Method for acquiring maculopathy edema features in retinal images, electronic device

Abstract

The embodiment of the present application provides a maculopathy edema feature acquisition method in a retinal image and an electronic device, and relates to the technical field of image recognition. The maculopathy edema feature acquisition method in a retinal image comprises the following steps: acquiring a current retinal image to be recognized in a plurality of retinal images; determining an edema brightness threshold of the current retinal image based on the brightness value of the pixels in the vitreous body region of the current retinal image; and determining an edema region in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, wherein the brightness value of the pixels in the edema region is less than the edema brightness threshold. The present application can extract the macular edema region in the image based on the brightness value of the pixels in the retinal image.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and in particular to a method and electronic device for acquiring macular lesion edema features in retinal images. Background Art

[0002] The retina is a thin layer of cells at the back of the eye. It is composed of photoreceptors (photoreceptors) and pigment epithelial cells. When stimulated by light, the retina converts light signals into neural signals, which are transmitted to the brain, allowing us to see. The retina is a very sensitive, shallow, and complex structure composed of photoreceptors, bipolar cells, and ganglion cells. Not only can ophthalmic diseases be reflected in the state of the retina, but some metabolic-related physical and mental illnesses, such as diabetic retinopathy, can also cause changes in the retina.

[0003] The macula is a crucial area of ​​the retina that plays a vital role in vision. Due to its large number of cone cells, the macula is the site of the sharpest vision, and the health of the macula directly affects vision. Macular degeneration is a common retinal disease, including age-related macular degeneration and macular edema. These diseases can severely damage central vision and affect patients' daily lives. For macular diseases, ophthalmologists need to use advanced examination equipment and techniques, such as optical coherence tomography (OCT) and fundus fluorescein angiography, to achieve early diagnosis and precise treatment of the disease.

[0004] The current diagnosis of macular diseases is basically performed by trained radiologists who perform eye examinations. From the images obtained, experienced ophthalmologists read the collected images to draw a conclusion on whether the patient has macular disease. However, the serious shortage of ophthalmic clinicians and radiologists is far from meeting clinical needs. The experience accumulation and film reading ability of young ophthalmologists also require long-term training and cultivation. Through manual reading, images with pathological tissues are selected from large data sets, and diagnostic conclusions are drawn from subtle tissue lesions in the images. This traditional method of diagnosing diseases cannot serve daily medical needs in a large-scale, precise and personalized manner. It cannot accurately identify, automatically analyze, and quantitatively track and evaluate the lesion site. The goal of future precision medicine cannot be achieved.

[0005] At the same time, the rapid development of OCT technology has led to its widespread clinical application, resulting in a massive and rapid accumulation of OCT image data. With each technological iteration, OCT image quality, imaging range, and resolution have significantly improved. However, existing diagnostic and analysis software for OCT images can only roughly assess central macular thickness and cannot accurately and accurately identify retinal layers. This results in poor accuracy in central macular thickness assessment. Furthermore, these software cannot accurately identify lesions, precisely define the affected tissue area, or quantitatively measure important indicators such as edema area and subretinal fluid area. Furthermore, these software cannot objectively monitor and record changes in lesion size during patient follow-up, let alone obtain effective data to support or predict prognosis through patient follow-up and treatment. This current dilemma not only hinders future research in macular diseases but also hinders the objective and quantitative analysis of patient conditions, hindering the transition from qualitative to quantitative analysis, and ultimately hindering the development of precision medicine.

[0006] Therefore, if macular degeneration can be intelligently identified and quantitatively analyzed, and if an effective computer-assisted image analysis system can be used to conveniently process the collected OCT images and provide clinicians with quantitative and objective measurements, it will be of great help to doctors in making clinical decisions, predicting disease prognosis, analyzing disease outcomes through big data, and formulating accurate and personalized treatment plans for patients.

[0007] However, the accuracy of the diagnostic analysis software currently available in clinical practice for OCT images is often limited by the turbidity of the refractive medium and the technician's ability to capture images, and is unable to effectively provide objective quantitative results of lesions. In addition, the description and quantification of changes in the lesion area based on central thickness cannot be completely consistent and cannot fully explain the nature of the changes in the lesion.

[0008] Thanks to the rapid development of artificial intelligence (AI), it is now possible to automatically identify and label lesions by inputting a large number of manually labeled OCT images. This can then be used to collect quantitative data on these lesions. Commonly used macular disease recognition systems rely on machine learning models, which are trained using a sufficient number of labeled OCT images to achieve a high theoretical accuracy.

[0009] But this is only in theory, and there are many problems in realizing it: for example, machine learning training requires a large number of labeled OCT images, but the labeling of OCT images requires specially trained ophthalmologists to perform the operation, and professional ophthalmologists often do not have a lot of time to focus on the labeling of OCT images. In this case, it is very difficult and time-consuming to obtain high-precision labeled data, and it requires excessively high labor costs, which is one of the barriers to using artificial intelligence technology for OCT images. In addition, the lesions of different diseases are different, so they need to be trained separately. If the same model is used to identify lesions for all diseases, the accuracy of lesion identification will be too poor, which undoubtedly further increases the difficulty of artificial intelligence for OCT image recognition. In addition, machine learning models have high requirements for training and testing data. If the model encounters special cases or the OCT shooting machine is replaced during use, the robustness and accuracy of the trained model will also be reduced.

