating the input unit 103. Specifically, 1) Selection or registration of an inspection set 2) Selection or addition of a scan mode in the selected inspection set 3) A procedure for setting imaging parameters corresponding to the scan mode. In this embodiment, the following settings are made. In step S302, OCTA imaging (under the same imaging conditions) is repeatedly executed a predetermined number of times with appropriate breaks.

1) Register the Molecular Diase inspection set 2) Select the OCTA scan mode 3) Set the following imaging parameters 3-1) Scan pattern: Small Square
3-2) Scanning area size: 3 × 3 mm
3-3) Main scanning direction: horizontal direction 3-4) Scanning interval: 0.01 mm
3-5) Fixation lamp position: fovea 3-7) Number of B scans per cluster: 4
3-6) Coherence gate position: Vitreous side 3-7) Default display report type: Single inspection report Note that the imaging procedure set for each inspection purpose (including the scan mode) and acquired in each scan mode It refers to the default display method for OCT images and OCTA images. As a result, an examination set including an OCTA scan mode that is set for a macular disease eye is registered with the name “Macro Disease”. The registered examination set is stored in the external storage unit 102.

<Step 312>
In step S312, when the input unit 103 receives an instruction to start imaging from the operator, the input unit 103 starts repeated OCTA imaging under the imaging conditions specified in step S311. The imaging control unit 101-03 instructs the tomographic imaging apparatus 100 to repeatedly perform OCTA imaging based on the setting instructed by the operator in step S311, and the tomographic imaging apparatus 100 corresponds to the OCT tomographic image. To get. In the present embodiment, the number of repeated imaging in this step is three. However, the number of repeated imaging may be set to an arbitrary number. Further, the present invention is not limited to the case where the photographing time interval between repeated photographing is longer than the photographing time interval of tomographic images in each repeated photographing, and the present invention also includes a case where both are substantially the same. The tomographic imaging apparatus 100 also acquires an SLO image, and executes a tracking process based on the SLO moving image. In this embodiment, the reference SLO image used for the tracking process in repeated OCTA imaging is the reference SLO image set in the first repeated OCTA imaging, and a common reference SLO image is used in all repeated OCTA imaging. Further, during OCTA repeated imaging, in addition to the imaging conditions set in step S311, the same set values are used (not changed) for the selection of left and right eyes and the presence or absence of the tracking process.

<Step 313>
In step S313, the image acquisition unit 101-01 and the image processing unit 101-04 generate a motion contrast image based on the OCT tomographic image acquired in S302. First, the tomographic image generation unit 101-11 performs wave number conversion, fast Fourier transform (FFT), and absolute value conversion (amplitude acquisition) on the interference signal acquired by the image acquisition unit 101-01, thereby obtaining a tomographic image for one cluster. Generate an image.

  Next, the alignment unit 101-41 aligns tomographic images belonging to the same cluster and performs an overlay process. The image feature acquisition unit 101-44 acquires layer boundary data from the superimposed tomographic image. In this embodiment, a variable shape model is used as a layer boundary acquisition method, but any known layer boundary acquisition method may be used. The layer boundary acquisition process is not essential. For example, when the motion contrast image is generated only in three dimensions and the two-dimensional motion contrast image projected in the depth direction is not generated, the layer boundary acquisition process can be omitted. The motion contrast data generation unit 101-12 calculates the motion contrast between adjacent tomographic images in the same cluster. In this embodiment, a decorrelation value Mxy is obtained as motion contrast based on the following equation (1).

  Here, Axy represents the amplitude (of the complex number data after FFT processing) at the position (x, y) of the tomographic image data A, and Bxy represents the amplitude at the same position (x, y) of the tomographic data B. 0 ≦ Mxy ≦ 1, and the closer the amplitude value is, the closer the value is to 1. The decorrelation calculation process as in equation (1) is performed between any adjacent tomographic images (belonging to the same cluster), and the average of the obtained (the number of tomographic images per cluster minus 1) motion contrast values is calculated. An image having pixel values is generated as a final motion contrast image. Here, the motion contrast is calculated based on the amplitude of the complex data after the FFT processing, but the method of calculating the motion contrast is not limited to the above. For example, motion contrast may be calculated based on phase information of complex number data, or motion contrast may be calculated based on both amplitude and phase information. Alternatively, the motion contrast may be calculated based on the real part and the imaginary part of the complex data.

