CN110097502B - Measuring method and device for fundus non-perfusion area and image processing method - Google Patents
Measuring method and device for fundus non-perfusion area and image processing method Download PDFInfo
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- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
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Abstract
The application discloses a method and a device for measuring an eye fundus non-perfusion area and an image processing method. The method comprises the following steps: acquiring an eye fundus OCTA blood vessel network image of an eye to be detected; clustering the fundus blood vessel network images, and forming a blood vessel area and a non-blood vessel area on the clustered fundus blood vessel network images; corroding the clustered fundus blood vessel network image, wherein a first region and at least one second region are formed on the corroded fundus blood vessel network image; searching a second region with the largest area in the fundus blood vessel network image after corrosion treatment; expanding the corroded fundus vascular network image to obtain a fundus non-perfusion area of the eye to be detected, wherein the fundus non-perfusion area comprises a second area with the largest area, and the expanding treatment and the corroding treatment adopt nuclei with the same size; and calculating the pixel number of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area. By adopting the method and the device, the efficiency of central bloodless perfusion division can be effectively improved.
Description
Technical Field
The application relates to the technical field of optical coherence tomography, in particular to a method and a device for measuring an eye fundus non-perfusion area and an image processing method.
Background
Retinal vascular disease is a leading cause of blindness, and Optical Coherence Tomography (OCT) has become the standard imaging method for assessing fluid accumulation and guiding treatment in these diseases in ophthalmology. OCT provides cross-sectional and three-dimensional (3D) imaging of the retina and optic nerve head with micron-scale depth resolution, and structural OCT enhances the ability of clinicians to detect and monitor fluid exudation associated with retinal vascular disease.
A common way of scanning OCT images is to combine multiple scans, with 304 × 304 or more, which can result in a relatively continuous blood vessel and thus a central avascular region of the retina.
However, this scanning requires more scanning time, making it inefficient to obtain the central avascular region of the retina.
Disclosure of Invention
The application provides a method and a device for measuring an eye fundus non-perfusion area and an image processing method, which can improve the efficiency of calculating the area of the eye fundus non-perfusion area or the efficiency of obtaining the eye fundus non-perfusion area under the condition of small scanning amount.
In a first aspect, an embodiment of the present application provides an OCTA vascular network image processing method, including:
acquiring a fundus vascular network image of the eye to be inspected, the fundus vascular network image being generated by Optical Coherence Tomography Angiography (OCTA);
clustering the fundus blood vessel network images, and forming a blood vessel area and a non-blood vessel area on the clustered fundus blood vessel network images;
performing corrosion treatment on the fundus blood vessel network image after the clustering treatment, wherein a first region and at least one second region are formed on the fundus blood vessel network image after the corrosion treatment, the first region comprises the blood vessel region, the at least one second region comprises the avascular region, the first region is a communicated integral region, and each second region is a communicated integral region;
searching a second region with the largest area in the fundus blood vessel network image after the corrosion treatment;
and performing expansion processing on the fundus vascular network image subjected to the corrosion processing to obtain a fundus non-perfusion area of the eye to be detected, wherein the fundus non-perfusion area comprises a second area with the largest area, and the expansion processing and the corrosion processing adopt a nucleus with the same size.
In a possible implementation manner, after the searching for the second region with the largest area in the fundus blood vessel network image after the erosion processing and before the performing the dilation processing on the fundus blood vessel network image after the erosion processing, the method further includes:
and filling the area except the second area with the largest area in the fundus blood vessel network image after the corrosion treatment so as to distinguish the second area with the largest area.
In one possible implementation, the acquiring a fundus angiomesh image of the eye to be examined includes:
acquiring a fundus blood vessel network image of the eye to be inspected by an optical coherence tomography device comprising a light source and an optical probe;
the acquiring of the fundus angiomesh image of the eye to be examined by an optical coherence tomography apparatus including a light source and an optical probe includes:
repeatedly scanning, by the optical probe, a retina of the eye to be examined with measurement light, wherein the measurement light is provided by the light source, the repeated scanning comprising at least two scans of a same location of the retina;
acquiring at least one spectral domain interference signal associated with the retina during a scan;
extracting data relating to cell, tissue or particle motion in the retina from the spectral domain interference signal;
and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
In one possible implementation, the clustering the fundus blood vessel network image includes:
and carrying out filtering processing on the fundus blood vessel network images, and classifying the fundus blood vessel network images after the filtering processing by adopting a K mean value method.
In one possible implementation manner, the filtering processing on the fundus blood vessel network image includes:
and carrying out interpolation processing on the fundus blood vessel network image, and carrying out filtering processing on the fundus blood vessel network image after the interpolation processing.
In a second aspect, an embodiment of the present application further provides an OCTA vascular network image processing method, including:
acquiring a fundus vascular network image of the eye to be examined, the fundus vascular network image being generated by optical coherence tomography angiography;
clustering the fundus blood vessel network images, and forming a blood vessel area and a non-blood vessel area on the clustered fundus blood vessel network images;
performing corrosion treatment on the fundus blood vessel network image after the clustering treatment, wherein a first region and at least one second region are formed on the fundus blood vessel network image after the corrosion treatment, the first region comprises the blood vessel region, the at least one second region comprises the avascular region, the first region is a communicated integral region, and each second region is a communicated integral region;
performing expansion processing on the fundus vascular network image subjected to the corrosion processing to obtain at least one third area, wherein each third area is a communicated integral area, each third area comprises one second area, and the expansion processing and the corrosion processing adopt kernels with the same size;
searching a third region with the largest area in the fundus vascular network image after expansion processing, wherein the third region with the largest area is a fundus non-perfusion region of the eye to be detected;
and calculating the pixel number of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area.
In a possible implementation manner, after the searching for the third region with the largest area in the fundus vascular network image after the dilation processing, the method further includes:
and performing filling processing on a region other than the third region with the largest area in the fundus blood vessel network image after the expansion processing to distinguish the third region with the largest area.