[0010] The above-mentioned problems show the difficulty of applying artificial intelligence technology to OCT image recognition. In addition, even if artificial intelligence technology is applied to OCT image recognition, it is still unknown whether it can achieve the desired recognition effect. Summary of the Invention

[0011] The purpose of the present invention is to provide a method for acquiring edema features of macular lesions in retinal images, an electronic device and a storage medium. The method can determine the edema brightness threshold of the retinal image based on the brightness values ​​of pixels in the vitreous area of ​​the retinal image, and then perform a binary judgment of edema and noise on the pixels in the retinal image based on the edema brightness threshold. It can remove non-edema noise in the retinal image and realize feature extraction of macular edema in the retinal image; that is, based on the pixel-level information of the retinal image, the feature extraction of macular edema can be performed automatically, and the macular edema area of ​​the retinal image can be obtained by using only a computer through automatic encoding and compilation technology. Compared with macular edema recognition through machine learning, it does not require training through data or high-performance hardware, and is simpler and easier to use; it achieves a higher signal-to-noise ratio, has the characteristics of high accuracy, fast recognition speed and low hardware requirements, and is suitable for a variety of macular edema feature extraction scenarios.

[0012] To achieve the above-mentioned objectives, the present invention provides a method for acquiring edema features of macular degeneration in retinal images, comprising: acquiring a current retinal image to be identified from multiple retinal images; determining an edema brightness threshold of the current retinal image based on the brightness values ​​of pixels in the vitreous area of ​​the current retinal image; determining an edema area in the current retinal image based on the brightness values ​​of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, wherein the brightness values ​​of the pixels in the edema area are less than the edema brightness threshold.

[0013] The present invention also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for acquiring macular lesion edema features in retinal images as described above.

[0014] The present invention also provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transient storage medium, on which a computer program is stored, and is characterized in that when the computer program is run by a processor, it executes the method for obtaining macular edema characteristics in retinal images as described above.

[0015] In one embodiment, the method further comprises:

[0016] performing a plurality of preprocessing operations on the current retinal image to obtain a plurality of reference retinal images that have undergone the plurality of preprocessing operations;

[0017] determining an edema area in each of the reference retinal images based on a brightness value of each pixel in each of the reference retinal images and an edema brightness threshold of each of the reference retinal images;

[0018] Based on the edema area of ​​each reference retinal image and the edema area of ​​the current retinal image and preset weight parameters, the effective edema area in the current retinal image is determined, and the preset weight parameters include: the weights of each reference retinal image and the current retinal image.

[0019] In one embodiment, before determining the edema area in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the method further includes:

[0020] Identifying an RPE layer of the current retinal image based on a brightness value of each pixel in the current retinal image;

[0021] Determining an edema area in the current retinal image based on a brightness value of each pixel in the current retinal image and an edema brightness threshold of the current retinal image includes:

[0022] Based on the RPE layer of the current retinal image, the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the edema area in the current retinal image is determined, and all pixels in the edema area are located above the RPE layer.

[0023] In one embodiment, determining the edema brightness threshold of the current retinal image based on the brightness values ​​of pixels within the vitreous region of the current retinal image includes:

[0024] Obtaining an initial brightness threshold value of a brightness value of pixels greater than a preset percentage of all pixels in the vitreous body region, wherein the preset percentage is greater than 50%;

[0025] Searching for target pixels in the vitreous region whose brightness values ​​are within a set brightness value range, and counting the number of target pixels having each target brightness value based on the target brightness value of each target pixel; wherein the set brightness value range is a brightness value range with the initial brightness threshold as the midpoint;

[0026] Searching for a target brightness value having the smallest target pixel number difference from the initial brightness threshold among all the target brightness values ​​as a minimum change brightness threshold;

[0027] The edema brightness threshold is determined based on the initial brightness threshold and the minimum change brightness threshold.

[0028] In one embodiment, determining the edema brightness threshold based on the initial brightness threshold and the minimum change brightness threshold includes:

[0029] The product of the average value between the initial brightness threshold and the minimum change brightness threshold and a preset correction coefficient is obtained as the edema brightness threshold, and the preset correction coefficient is greater than 0.5.

[0030] In one embodiment, determining the edema area in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image includes:

[0031] In the closed area formed by pixels in the current retinal image whose brightness value is less than the edema brightness threshold, a closed area whose area meets a preset condition is selected as an edema area;

[0032] The preset condition is that the area of ​​the closed area is smaller than the product of the area of ​​the effective area in the current retinal image and a set threshold and is larger than the area of ​​a preset number of pixels, and the set threshold is greater than 0 and less than 0.5.