  In the present embodiment, the decorrelation value is calculated as the motion contrast, but the method of calculating the motion contrast is not limited to this. For example, the motion contrast may be calculated based on the difference between the two values, or the motion contrast may be calculated based on the ratio between the two values. Further, in the above description, the final motion contrast image is obtained by obtaining an average value of a plurality of acquired decorrelation values, but the present invention is not limited to this. For example, an image having a median value or a maximum value of a plurality of acquired decorrelation values as pixel values may be generated as a final motion contrast image.

<Step 314>
In step S314, the image processing unit 101-04 generates a high-contrast synthesized motion contrast image by three-dimensionally aligning and averaging the motion contrast image groups obtained through repeated OCTA imaging. Note that the composition process is not limited to simple addition averaging. For example, it may be an average value after arbitrarily weighting the luminance value of each motion contrast image, or an arbitrary statistical value including a median value may be calculated. Further, the case where the alignment process is performed two-dimensionally is also included in the present invention. The composition unit 101-42 may determine whether or not a motion contrast image inappropriate for the composition process is included, and then perform the composition process by removing the motion contrast image determined to be inappropriate. For example, when an evaluation value (for example, an average value of decorrelation values or fSNR) is out of a predetermined range with respect to each motion contrast image, it may be determined to be unsuitable for the synthesis process.

  In the present embodiment, after the synthesizing unit 101-42 synthesizes the motion contrast image three-dimensionally, the correction unit 101-43 performs a process of three-dimensionally suppressing (reducing) projection artifacts generated in the motion contrast image. Here, the projection artifact refers to a phenomenon in which the motion contrast in the retinal surface blood vessels is reflected on the deep layer side (the deep retinal layer, the outer retinal layer, or the choroid), and a high decorrelation value is actually generated in the deep layer region where no blood vessel exists. . The correcting unit 101-43 executes a process for suppressing the projection artifact 802 generated on the three-dimensional synthesized motion contrast image. Although any known projection artifact suppression technique may be used, Step-down Exponential Filtering is used in the present embodiment. In Step-down Exponential Filtering, projection artifacts are suppressed by executing the processing represented by Expression (2) for each A scan data on the three-dimensional motion contrast image.

Here, γ is a negative attenuation coefficient, D (x, y, z) is a decorrelation value before the projection artifact suppression process, and D _E (x, y, z) is a decorrelation value after the suppression process. Represents.

  Next, the projection unit 101-45 projects a motion contrast image in a depth range based on the layer boundary position acquired by the image feature acquisition unit 101-44 in S303, and generates a motion contrast front image. Although projection may be performed in an arbitrary depth range, in this embodiment, two types of front synthesized motion contrast images are generated in the depth range of the retina surface layer and the deep retina layer. In addition, as a projection method, either maximum value projection (MIP) or average value projection (AIP) can be selected, and in this embodiment, projection is performed with maximum value projection.

<Step 315>
In step S315, the image processing apparatus 101 obtains the acquired image group (SLO image or tomographic image), imaging condition data of the image group, and generated generation condition data associated with the generated three-dimensional and motion contrast front image, The data is stored in the external storage unit 102 in association with the information for identifying the test eye.

  The above steps are performed, and the fundus blood vessel image acquisition processing procedure in this embodiment is completed.

  Next, a specific processing procedure for specifying the NPA region of the fundus blood vessel image (OCTA image) in step S340 of the present embodiment will be described with reference to FIG. In the present invention, the smoothing process in step S343 is not an essential process and may be omitted.

<Step 341>
In step S341, the coordinate conversion unit 101-462 converts the orthogonal coordinate OCTA image to the polar coordinate OCTA image based on the determined pole. That is, the coordinate conversion unit 101-462 performs coordinate conversion processing for converting a fundus blood vessel image from an orthogonal coordinate image to a polar coordinate image (an example of a coordinate image different from the orthogonal coordinate image) based on the determined pole. The value of the angle step δθ depends on the OCTA image size S and the pixel dimension p. The angle step for generating the loss-less image in polar coordinates can be calculated from the cosine method as shown in Equation (3).