In one possible implementation, the acquiring a fundus angiomesh image of the eye to be examined includes:
acquiring a fundus blood vessel network image of the eye to be inspected by an optical coherence tomography device comprising a light source and an optical probe;
the acquiring of the fundus angiomesh image of the eye to be examined by an optical coherence tomography apparatus including a light source and an optical probe includes:
repeatedly scanning, by the optical probe, a retina of the eye to be examined with measurement light, wherein the measurement light is provided by the light source, the repeated scanning comprising at least two scans of a same location of the retina;
acquiring at least one spectral domain interference signal associated with the retina during a scan;
extracting data relating to cell, tissue or particle motion in the retina from the spectral domain interference signal;
and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
In one possible implementation, the clustering the fundus blood vessel network image includes:
and carrying out filtering processing on the fundus blood vessel network images, and classifying the fundus blood vessel network images after the filtering processing by adopting a K mean value method.
In one possible implementation manner, the filtering processing on the fundus blood vessel network image includes:
and carrying out interpolation processing on the fundus blood vessel network image, and carrying out filtering processing on the fundus blood vessel network image after the interpolation processing.
In a fourth aspect, the present application provides a method for measuring an area of a fundus non-perfusion area of an eye to be examined, including the foregoing method for processing an OCTA vascular network image, and further including calculating a number of pixels of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area.
In a fourth aspect, embodiments of the present application provide an apparatus for measuring an area of an ocular fundus perfusionless area of an eye to be examined, the apparatus comprising:
an acquisition unit configured to acquire a fundus blood vessel network image of the eye to be inspected, the fundus blood vessel network image being generated by optical coherence tomography angiography;
the clustering unit is used for clustering the fundus blood vessel network images, and blood vessel regions and avascular regions are formed on the fundus blood vessel network images after clustering;
the corrosion processing unit is used for carrying out corrosion processing on the fundus blood vessel network image after the clustering processing, a first area and at least one second area are formed on the fundus blood vessel network image after the corrosion processing, the first area comprises the blood vessel area, the at least one second area comprises the avascular area, the first area is a communicated integral area, and each second area is a communicated integral area;
the searching unit is used for searching a second region with the largest area in the fundus blood vessel network image after the corrosion treatment;
the expansion processing unit is used for performing expansion processing on the fundus vascular network image subjected to the corrosion processing to obtain a fundus non-perfusion area of the eye to be detected, the fundus non-perfusion area comprises a second area with the largest area, and the expansion processing and the corrosion processing adopt a kernel with the same size;
and the calculation unit is used for calculating the pixel number of the fundus non-perfusion area so as to calculate the area of the fundus non-perfusion area.
In one possible implementation, the apparatus further includes:
and the filling processing unit is used for filling the area except the second area with the largest area in the fundus blood vessel network image after the corrosion processing so as to distinguish the second area with the largest area.
In a possible implementation, the acquisition unit is specifically configured to acquire a fundus angiomesh image of the eye to be examined by an optical coherence tomography apparatus including a light source and an optical probe.
In a possible implementation, the acquiring unit is specifically configured to perform repeated scanning of the retina of the eye to be inspected by the optical probe with measuring light, wherein the measuring light is provided by the light source, and the repeated scanning includes at least two scans of the same position of the retina; acquiring at least one spectral domain interference signal associated with the retina during a scan; and extracting data relating to cell, tissue or particle motion in the retina from the spectral domain interference signal; and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
In a possible implementation manner, the clustering unit includes:
the filtering processing subunit is used for performing filtering processing on the fundus blood vessel network image;
and the clustering processing subunit is used for classifying the fundus blood vessel network images after the filtering processing by adopting a K mean value method.
In a possible implementation manner, the filtering processing subunit is specifically configured to perform interpolation processing on the fundus blood vessel network image, and perform filtering processing on the fundus blood vessel network image after the interpolation processing.
In a fifth aspect, embodiments of the present application further provide an fundus perfusion-free region measuring apparatus for measuring an area of a fundus perfusion-free region of an eye to be examined, the apparatus including:
an acquisition unit configured to acquire a fundus blood vessel network image of the eye to be inspected, the fundus blood vessel network image being generated by optical coherence tomography angiography;
the clustering unit is used for clustering the fundus blood vessel network images, and blood vessel regions and avascular regions are formed on the fundus blood vessel network images after clustering;
the corrosion processing unit is used for carrying out corrosion processing on the fundus blood vessel network image after the clustering processing, a first area and at least one second area are formed on the fundus blood vessel network image after the corrosion processing, the first area comprises the blood vessel area, the at least one second area comprises the avascular area, the first area is a communicated integral area, and each second area is a communicated integral area;
the expansion processing unit is used for performing expansion processing on the fundus blood vessel network image subjected to the corrosion processing to obtain at least one third area, each third area is a communicated integral area, each third area comprises one second area, and the expansion processing and the corrosion processing adopt kernels with the same size;
the searching unit is used for searching a third area with the largest area in the fundus blood vessel network image after expansion processing, wherein the third area with the largest area is a fundus non-perfusion area of the eye to be detected;
and the calculation unit is used for calculating the pixel number of the fundus non-perfusion area so as to calculate the area of the fundus non-perfusion area.
In one possible implementation, the apparatus further includes:
and a filling processing unit, configured to perform filling processing on a region other than the second region with the largest area in the fundus vascular network image after the expansion processing, so as to distinguish the second region with the largest area.
In a possible implementation, the acquisition unit is specifically configured to acquire a fundus angiomesh image of the eye to be examined by an optical coherence tomography apparatus including a light source and an optical probe.
In a possible implementation, the acquiring unit is specifically configured to perform repeated scanning of the retina of the eye to be inspected by the optical probe with measuring light, wherein the measuring light is provided by the light source, and the repeated scanning includes at least two scans of the same position of the retina; acquiring at least one spectral domain interference signal associated with the retina during a scan; and extracting data relating to cell, tissue or particle motion in the retina from the spectral domain interference signal; and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
In a possible implementation manner, the clustering unit includes:
the filtering processing subunit is used for carrying out filtering processing on the fundus blood vessel network image;
and the clustering processing subunit is used for classifying the fundus blood vessel network images after the filtering processing by adopting a K mean value method.
In a possible implementation manner, the filtering processing subunit is specifically configured to perform interpolation processing on the fundus blood vessel network image, and perform filtering processing on the fundus blood vessel network image after the interpolation processing.
In a sixth aspect, embodiments of the present application further provide an apparatus for measuring an area of fundus perfusion-free, the apparatus comprising a processor and a memory, the processor and the memory being coupled, the memory having stored therein program instructions that, when executed by the processor, cause the apparatus to perform the method of the first or second aspect.
In a seventh aspect, the present application provides a computer-readable storage medium, in which program instructions are stored, and when executed by a processor of a computer, the program instructions cause the processor to execute the method according to the first aspect or the second aspect.
In an eighth aspect, the present application further provides a computer program product, in which program instructions are stored, and when executed by a processor of a computer, the program instructions cause the processor to execute the method according to the first aspect or the second aspect.
By implementing the embodiment of the application, the retina of the eye to be inspected is scanned through the OCTA, so that the fundus vascular network image is obtained, then the fundus vascular network image is subjected to clustering processing and corrosion processing, the fundus vascular network image comprising a first area and at least one second area is obtained, after the second area with the largest area is found, expansion processing is performed, the area reduced due to corrosion processing can be effectively made up, and then the fundus non-perfusion area can be obtained, the number of scanning points for scanning the retina of the eye to be inspected through the OCTA can be reduced, scanning with higher density is avoided, the efficiency of obtaining the fundus non-perfusion area can be improved, and the complete FAZ area can be obtained.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
FIG. 1 is a schematic flow chart of a method for measuring an area without perfusion in the fundus oculi according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an optical coherence tomography imaging apparatus;
fig. 3 is a schematic flowchart of an image processing method according to an embodiment of the present application;
FIG. 4a is a schematic diagram of a fundus vascular network image provided by an embodiment of the present application;
FIG. 4b is a schematic diagram of a clustered fundus blood vessel network image according to an embodiment of the present application;
FIG. 4c is a schematic diagram of a fundus angiomesh image after erosion treatment according to an embodiment of the present application;
FIG. 4d is a schematic diagram of a fundus vascular network image after filling processing according to an embodiment of the present application;
FIG. 4e is a schematic view of a non-perfusion region of the fundus provided by an embodiment of the present application;
FIG. 5 is a schematic flow chart of another method for measuring the perfusion-free zone of the fundus oculi according to the embodiment of the present application;
FIG. 6 is a schematic structural diagram of a fundus perfusion-free region measuring device according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of another fundus non-perfusion region measuring device provided in the embodiments of the present application;
fig. 8 is a schematic structural diagram of a clustering unit according to an embodiment of the present application;
FIG. 9 is a schematic structural diagram of a fundus perfusion-free region measuring device according to an embodiment of the present application;
FIG. 10 is a schematic structural diagram of another fundus perfusion-free region measuring device provided in an embodiment of the present application;
fig. 11 is a schematic structural diagram of a clustering unit according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of another fundus perfusion-free region measuring device provided in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be further described in detail with reference to the accompanying drawings.
The terms "first" and "second," and the like in the description, claims, and drawings of the present application are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for measuring an ocular fundus non-perfusion area provided in an embodiment of the present application, where the method for measuring an ocular fundus non-perfusion area is applicable to an ocular fundus non-perfusion area measuring apparatus, and the ocular fundus non-perfusion area measuring apparatus may be any electronic device capable of measuring an ocular fundus non-perfusion area. As shown in fig. 1, the method for measuring the perfusion-free region of the fundus oculi includes:
s101, acquiring a fundus blood vessel network image of the eye to be detected, wherein the fundus blood vessel network image is generated by optical coherence tomography angiography.
In the embodiment of the application, the fundus blood vessel network image is an image related to the retina, namely the fundus blood vessel network image comprises an image of a fundus non-perfusion area needing to be extracted. It is understood that the fundus angiomesh image is an image generated by the OCTA technique. Wherein, the eye to be inspected can be understood as the eye of the user to be inspected.
Specifically, 256 × 256 scanning points can be used to scan the eye to be examined, and the image is acquired while scanning, so that the fundus blood vessel network image can be obtained after the scanning is completed. If the light is emitted to a point on the retina of the eye to be detected, a beam of parallel light is emitted, the parallel light is converged on the retina of the eye to be detected to form converged light, and therefore after the retina of the eye to be detected is scanned, the fundus blood vessel network image can be obtained.
Alternatively, a fundus angiomesh image of the eye to be examined may be acquired by an OCT apparatus including a light source and an optical probe.
Specifically, the method for acquiring a fundus blood vessel network image of an eye to be inspected by an optical coherence tomography device comprising a light source and an optical probe comprises the following steps:
repeatedly scanning the retina of the eye to be examined by the optical probe with measuring light, wherein the measuring light is provided by the light source, the repeated scanning comprises at least two scans of the same position of the retina;
acquiring at least one spectral domain interference signal associated with the retina during the scan;
extracting data related to cell, tissue or particle motion in the retina from the spectral domain interference signal;
the fundus blood vessel network image is calculated according to the data related to the movement of cells, tissues or particles in the retina.
In the present embodiment, reference is made to fig. 2 for how a fundus blood vessel network image of an eye to be inspected is acquired by an optical coherence tomography apparatus including a light source and an optical probe, and fig. 2 is a schematic diagram of an optical coherence tomography imaging apparatus. The principle of OCT imaging is similar to that of ultrasonic imaging, and both of them use interference of waves.
As shown in fig. 2, light from a light source 101 enters a coupler 102, and is split into measurement light and reference light at the coupler 102. The measurement light finally enters the eye 108 via the optical fiber 105 and the scanning device 107, scans the retina 110 of the eye 108, is scattered by the retina 110 of the eye 108, and a part of the measurement light is returned as it is and enters the coupler 102 via the optical fiber 105. And the reference light is made to strike the mirror 104 via the optical fiber 103, and then returned to the coupler 102 via the optical fiber 103. The distance from coupler 102 to retina 110 and back to coupler 102 is not much different from the distance from coupler 102 to mirror 104 and back to coupler 102. The returned measurement light and the reference light interfere at the coupler 102, and the interference light is collected by the detector 120.