  Here, acos is an arccosine function and size S is the distance in mm from the center of the OCTA image to its furthest point. For example, if the resolution of a 3x3mm OCTA image is 232x232 pixels,

And p = 13 μm, and the angle step δθ is about 0.35 degrees. The sample angle θ (i) is set to θ (i) = δθxi. Here, i is the index of the sample. The first sample angle, θ (0) = 0 degrees. FIG. 5C shows the processing result of this step. Here, the coordinate conversion is described by taking, as an example, the conversion from orthogonal coordinates to polar coordinates. However, the present invention is not limited to this conversion method, and other methods, for example, conversion to non-orthogonal coordinates (for example, spherical or cylindrical coordinates) may be used.

<Step 342>
In step S342, the extraction unit 101-462, which is an example of a boundary line determination unit, extracts an NPA boundary line from the polar coordinate image generated in step S341. The edge first found from the pole (the lowermost end of the pole image) is set as the NPA boundary line candidate. The first edge is represented by a dotted line in FIG.

<Step 343>
In step S343, the extraction unit 101-462, which is an example of a smoothing processing unit, performs a smoothing process on the NPA boundary line candidates extracted in step S342. In this smoothing process, a moving media filter (moving median filter) of 10 degrees window size is used. The purpose of the smoothing process is to delete the spike (spike, singular point) of the boundary line candidate extracted in step S343. These spikes are generated from discontinuities in the vasculature and are considered unsuitable as NPA boundaries. The solid line in FIG. 6 shows the NPA boundary line after the smoothing process. Although the description has been made using the smoothing process and the moving media filter in the present embodiment, the present invention is not limited to this method, and for example, a moving average method, a Savitzky-Golay filter, a filter based on a Fourier transform method, or the like may be used.

<Step 344>
In step S344, the coordinate conversion unit 101-464 converts the polar NPA boundary line image extracted in step S343 into orthogonal coordinates.

<Step 345>
In step S345, the measurement unit 101-463, which is an example of a generation unit, generates a mask image of the NPA area. The measurement unit 101-463 uses the NPA boundary line of the orthogonal coordinates generated in step S344 to change the pixels in the NPA boundary line to white pixels and the other pixels to black pixels, so that the NPA mask image Generate. A white area of the NPA mask image is an NPA area. FIG. 7 shows an example of an NPA mask image.

  The above steps are performed, and the procedure for identifying the NPA region of the fundus blood vessel image according to the present embodiment is completed.

<Variation 1 of the first embodiment>
In this embodiment, the example of the NPA boundary line extraction process has been described in step S342. However, the present invention is not limited to this method. FIG. 8A shows an example where there is noise in the OCTA image. As in this example, when the pole 10 is assigned to noise 20 (2-pixel noise), the noise 20 corresponds to Radius = 0 of the polar coordinate image as shown in FIG. 8B after conversion to polar coordinates. . Since the first edge has R = 0, it is impossible to correctly extract the NPA boundary line. To avoid this problem, the process in step S342 checks if the pixel is a non-signal pixel (pixel value equals zero) and does the following: When searching for the first edge, Radius = 0 is the signal If it is a pixel (there is a signal in the pixel), the Radius value is incremented sequentially until a non-signal pixel is found. After a non-signal pixel is found, the Radius value is incremented sequentially until more signal pixels are found, and when the first signal pixel is found, it is made the NPA boundary. Furthermore, other processing methods may be used. For example, when determining a pole, when there is a signal in the pixel, a method may be used in which a peripheral non-signal pixel is searched and the non-signal pixel is used as a pole.