Further, OCT is structural and OCTA is functional. More vividly, the OCT detects whether there is something in the scan point, while what is near the scan point will not move. If there is something in a moment and nothing in the same scanning point, it is said that there is something moving in the scanning point. Therefore, the OCTA requires at least two OCT images to be scanned. Specifically, the OCTA can obtain 4 images by scanning. It is understood that the above is only an example of the OCTA, and should not be construed as a limitation to the present embodiment.
That is to say, in the embodiment of the present application, the retina can be dynamically scanned by the optical coherence tomography angiography, so that the fundus angionet image is obtained.
Optionally, acquiring a fundus angiomesh image of the eye to be examined includes:
scanning the eye to be detected through OCTA to obtain a scanning signal of a scanning point;
processing the scanning point according to noise and zeroing the gray value of the scanning point under the condition that the scanning signal of the scanning point is smaller than a reference threshold value;
when the eye to be inspected is completely scanned, a fundus angio-network image is obtained.
In this embodiment, the reference threshold is a threshold used for measuring a scanning signal of a scanning point, and therefore, a specific value of the reference threshold is not limited in this embodiment. The reference threshold may be a preset threshold, for example, the fundus perfusion-free region measuring device may set the reference threshold by receiving a setting instruction input by a user, or may be automatically set by the fundus perfusion-free region measuring device, and the like, and the present embodiment is not limited thereto.
In this embodiment, if the retina of the eye to be inspected is scanned by the OCTA method, if the scanning signal of the scanning point is smaller than the reference threshold, the scanning point may be processed by noise, for example, the gray level of the scanning point is set to zero; and then continuing to scan the next scanning point, if the scanning signal of the next scanning point is greater than the reference threshold, reserving the next scanning point until the scanning is finished, and obtaining the fundus angiocarpy image.
It is understood that the scanning signal of the scanning point can be understood as the light intensity of the scanning point, and the process of zeroing the gray value of the scanning point can be understood as the process of zeroing the brightness of the scanning point. If the gray scale value of the scanning point is between 0-155, and if the scanning signal of the scanning point is less than the reference threshold, the gray scale value of the scanning point can be 0.
S102, clustering the fundus blood vessel network images, and forming a blood vessel region and a blood vessel-free region on the clustered fundus blood vessel network images.
In the embodiment of the application, after the fundus blood vessel network images are clustered, blood vessel regions and blood vessel-free regions can be formed on the clustered fundus blood vessel network images. That is, when the fundus blood vessel network image of the eye to be inspected is acquired through the OCTA, the distinction between the blood vessel region and the avascular region in the fundus blood vessel network image may not be obvious or can not be distinguished, but the blood vessel region and the avascular region in the fundus blood vessel network image after the clustering processing can be effectively distinguished through the clustering processing of the fundus blood vessel network image. It is understood that, when the clustering process is performed, the clustering analysis can be performed using the characteristics of the blood vessel region and the characteristics of the avascular region.
Optionally, an embodiment of the present application provides a clustering method, which is as follows:
clustering the fundus blood vessel network images, comprising:
and filtering the fundus blood vessel network images, and classifying the fundus blood vessel network images after filtering by adopting a K mean value method.
In the embodiment, the noise in the fundus blood vessel network image can be effectively filtered out by filtering the fundus blood vessel network image, and preparation can be made for implementing a K-means method. The K-means method is used for clustering regions in the filtered fundus blood vessel network image, so that a blood vessel region and a blood vessel-free region are obtained.
It can be understood that, in this embodiment, before performing the filtering processing on the fundus blood vessel network image, the fundus blood vessel network image may be further subjected to interpolation processing to make the blood vessels clearer, and then the filtering processing is performed on the fundus blood vessel network image after the interpolation processing.
The interpolation processing method can perform interpolation processing by a Fourier transform method, so that the situation that a blood vessel region and an avascular region cannot be effectively distinguished due to too few scanning points in a fundus blood vessel network image obtained by noise reduction processing can be effectively compensated.
And S103, carrying out corrosion treatment on the clustered fundus blood vessel network image, wherein a first area and at least one second area are formed on the fundus blood vessel network image after the corrosion treatment as the number of scanning points for scanning the retina of the detected eye is reduced in the past, and referring to fig. 4b and 4 c. Referring to fig. 4c, the first region is a black region and the second region is a white region. The first region includes a vascular region, and the at least one second region includes an avascular region, the first region being a connected global region, each second region being a connected global region.
In the embodiment of the application, the fundus vascular network image after clustering processing of the blood vessel region and the avascular region is formed is subjected to corrosion processing, so that the areas of the blood vessel region and the avascular region can be effectively reduced, and the fundus vascular network image with the blood vessel region and the avascular region separated is formed. Specifically, the erosion treatment is performed on the blood vessel image at the bottom of the eye, so that a first region and at least one second region can be formed, the first region can comprise a blood vessel region, and the second region can comprise a blood vessel-free region. The first region is understood to be a continuous overall region, and each second region is understood to be a continuous overall region.
And S104, searching a second region with the largest area in the fundus blood vessel network image after the corrosion treatment.
It can be understood that after the fundus blood vessel network image is subjected to the erosion processing, a lot of noise points such as a small white area in fig. 4c may exist in the obtained fundus blood vessel network image, so that the fundus non-perfusion area of the eye to be inspected can be obtained by searching for the second area with the largest area in the fundus blood vessel network image.
And S105, performing expansion processing on the corroded fundus blood vessel network image to obtain a fundus non-perfusion area of the eye to be detected, wherein the fundus non-perfusion area comprises a second area with the largest area, and the expansion processing and the corrosion processing adopt nuclei with the same size.
In the embodiment of the present application, the expansion processing is performed on the fundus vascular network image after the erosion processing, which may be understood as performing the expansion processing on the fundus vascular network image of the first region and the at least one second region that are formed. Specifically, the expansion process is performed on the second region having the largest area and the first region in the blood vessel network image of the fundus.
In the embodiment of the application, the area of the second area with the largest area, namely the fundus non-perfusion area, is reduced through corrosion treatment, so that expansion treatment is performed, the area reduced by corrosion can be effectively made up, and the real fundus non-perfusion area is obtained. It is understood that in the present embodiment, the degree of corrosion by the corrosion treatment is the same as the degree of swelling by the swelling treatment, that is, the nucleus by the corrosion treatment is the same as the nucleus by the swelling treatment.
Optionally, after searching for the second region with the largest area in the fundus vascular network image after the erosion processing, and before performing the dilation processing on the fundus vascular network image after the erosion processing, the method further includes:
referring to fig. 4c, the region other than the second region having the largest area in the fundus blood vessel network image after the erosion processing is subjected to filling processing to distinguish the second region having the largest area.
Referring to fig. 4c, the fundus angiomesh image after the erosion process may have noise points such as a small white area in fig. 4c, which may affect the subsequent identification or calculation. In this embodiment, by performing the filling processing on the region other than the second region having the largest area in the fundus blood vessel network image, the noise influence in the fundus blood vessel network image can be effectively reduced, thereby facilitating the identification or calculation of the second region having the largest area.
And S106, calculating the pixel number of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area.
In the embodiment of the application, the area of the fundus oculi perfusion-free area can be calculated by calculating the number of pixels of the fundus oculi perfusion-free area.
By implementing the embodiment of the application, the retina of the eye to be inspected is scanned through the OCTA, so that the fundus vascular network image is obtained, then the fundus vascular network image is subjected to clustering processing and corrosion processing, so that the fundus vascular network image comprising the first area and the at least one second area is obtained, after the second area with the largest area is found, the expansion processing is performed, the area reduced due to the corrosion processing can be effectively made up, so that the fundus non-perfusion area can be obtained, the number of scanning points for scanning the retina of the eye to be inspected through the OCTA can be reduced, the scanning with higher density is avoided, the efficiency of obtaining the fundus non-perfusion area can be improved, and the complete FAZ (foveal avascular zone) area can be obtained. In this case, the number of scanning times is reduced, that is, the number of scanning points is reduced.
For a more visual understanding of the method shown in fig. 1, referring to fig. 3, fig. 3 is a schematic flow chart of an image processing method provided in an embodiment of the present application. The image processing method of the embodiment of the application is specifically an OCTA blood vessel network image processing method. As shown in fig. 3, the image processing method includes:
s301, scanning the retina of the eye to be detected by using optical coherence tomography angiography to obtain a scanning signal of a scanning point, and directly carrying out zero processing on the gray value of the scanning point according to noise processing under the condition that the scanning signal of the scanning point is lower than a preset threshold value. In the case where the retina is scanned, a fundus blood vessel network image is obtained.
As shown in fig. 4a, fig. 4a is a schematic diagram of a fundus vascular network image provided in an embodiment of the present application, and the fundus vascular network image includes a fundus non-perfusion region, i.e., a central region in the diagram.
S302, performing interpolation processing, filtering processing and clustering processing by adopting a K-means (K-means) method on the blood vessel network image of the fundus.
It is understood that after the processing of step S302, a blood vessel region and a blood-free region may be formed on the fundus blood vessel mesh image.
As shown in fig. 4b, fig. 4b is a schematic diagram of a fundus blood vessel network image after clustering processing according to an embodiment of the present application. The fundus blood vessel network image after the clustering processing comprises a blood vessel area and a blood vessel free area, wherein the black part is the blood vessel area, and the white part is the blood vessel free area. Because the number of scanning points is too small, the black blood vessels in the image can be greatly lost, so that the avascular region and the vascular region can be connected together. In order to demarcate the avascular region, it may be as shown in the subsequent step.
And S303, carrying out corrosion treatment on the clustered fundus blood vessel network images.
After the corrosion treatment, a first region and at least one second region can be formed on the fundus blood vessel network image.
Fig. 4c is a schematic diagram of a fundus blood vessel network image after corrosion treatment according to an embodiment of the present application, as shown in fig. 4 c. Due to the fact that the corrosion treatment is carried out on the blood vessel network image of the ocular fundus, the area of the white area can be reduced due to corrosion, and therefore the connected areas are separated. Since there is a very small noise spot in the middle (e.g., a small area of white area in the figure), there is a black square after etching, but this does not affect the final result. Then, the largest white area in the fundus blood vessel network image is found and filled in as shown in S304.
S304, searching the largest white area (namely the fundus non-perfusion area) in the fundus blood vessel network image after the clustering processing, and filling the areas except the largest white area.
Fig. 4d is a schematic diagram of a fundus blood vessel network image after filling processing according to an embodiment of the present application, as shown in fig. 4 d.
S305, performing expansion processing on the fundus blood vessel network image to obtain a fundus non-perfusion area of the eye to be detected.
As shown in fig. 4e, fig. 4e is a schematic view of a fundus non-perfusion region according to an embodiment of the present application. From fig. 4e it can be seen that by the dilation process, a fundus non-perfused area (white area) is finally obtained. Wherein the expansion treatment and the erosion treatment select nuclei of the same size, thereby making up for areas of reduced erosion, so that the area of the obtained fundus non-perfusion area coincides or substantially coincides with the area of the original area.
And S306, calculating the pixel number of the fundus non-perfusion area to obtain the area of the fundus non-perfusion area.
By implementing the embodiment of the application, the overlong scanning time caused by scanning with larger density can be effectively avoided, the efficiency of acquiring the central perfusion-free area by the fundus perfusion-free area measuring device can be effectively improved, and the efficiency of acquiring the complete FAZ area is improved.
Referring to fig. 5, fig. 5 is a schematic flow chart of another fundus perfusion-free region measuring method provided in the embodiment of the present application, and the measuring method can also be applied to a fundus perfusion-free region measuring device. As shown in fig. 5, the method for measuring the fundus perfusion-free region includes:
s501, acquiring a fundus blood vessel network image of the eye to be detected, wherein the fundus blood vessel network image is generated by optical coherence tomography angiography.
And S502, clustering the fundus blood vessel network images, and forming a blood vessel region and a blood vessel-free region on the clustered fundus blood vessel network images.