<Modification 2 of the first embodiment>
In the present embodiment, the processing example using the fixed window size moving media filter as the processing example of the NPA boundary line smoothing in step S343 has been described. However, the present invention is not limited to this method. Here, an example in which the window size is determined based on the distance from the pole to the NPA boundary line will be described with reference to FIG. FIG. 9A shows an NPA area of an OCTA image in an orthogonal coordinate system. In FIG. 9A, the solid line 100 and the solid line 120 are NPA boundary lines. FIG. 9B shows a polar coordinate image of the same image. In orthogonal coordinates, the gaps 110 and 130 are the same size, but due to the distance from the pole 140, the sizes of the gaps 110 and 130 in the polar coordinate image are greatly different, that is, the gap 110 is larger than the gap 130. Here, the window size of the moving media filter is set as shown in Expression (4).

  Here, R is the distance from the pole. In the present embodiment, μ = 0.05 mm is described as an example, but the value is not limited to this value. Further, the window size may be determined by, for example, a Savitzky-Golay filter or other expressions.

<Modification 3 of Embodiment 1>
In this embodiment, the OCTA image is used as the fundus blood vessel image. However, the present invention is not limited to this, and other fundus blood vessel images such as a fluorescein angiographic image, an ICGA angiographic image, or other A contrast fundus image may be used.

  With the above configuration, using the poles in the NPA region of the fundus vascular image (OCTA image), the orthogonal coordinate OCTA image is converted to the polar coordinate OCTA image, the extraction of the NPA boundary line is accelerated, and the boundary line is smoothed. By performing the conversion processing, spikes in the boundary line are reduced, and it is possible to improve the accuracy of specifying the NPA region.

[Second Embodiment]
In the first embodiment, the polar coordinate OCTA image is generated, the NPA boundary line is extracted, and the NPA region specifying method is described. However, since the NPA area specification is calculated based on one pole, it is easily affected by image noise. In this embodiment, in order to perform NPA region specification more robustly, an NPA region is specified from each based on a plurality of poles, and a final NPA region is specified using each NPA region specification result. That is, the pole determining means according to the present embodiment determines one pole of the avascular region and determines at least one pole based on the boundary line of the avascular region corresponding to one pole. Moreover, the boundary line determination means according to the present embodiment determines one boundary line of the avascular region based on a plurality of boundary lines corresponding to a plurality of poles including one pole and at least one pole.

  Next, it is a flowchart which shows the flow of the operation | movement process of this whole system by the image processing apparatus of this embodiment using the flowchart shown in FIG. However, Step S810, Step S820, Step S830, and Step S920 are the same as Step S310, Step S320, and Step S330 of the processing flow of the first embodiment, respectively, and thus description thereof is omitted. Further, Step S840 and Step S870 are the same as Step S340 in the processing flow of the first embodiment, and thus description thereof is omitted.

<Step S850>
In step S850, the image processing unit 101-04, which is an example of a pole determining unit, acquires a pole candidate to be added from the NPA area specified in the previous step. That is, the pole determining means first determines one pole (first pole) in step S830, and determines at least one pole (at least one other of the plurality of poles) different from one pole. . Here, an example using a reduced NPA area will be described. In this embodiment, as shown in FIG. 12, the reduction ratio = 0.6; the reduction center: the center of gravity of the region. Next, a plurality of points (a plurality of positions) are selected from the outline of the reduced area. Here, 30 points placed at substantially equal intervals on the contour line are set as a plurality of pole (center point) candidates. However, in this embodiment, the determination of the reduction ratio and the center is not limited by this method. For example, the reduction ratio = 0.9; Furthermore, although a fixed number of pole points will be described as an example, the present invention is not limited to this example. For example, the number of points may be determined according to the size of the contour line. In the present embodiment, the method for determining a plurality of pole candidates is not limited to the above method, and for example, pole candidates may be placed two-dimensionally at equal intervals in the NPA region. Alternatively, other methods may be used.

<Step S860>
In step S860, the image processing unit 101-04 selects one candidate from the plurality of pole candidates calculated in step S850. Here, the pole candidate is referred to as a pole (J) or a Jth candidate. The first candidate is J = 1.

<Step S870>
In step S870, the image processing unit 101-04 specifies the NPA (J) region using the pole (J) selected in step S860. Since this process is the same as step S340 in the first embodiment, a description thereof is omitted here. The NPA region identified using the pole (J) is referred to as NPA (J) or intermediate NPA region.

<Step S880>
In step S880, the image processing unit 101-04 stores the intermediate NPA (J) area specified using the pole (J) in step S870 in the storage unit 101-02.