S503, carrying out corrosion treatment on the clustered fundus blood vessel network image, wherein a first area and at least one second area are formed on the clustered fundus blood vessel network image, the first area comprises a blood vessel area, the at least one second area comprises a non-blood vessel area, the first area is a communicated integral area, and each second area is a communicated integral area.
S504, performing expansion processing on the corroded fundus blood vessel network image to obtain at least one third area, wherein each third area is a communicated integral area, each third area comprises a second area, and nuclei with the same size are used for the expansion processing and the corrosion processing.
And S505, searching a third area with the largest area in the fundus blood vessel network image after expansion processing, wherein the third area with the largest area is a fundus non-perfusion area of the eye to be detected.
Optionally, after finding the third region with the largest area in the fundus blood vessel network image after the dilation processing, the method further includes:
filling processing is performed on the region other than the third region with the largest area in the fundus blood vessel network image after the expansion processing to distinguish the third region with the largest area.
And S506, calculating the pixel number of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area.
It is understood that the specific implementation shown in fig. 5 may correspond to the implementation described with reference to fig. 2 or fig. 3, and is not described in detail here.
The method of the embodiments of the present application is set forth above in detail and the apparatus of the embodiments of the present application is provided below.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a fundus perfusion-free region measuring apparatus according to an embodiment of the present application, and an image processing apparatus may be used to perform the methods shown in fig. 2 to 5. As shown in fig. 6, the fundus perfusion-free region measuring apparatus includes:
an acquisition unit 601 for acquiring a fundus blood vessel network image of an eye to be examined, the fundus blood vessel network image being generated by optical coherence tomography angiography;
a clustering unit 602, configured to perform clustering processing on the fundus vascular network image, where a vascular region and an avascular region are formed on the fundus vascular network image after the clustering processing;
the erosion processing unit 603 is configured to perform erosion processing on the clustered fundus blood vessel network image, where a first region and at least one second region are formed on the clustered fundus blood vessel network image, the first region includes a blood vessel region, the at least one second region includes a non-blood vessel region, the first region is a connected whole region, and each second region is a connected whole region;
a searching unit 604, configured to search for a second region with a largest area in the fundus vascular network image after the corrosion processing;
an expansion processing unit 605, configured to perform expansion processing on the fundus vascular network image after the erosion processing to obtain a fundus non-perfusion area of the eye to be inspected, where the fundus non-perfusion area includes a second area with a largest area, and a nucleus with the same size is used for the expansion processing and the erosion processing;
a calculating unit 606 for calculating the number of pixels of the fundus non-perfusion region to calculate the area of the fundus non-perfusion region.
By implementing the embodiment of the application, the retina of the eye to be inspected is scanned through the OCTA, so that the fundus vascular network image is obtained, then the fundus vascular network image is subjected to clustering processing and corrosion processing, so that the fundus vascular network image comprising a first area and at least one second area is obtained, after the second area with the largest area is found, the expansion processing is performed, the area reduced due to the corrosion processing can be effectively made up, and then the fundus non-perfusion area can be obtained, the scanning with higher density is avoided, the efficiency of obtaining the fundus non-perfusion area is improved, and the complete FAZ area can be obtained.
Optionally, as shown in fig. 7, the fundus perfusion-free region measuring apparatus further includes:
and a filling processing unit 607 for performing filling processing on a region other than the second region having the largest area in the fundus blood vessel network image after the erosion processing to distinguish the second region having the largest area.
Alternatively, the acquiring unit 601 is specifically configured to acquire a fundus angiomesh image of the eye to be examined by an optical coherence tomography apparatus including a light source and an optical probe.
Optionally, the obtaining unit 601 is specifically configured to perform repeated scanning on the retina of the eye to be inspected by using the measuring light through the optical probe, where the measuring light is provided by the light source, and the repeated scanning includes at least two times of scanning the same position of the retina; acquiring at least one spectral domain interference signal associated with the retina during the scan; and extracting data relating to movement of cells, tissues or particles in the retina from the spectral domain interference signal; and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
Optionally, as shown in fig. 8, the clustering unit 602 includes:
a filtering processing subunit 6021 configured to perform filtering processing on the fundus blood vessel network image;
and the clustering processing subunit 6022 is configured to classify the filtered fundus blood vessel network images by using a K-means method.
Optionally, the filtering processing subunit 6021 is specifically configured to perform interpolation processing on the fundus blood vessel network image, and perform filtering processing on the fundus blood vessel network image after the interpolation processing.
It should be noted that the implementation of each unit may also correspond to the corresponding description of the method embodiments shown in fig. 1 to 5, and is not described in detail here.
Referring to fig. 9, fig. 9 is a schematic structural diagram of a fundus perfusion-free region measuring apparatus according to an embodiment of the present application, and an image processing apparatus may be used to perform the methods shown in fig. 2 to 5. As shown in fig. 9, the fundus perfusion-free region measuring apparatus includes:
an acquisition unit 901 for acquiring a fundus blood vessel network image of an eye to be examined, the fundus blood vessel network image being generated by optical coherence tomography angiography;
a clustering unit 902, configured to perform clustering on the fundus vascular network image, where a vascular region and an avascular region are formed on the fundus vascular network image after the clustering;
the corrosion processing unit 903 is configured to perform corrosion processing on the clustered fundus blood vessel network image, where a first region and at least one second region are formed on the clustered fundus blood vessel network image, the first region includes a blood vessel region, the at least one second region includes a non-blood vessel region, the first region is a connected whole region, and each second region is a connected whole region;
an expansion processing unit 904, configured to perform expansion processing on the fundus vascular network image after the erosion processing to obtain at least one third region, where each third region is a communicated whole region, each third region includes a second region, and a nucleus with the same size is used for the expansion processing and the erosion processing;
the searching unit 905 is used for searching a third region with the largest area in the fundus blood vessel network image after expansion processing, wherein the third region with the largest area is a fundus non-perfusion region of the eye to be detected;
a calculating unit 906 for calculating the number of pixels of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area.