<Step S890>
In step S890, the image processing unit 101-04 determines whether there is a next candidate for the plurality of poles acquired in step S850. If there is a next candidate, it is set as the pole (J + 1), and the process proceeds to step S860. If the identification of each intermediate NPA has been completed from all the poles acquired in step S850, the process proceeds to step S900.

<Step S900>
In step S900, the image processing unit 101-04, which is an example of a boundary line determination unit, synthesizes the final NPA area using all the intermediate NPA areas saved in step S880. That is, the boundary line determination unit according to the present embodiment determines one boundary line by combining a plurality of regions defined by a plurality of boundary lines corresponding to a plurality of poles. In the present embodiment, as a synthesis method, the intermediate NPA areas are added together, and the number of NPA areas common to each pixel (position in the NPA area) is used as a weight. FIG. 13 shows an example. A pixel (position) having a certain weight or more (eg, half the number of pole candidates) is set as the final NPA area. Other synthesis methods may be used. For example, all intermediate NPA areas may be ANDed.

<Step S910>
In step S910, the image processing unit 101-04 determines whether the accuracy of the final NPA synthesized in step S900 is sufficient. In the present embodiment, an example will be described in which addition candidates are again performed from the synthesized final NPA. That is, the processing loop from step S850 to step S900 is executed again. Here, in order to increase the accuracy of the final NPA, the processing loop from step S850 to step S900 is executed twice. In addition, although fixed number of times (2 times) was demonstrated here as an example, the other fixed number of times may be sufficient. Further, for example, a new NPA may be further synthesized using NPA synthesis results obtained from all the processing loops. In step S910, if the process loop is turned once again, the process returns to S850. If the NPA synthesis is sufficient, the process proceeds to step S920. Specifically, a value indicating a change between a boundary line of an avascular region corresponding to one pole and one boundary line determined based on a plurality of boundary lines corresponding to a plurality of poles is equal to or greater than a threshold value. Is determined to be insufficient. That is, the pole determining unit according to the present embodiment determines again as a pole to which at least one pole is added when the value indicating the change is equal to or greater than the threshold value. If the value indicating the change is less than the threshold value, it is determined to be sufficient. That is, the pole determining unit according to the present embodiment ends the determination of at least one pole when the value indicating the change is less than the threshold value. Note that the value indicating the change is, for example, the area of the region defined by the boundary line.

<Step S920>
In step S920, the image processing unit 101-04 stores the NPA area information calculated in the above processing in the storage unit 101-02.

  The above steps are performed, and the procedure for identifying the NPA region of the fundus blood vessel image according to the present embodiment is completed.

<Modification 1 of Second Embodiment>
In this embodiment, the value of the angle step δθ in step S341 depends on the size S of the OCTA image and the pixel dimension p, and a fixed value is used as the angle step δθ. However, the value is not limited to a fixed value and can be changed. It may be. For example, the angle step δθ (i) may be determined so that the sampling points are substantially equally spaced on the NPA boundary line. The sampling angle θ (i) = θ (i−1) + δθ (i) is set. The following process is an example. In step S342, the image processing unit 101-04 extracts the NPA boundary line, and then sets the angular step δθ (i so that the sampling points (i) are equally spaced (or substantially equally spaced) on the boundary line. ). When the NPA specifying process is executed again in step S870, the angle step δθ (i) is used for the coordinate conversion in step S341. FIG. 10 shows an example. Alternatively, in step S342, the sample angle θ (i) may be calculated based on the extracted distance R (j) to the NPA boundary line. The following is an example. First, the distance R (0) to the NPA boundary line at the sample angle θ (0) = 0 degree is calculated, and K (0) = θ (0) × R (0) is set. Then, θ (i) = K (i−1) / R (i−1). However, the angle θ (i) is in radians.