Optionally, as shown in fig. 10, the fundus perfusion-free region measuring apparatus further includes:
a filling processing unit 907 for performing filling processing on a region other than the second region having the largest area in the fundus blood vessel network image after the inflation processing to distinguish the second region having the largest area.
Optionally, the acquiring unit 901 is specifically configured to acquire a fundus angiomesh image of the eye to be inspected by an optical coherence tomography apparatus including a light source and an optical probe.
Optionally, the obtaining unit 901 is specifically configured to perform repeated scanning on the retina of the eye to be inspected by using measuring light through the optical probe, where the measuring light is provided by the light source, and the repeated scanning includes at least two times of scanning the same position of the retina; acquiring at least one spectral domain interference signal associated with the retina during the scan; and extracting data relating to movement of cells, tissues or particles in the retina from the spectral domain interference signal; and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
Optionally, as shown in fig. 11, the clustering unit 902 includes:
a filtering processing subunit 9021, configured to perform filtering processing on the fundus blood vessel network image;
and the clustering processing subunit 9022 is configured to classify the filtered fundus blood vessel network images by using a K-means method.
Optionally, the filtering processing subunit 9021 is specifically configured to perform interpolation processing on the fundus blood vessel network image, and perform filtering processing on the fundus blood vessel network image after the interpolation processing.
It should be noted that the implementation of each unit may also correspond to the corresponding description of the method embodiments shown in fig. 1 to 5, and is not described in detail here.
According to the above, the existing OCTA vascular network scanning method obtains a better image by multiple times of scanning, the number of the scanning points is 304 × 304 or even more, the vessels surrounded by the obtained central non-vessel perfusion area are continuous, and the fundus non-perfusion area can be directly obtained. However, the number of points scanned by such a method is large, and the time required for scanning is long, so that the efficiency is low. Reducing the number of scans, such as a single scan or a number of scans, can reduce the time required for scanning, but the resulting blood vessel images are intermittent and non-perfused areas of the fundus cannot be obtained according to existing methods. In the embodiment of the application, the retina is scanned by using optical coherence tomography angiography, and for each scanning point, if the scanning signal of the point is lower than a reference threshold value, the noise processing is directly carried out and the gray value of the point is subjected to zero processing; clustering the processed images, wherein the clustering comprises interpolation processing, filtering processing, classification by adopting a K mean value method, and then corrosion processing; in the fundus angiocarpy image formed with a first area and at least one second area after corrosion treatment, searching for the second area with the largest area, then filling the area outside the second area with the largest area, and then performing expansion treatment by adopting a nucleus with the same size as the corrosion treatment to obtain a complete fundus non-perfusion area; or, performing expansion processing on the fundus vascular network image with the first area and the at least one second area formed after the corrosion processing by adopting a nucleus with the same size as the corrosion processing to form a fundus vascular network image with at least one third area, wherein each third area comprises one second area, searching the third area with the largest area, and performing filling processing on the area except the third area with the largest area to obtain a complete fundus non-perfusion area; in this way, when the number of scanning points is reduced, the fundus non-perfusion region is obtained from the image in which the capillary vessels are intermittent, and the time required for scanning can be reduced. In the embodiment of the present application, the number of scan points for scanning the retina of the eye to be examined by the OCTA is lower than 304, and a complete fundus non-perfusion region can still be obtained when the number of scan points is 256 × 256, so that the number of scan points can be set to 256 × 256 to 304 × 304. The obtained perfusion-free region of the fundus can be used for image recognition or for calculating the FAZ area.
Referring to fig. 12, fig. 12 is a schematic structural diagram of an fundus perfusion-free region measuring apparatus provided in an embodiment of the present application, and the fundus perfusion-free region measuring apparatus includes a processor 1201, a memory 1202, and an input/output interface 1203, where the processor 1201, the memory 1202, and the input/output interface 1203 are connected to each other through a bus.
The memory 1202 includes, but is not limited to, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a compact disk read-only memory (CD-ROM), and the memory 1202 is used for storing related instructions and data. In the embodiment of the present application, the memory may be used to store the reference threshold, and may also be used to store a degree parameter of the etching process, a degree parameter of the swelling process, and the like, which is not limited in the embodiment of the present application.
The input/output interface 1203 is used for inputting and/or outputting data. In the embodiment of the application, the input/output interface can be connected with a display, and the display can be used for displaying the fundus blood vessel network image.
The processor 1201 may be one or more Central Processing Units (CPUs), and in the case that the processor 1201 is one CPU, the CPU may be a single-core CPU or a multi-core CPU. Alternatively, the processor may be another type of processor (e.g., an image processor), and so forth. Alternatively, the processor may be a processor group including a plurality of processors, and the plurality of processors may be connected to each other by one or more buses.
In one embodiment, the processor may be configured to execute the method performed by the acquisition unit, the clustering unit, the erosion processing unit, the lookup unit, the dilation processing unit, and the calculation unit. And will not be described in detail herein.
In one embodiment, the fundus perfusion-free region measurement device may also be an optical coherence tomography device, the optical coherence tomography device may include a light source and an optical probe, and the optical coherence tomography device may further include a processor, a memory, and the like, which are not limited by the embodiments of the present application.
It should be noted that the implementation of the respective operations may also correspond to the corresponding description of the method embodiments shown in fig. 1 to 5.
It is understood that the structural schematic diagram of the fundus perfusion-free region measuring apparatus provided above for the embodiments of the present application is only provided, and in a specific implementation, the fundus perfusion-free region measuring apparatus may have more or less components than those shown, may combine two or more components, or may have different configuration implementations of different components, and so on.
One of ordinary skill in the art will appreciate that all or part of the processes in the methods of the embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include processes such as those of the embodiments of the methods. And the aforementioned storage medium includes: various media capable of storing program codes, such as ROM or RAM, magnetic or optical disks, etc.