<Modification 2 of the second embodiment>
In the present embodiment, a pole candidate to be added may be acquired based on the analysis result of the first derivative of the boundary line on the polar coordinates of the NPA region identified in the previous step in step S850. An example of this will be described with reference to FIG. FIG. 14A shows an NPA region boundary line in polar coordinates. FIG. 14B shows the result of the primary differentiation corresponding to the boundary line. And a pole candidate is decided based on the peak which appeared in the primary differentiation. Here, the threshold value = 3 pixels. A dotted line in FIG. 14B indicates a threshold value. However, here, a fixed threshold value is used for explanation, but the value is not limited to this value. For example, the value may depend on the angle step δθ or may be another value. The value of the first derivative determines the pole candidate based on the position exceeding the ± threshold. As an example, description will be made using θA and θB having a first derivative peak in FIG. FIG. 15 is a partial enlargement of FIG. 14A including θA and θB. The distance RA is the distance R at the position of the angle (θA + Δθ). The pole candidate A to be added is (θA + Δθ, γRA). Similarly, the pole candidate B is (θB−Δθ, γxRB). Whether Δθ is added to or subtracted from the angle θ is determined based on whether the first derivative is true or negative. In the description here, Δθ = 7.5 degrees and γ = 0.78, but other values determined based on experience may be used. The closer to γ = 1.0, the more pole candidates can be placed closer to the singular point of a complex region.

<Modification 3 of the second embodiment>
In the present embodiment, the fixed number of times has been described as an example in step S910. However, the present invention is not limited to this method. For example, it may be determined whether to execute the processing loop again based on the change in the NPA synthesis result in step S900. That is, if the difference between the NPA synthesis result and the result of the preprocessing loop is small, the processing loop may be stopped. Next, an example will be described. The NPA area area value of the processing loop k is A (k). Then, the change ΔA = 100% × (A (k) −A (k−1)) / A (k−1). If the change ΔA is equal to or less than the threshold value = 5%, the processing loop is ended. Here, the threshold value for the end condition is set to 5%. However, for example, the condition change ΔA may be determined based on the processing time and the complexity of the NPA area. If the NPA region area value A (k) tends to converge, the processing loop may be terminated. For example, if the change ΔA (k) in the processing loop k (ie, ΔA (k) −ΔA (k−1)) is equal to or less than the threshold value, the process may be terminated.

  With the above configuration, using a plurality of poles in the NPA area of the fundus blood vessel image (OCTA image), the NPA area is specified based on each pole, and further, the synthesis in the specified plurality of NPA areas is performed. Thus, it is possible to reduce the influence of the fundus blood vessel image noise and further improve the accuracy of specifying the NPA region.

[Third embodiment]
In the second embodiment, an NPA region is identified from each of the first estimated NPA region, based on a plurality of poles from NPA (0), and a final NPA region is identified using each NPA region identification result. The method for performing the above has been described, but the image enhancement processing has been performed in step S820. In the present embodiment, an example will be described in which the image enhancement processing is performed based on the NPA region, NPA (0) estimated first. Since the configuration of the image processing apparatus according to this embodiment is the same as that of the first embodiment, description thereof is omitted. Since the operation processing of the entire system by the image processing apparatus of the present embodiment is the same as that of the second embodiment, description thereof is omitted. However, step S865 is added between step S860 and step S870. The operation of step S865 is as follows.

<Step S865>
In step S865, the enhancement unit 101-461 performs image enhancement of the fundus blood vessel image while minimizing the influence of noise. Step S865 is the same as step S320, but the binarization process (detailed process 4 of step S320) is different, and will be described in detail here. The threshold value RL is determined based on the analysis result of NPA (0). That is, the threshold value RL = NF + 3 × SD. However, NF (noise floor) is an average value of the pixel values in the region NPA (0). SD is a standard deviation value of pixel values in the region NPA (0). This is the end of the detailed description of the NPA region specifying procedure for the fundus blood vessel image of the present embodiment. With the above configuration, by performing binarization processing based on the information of the NPA area that is the attention area of the fundus blood vessel image (OCTA image), it is possible to reduce the influence of noise for identifying the NPA area, The blood vessel information is emphasized, and the accuracy of specifying the boundary line of the NPA region can be further improved.

[Fourth embodiment]
In the second embodiment, the method for specifying a two-dimensional NPA region has been described based on a two-dimensional fundus blood vessel image. In this embodiment, an example will be described in which a three-dimensional NPA region is specified based on a three-dimensional fundus blood vessel image. Since the configuration of the image processing apparatus according to this embodiment is the same as that of the first embodiment, description thereof is omitted. Since the operation processing of the entire system by the image processing apparatus of the present embodiment is the same as that of the second embodiment, description thereof is omitted. However, since the processes of step S320, step S341, step S344 and step S850 are different, detailed description will be given here.

<Step S320>
In step S320 of the present embodiment, the enhancement unit 101-461 performs three-dimensional image enhancement of the fundus blood vessel image while minimizing the influence of noise. The LPF process used in step S320 of the present embodiment uses a three-dimensional filter. Other processes are also performed three-dimensionally.

<Step S341>
In step S341 of the present embodiment, the coordinate conversion unit 101-462 converts a three-dimensional OCTA image with orthogonal coordinates into a three-dimensional OCTA image with spherical coordinates (three-dimensional polar coordinates) based on the determined poles. . At this time, the determined pole is the origin of the three-dimensional polar coordinates.

<Step S344>
In step S344 of the present embodiment, the coordinate conversion unit 101-462 converts a spherical coordinate three-dimensional OCTA image into a rectangular coordinate three-dimensional OCTA image based on the determined pole.

<Step S850>
In step S850 of the present embodiment, the image processing unit 101-04 acquires a pole candidate to be added from the three-dimensional NPA region specified in the previous step. Here, the volume boundary surface of the reduced three-dimensional NPA region is used. And a process performs a two-dimensional process and a similar process. However, the method is not limited to this method, and other three-dimensional processing methods may be used.

<Modification 1 of the fourth embodiment>
In the present embodiment, the description has been made using spherical coordinates for three-dimensional coordinate conversion, but other three-dimensional coordinates may be used. For example, coordinate conversion from three-dimensional orthogonal coordinates to cylindrical coordinates may be used. In this case, processing such as LPF may be performed two-dimensionally. Anatomically, the retina is more like a cylinder than a sphere, so it can be processed more appropriately with this method. With the above configuration, it is possible to specify a three-dimensional NPA region from a three-dimensional fundus blood vessel image (OCTA image), and to analyze the volume of the NPA and the like.

[Other Embodiments]
In each of the above embodiments, the present invention is realized as the image processing apparatus 101. However, the embodiment of the present invention is not limited to the image processing apparatus 101 alone. For example, the present invention can take an embodiment as a system, apparatus, method, program, storage medium, or the like.

Claims (26)