Claims (10)
1. An OCTA blood vessel network image processing method is characterized by comprising the following steps:
acquiring a fundus vascular network image of an eye to be examined, the fundus vascular network image being generated by optical coherence tomography angiography;
clustering the fundus blood vessel network images, and forming a blood vessel area and a non-blood vessel area on the clustered fundus blood vessel network images;
performing corrosion treatment on the fundus blood vessel network image after the clustering treatment, wherein a first region and at least one second region are formed on the fundus blood vessel network image after the corrosion treatment, the first region comprises the blood vessel region, the at least one second region comprises the avascular region, the first region is a communicated integral region, and each second region is a communicated integral region;
after the corrosion treatment, the method also comprises the following steps:
searching a second region with the largest area in the fundus blood vessel network image after the corrosion treatment;
performing expansion processing on the fundus vascular network image subjected to corrosion processing to obtain a fundus non-perfusion area of the eye to be detected, wherein the fundus non-perfusion area comprises a second area with the largest area, and the expansion processing and the corrosion processing adopt a nucleus with the same size;
or, after the etching treatment, the method further comprises:
performing expansion processing on the fundus vascular network image subjected to the corrosion processing to obtain at least one third area, wherein each third area is a communicated integral area, each third area comprises one second area, and the expansion processing and the corrosion processing adopt kernels with the same size;
and searching a third region with the largest area in the fundus blood vessel network image after expansion processing, wherein the third region with the largest area is a fundus non-perfusion region of the eye to be detected.
2. The method of claim 1,
after searching for the second region with the largest area in the fundus blood vessel network image after the corrosion treatment and before performing the expansion treatment on the fundus blood vessel network image after the corrosion treatment, the method further includes:
filling the area except the second area with the largest area in the fundus blood vessel network image after the corrosion treatment so as to distinguish the second area with the largest area;
or after the searching for the third region with the largest area in the fundus blood vessel network image after the expansion processing, the method further comprises:
and performing filling processing on a region other than the third region with the largest area in the fundus blood vessel network image after the expansion processing to distinguish the third region with the largest area.
3. The method according to claim 1 or 2, wherein said acquiring a fundus angiomesh image of the eye to be examined comprises:
acquiring a fundus blood vessel network image of the eye to be inspected by an optical coherence tomography device comprising a light source and an optical probe;
the acquiring of the fundus angiomesh image of the eye to be examined by an optical coherence tomography apparatus including a light source and an optical probe includes:
repeatedly scanning, by the optical probe, a retina of the eye to be examined with measurement light, wherein the measurement light is provided by the light source, the repeated scanning comprising at least two scans of a same location of the retina;
acquiring at least one spectral domain interference signal associated with the retina during a scan;
extracting data relating to cell, tissue or particle motion in the retina from the spectral domain interference signal;
and calculating the fundus blood vessel network image according to the data related to the movement of the cells, tissues or particles in the retina.
4. The method according to claim 1, wherein the clustering the fundus vascular network image comprises:
and carrying out filtering processing on the fundus blood vessel network images, and classifying the fundus blood vessel network images after the filtering processing by adopting a K mean value method.
5. The method according to claim 4, wherein the filtering the fundus blood vessel network image includes:
and carrying out interpolation processing on the fundus blood vessel network image, and carrying out filtering processing on the fundus blood vessel network image after the interpolation processing.
6. A method of measuring an area of a fundus nonperfusion zone of an eye to be examined, comprising the method according to any one of claims 1 to 5, further comprising: and calculating the pixel number of the fundus non-perfusion area to calculate the area of the fundus non-perfusion area.
7. An apparatus for measuring the perfusion-free zone of the fundus of an eye to be examined, the apparatus comprising:
an acquisition unit configured to acquire a fundus blood vessel network image of the eye to be inspected, the fundus blood vessel network image being generated by optical coherence tomography angiography;
the clustering unit is used for clustering the fundus blood vessel network images, and blood vessel regions and avascular regions are formed on the fundus blood vessel network images after clustering;
the corrosion processing unit is used for carrying out corrosion processing on the fundus blood vessel network image after the clustering processing, a first area and at least one second area are formed on the fundus blood vessel network image after the corrosion processing, the first area comprises the blood vessel area, the at least one second area comprises the avascular area, the first area is a communicated integral area, and each second area is a communicated integral area;
the searching unit is used for searching a second region with the largest area in the fundus blood vessel network image after the corrosion treatment;
the expansion processing unit is used for performing expansion processing on the fundus vascular network image subjected to the corrosion processing to obtain a fundus non-perfusion area of the eye to be detected, the fundus non-perfusion area comprises a second area with the largest area, and the expansion processing and the corrosion processing adopt a kernel with the same size;
and the calculation unit is used for calculating the pixel number of the fundus non-perfusion area so as to calculate the area of the fundus non-perfusion area.
8. An apparatus for measuring the perfusion-free zone of the fundus of an eye to be examined, the apparatus comprising:
an acquisition unit configured to acquire a fundus blood vessel network image of the eye to be inspected, the fundus blood vessel network image being generated by optical coherence tomography angiography;
the clustering unit is used for clustering the fundus blood vessel network images, and blood vessel regions and avascular regions are formed on the fundus blood vessel network images after clustering;
the corrosion processing unit is used for carrying out corrosion processing on the fundus blood vessel network image after the clustering processing, a first area and at least one second area are formed on the fundus blood vessel network image after the corrosion processing, the first area comprises the blood vessel area, the at least one second area comprises the avascular area, the first area is a communicated integral area, and each second area is a communicated integral area;
the expansion processing unit is used for performing expansion processing on the fundus blood vessel network image subjected to the corrosion processing to obtain at least one third area, each third area is a communicated integral area, each third area comprises one second area, and the expansion processing and the corrosion processing adopt kernels with the same size;
the searching unit is used for searching a third area with the largest area in the fundus blood vessel network image after expansion processing, wherein the third area with the largest area is a fundus non-perfusion area of the eye to be detected;
and the calculation unit is used for calculating the pixel number of the fundus non-perfusion area so as to calculate the area of the fundus non-perfusion area.
9. An ocular fundus perfusion-free zone measurement apparatus comprising a processor and a memory, the memory storing program instructions which, when executed by the processor, cause an image processing apparatus to perform the method of any one of claims 1 to 6.
10. A computer-readable storage medium, in which program instructions are stored, which program instructions, when executed by a processor of a computer, cause the processor to carry out the method according to any one of claims 1-6.
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