Pole determining means for determining the pole of the avascular region in the fundus blood vessel image of the eye to be examined;
Coordinate conversion means for executing a coordinate conversion process for converting the fundus blood vessel image into a coordinate image different from the orthogonal coordinate image based on the determined pole;
Boundary line determination means for determining a boundary line of the avascular region in the different coordinate images;
An image processing apparatus comprising:   The image processing apparatus according to claim 1, wherein the coordinate conversion unit converts a boundary line image indicating the determined boundary line from the different coordinate image to the orthogonal coordinate image. The boundary line determining means further comprises a smoothing processing means for performing a smoothing process of the determined boundary line,
The image processing apparatus according to claim 2, wherein the coordinate conversion unit converts a boundary line image indicating a boundary line obtained by the smoothing process from the different coordinate image to the orthogonal coordinate image. The pole determining means determines one pole of the avascular region, determines as a pole to add at least one pole based on a boundary line of the avascular region corresponding to the one pole,
The boundary line determining means determines one boundary line of the avascular region based on a plurality of boundary lines corresponding to a plurality of poles including the one pole and the at least one pole. The image processing apparatus according to claim 1. The coordinate conversion means performs the coordinate conversion processing based on each of the plurality of poles,
The said boundary line determination means determines the said one boundary line based on the said some boundary line in the said several different coordinate image corresponding to each of these said some pole, The said one boundary line is characterized by the above-mentioned. Image processing device.   6. The image processing apparatus according to claim 4, wherein the one boundary line is determined by combining a plurality of regions defined by the plurality of boundary lines.   The plurality of positions that are substantially equidistant along a boundary line of the reduced region of the avascular region corresponding to the one pole are determined as the additional poles. The image processing apparatus according to any one of the above.   7. The pole determination unit according to claim 4, wherein the pole determination unit determines the pole to be added as the at least one pole based on a value of a first derivative of the determined boundary line in the different coordinate image. The image processing apparatus according to claim 1.   By performing binarization processing on the fundus blood vessel image using threshold values determined based on a plurality of pixel values in the avascular region corresponding to the one pole, blood vessels in the fundus blood vessel image The image processing apparatus according to claim 4, further comprising an enhancement unit that executes an enhancement process for enhancing a structure.   The pole determining means is a value indicating a change between a boundary line of an avascular region corresponding to the one pole and one boundary line determined based on a plurality of boundary lines corresponding to the plurality of poles. When the value is equal to or greater than a threshold value, at least one pole is re-determined as an additional pole, and when the value indicating the change is less than the threshold value, the determination of at least one pole is terminated. The image processing apparatus according to any one of 4 to 9. Image acquisition means for acquiring a two-dimensional fundus blood vessel image of the eye to be examined;
The image processing according to any one of claims 1 to 10, wherein the coordinate conversion means converts the two-dimensional fundus blood vessel image from the orthogonal coordinate image to a polar coordinate image as the coordinate conversion processing. apparatus. Image acquisition means for acquiring a three-dimensional fundus blood vessel image of the eye to be examined;
The coordinate conversion means converts the three-dimensional fundus blood vessel image from the orthogonal coordinate image into a spherical coordinate image or a cylindrical coordinate image as the coordinate conversion processing. An image processing apparatus according to 1.   The image processing device according to claim 1, wherein the pole determining unit determines the pole based on an analysis result of the fundus blood vessel image or the fundus image of the eye to be examined. .   The image processing apparatus according to claim 1, wherein the pole determining unit determines the pole in accordance with an instruction from an examiner with respect to the fundus blood vessel image. In accordance with an instruction from the examiner for the fundus blood vessel image of the eye to be examined, position determining means for determining the position of the avascular region in the fundus blood vessel image;
Boundary line determination means for determining a boundary line of the avascular region based on the determined position;
An image processing apparatus comprising:   The image processing apparatus according to claim 1, further comprising a generation unit configured to generate a mask image of the avascular region based on the determined boundary line.   The image processing according to any one of claims 1 to 16, further comprising display control means for causing the display means to display information indicating the determined boundary line in a state of being superimposed on the fundus blood vessel image. apparatus. Measuring means for calculating a measurement value related to the area specified by the determined boundary line;
The image processing apparatus according to claim 17, wherein the display control unit displays the measurement value on the display unit in a state of being superimposed on the fundus blood vessel image.   The image processing apparatus according to claim 17 or 18, wherein a position of information indicating the determined boundary line is changeable in accordance with an instruction from an examiner.   The image processing apparatus according to claim 1, wherein the avascular region is a FAZ of the retina of the eye to be examined.   21. The image processing apparatus according to claim 1, wherein the fundus blood vessel image is a motion contrast image.   The image processing apparatus according to claim 21, further comprising a reduction unit that executes a process of reducing projection artifacts in the motion contrast image. Alignment means for aligning a three-dimensional motion contrast image of the eye to be examined;
And a synthesis means for synthesizing the three-dimensional motion contrast image obtained by the alignment,
The fundus blood vessel image is a three-dimensional motion contrast image obtained by the synthesis or a motion contrast front image generated using data of at least a part of a depth range in the three-dimensional motion contrast image obtained by the synthesis. The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. Determining the pole of the avascular region in the fundus blood vessel image of the eye to be examined;
Performing a coordinate conversion process for converting the fundus blood vessel image into a coordinate image different from the orthogonal coordinate image based on the determined pole;
Determining a borderline of the avascular region in the different coordinate images;
An image processing method comprising: In accordance with an instruction from the examiner on the fundus blood vessel image of the eye to be examined, determining a position of an avascular region in the fundus blood vessel image;
Determining a borderline of the avascular region based on the determined position;
An image processing method comprising:   A program for causing a computer to execute the image processing method according to claim 24 or 25.