[0033] In one embodiment, after determining the edema area in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the method further includes:

[0034] For each edema region, the area of ​​the edema region is determined based on the pixel spacing information of the current retinal image and the number of pixels in the edema region.

[0035] In one embodiment, after determining the area of ​​each edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region, the method further includes:

[0036] The volume of each edema region in the current retinal image is obtained based on the area of ​​each edema region in the current retinal image and the image distance information of the current retinal image.

[0037] In one embodiment, before determining the area of ​​each edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region, the method further includes:

[0038] Performing etdrs partitioning on the current retinal image to obtain the edema area within the etdrs partition;

[0039] For each edema region, determining the area of ​​the edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region includes:

[0040] For each of the edema regions within the etdrs partition, the area of ​​the edema region is determined based on the pixel spacing information of the current retinal image and the number of pixels within the edema region.

[0041] In one embodiment, identifying the RPE layer of the current retinal image based on the brightness value of each pixel in the current retinal image includes:

[0042] Searching for a designated pixel with the highest brightness in a valid area of ​​the current retinal image by column, and determining, for a designated pixel in a current column within the valid area of ​​the current retinal image found to be in the current column, the vertical axis position of the designated pixel in the current column based on the vertical axis position of the designated pixel in the previous column adjacent to the current column if a vertical axis position difference between the designated pixel in the current column and the designated pixel in the previous column adjacent to the current column is greater than a first difference threshold;

[0043] The RPE layer of the current retinal image is obtained by combining the designated pixels in each column within the effective area of ​​the current retinal image.

[0044] In one embodiment, searching for target pixels with the highest brightness in columns within the valid area of ​​the current retinal image includes:

[0045] Starting from any middle column in the effective area of ​​the current retinal image, search leftward and rightward respectively for the target pixel with the highest brightness in each column.

[0046] In one embodiment, after obtaining the RPE layer of the current retinal image by combining the designated pixels in each column within the effective area of ​​the current retinal image, the method further includes:

[0047] Acquire multiple designated retinal images associated with the current retinal image, and determine a pixel reference height of the RPE layer based on an average height of pixels included in the RPE layer of the multiple designated retinal images;

[0048] If the difference between the average pixel height of the pixels included in the RPE layer of the current retinal image and the pixel reference height is greater than a second difference threshold, the RPE layer of the current retinal image is determined based on the longitudinal axis position of the pixels included in the RPE layer of the specified retinal image adjacent to the current retinal image.

[0049] In one embodiment, the vitreous body area of ​​the current retinal image is determined as follows:

[0050] The effective area of ​​the current retinal image is sampled in sequence using a sampling frame of a set size, and if the average brightness value of the pixels in the sampling area currently selected by the sampling frame is within a preset brightness range, it is determined that the sampling area currently selected by the sampling frame is the vitreous area. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a schematic diagram of a method for acquiring macular edema features in a retinal image according to a first embodiment of the present invention;

[0052] Figure 2 is a schematic diagram of an original retinal image according to the first embodiment of the present invention;

[0053] Figure 3 yes Figure 2 Schematic diagram of the layered structure of the mid-retinal image;

[0054] Figure 4 yes Figure 1 A specific flow chart of step 102 of the method for acquiring macular edema features in a retinal image;

[0055] Figure 5 is a schematic diagram of an edema area identified in an original retinal image according to the method for acquiring macular lesion edema features in a retinal image according to the first embodiment of the present invention;

[0056] Figure 6 is a schematic diagram of the current retinal image partitioned by etdrs according to the first embodiment of the present invention;

[0057] Figure 7is a schematic diagram of a method for acquiring macular edema features in a retinal image according to a second embodiment of the present invention;

[0058] Figure 8 is a schematic diagram of a current retinal image before and after non-local mean processing and sharpening processing according to a second embodiment of the present invention;

[0059] Figure 9 is a schematic diagram of the current retinal image before and after automatic contrast adjustment processing according to the second embodiment of the present invention;

[0060] Figure 10 is a schematic diagram of the current retinal image before and after sharpening processing according to the second embodiment of the present invention;

[0061] Figure 11 is a schematic diagram of a current retinal image before and after being processed by the Otsu method according to a second embodiment of the present invention;

[0062] Figure 12 is a schematic diagram of a method for acquiring macular edema features in a retinal image according to a third embodiment of the present invention;

[0063] Figure 13 is a schematic diagram of the portion below the RPE layer in the current retinal image before and after being adjusted to black according to the third embodiment of the present invention;

[0064] Figure 14 3 is a schematic diagram of an edema area identified in a current retinal image in which the portion below the RPE layer is adjusted to black according to the method for acquiring macular lesion edema features in a retinal image in the third embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will describe in detail various embodiments of the present invention in conjunction with the accompanying drawings to provide a clearer understanding of the objectives, features and advantages of the present invention. It should be understood that the embodiments shown in the accompanying drawings are not intended to limit the scope of the present invention, but are only intended to illustrate the essential spirit of the technical solution of the present invention.

[0066] In the following description, for the purpose of illustrating the various disclosed embodiments, certain specific details are set forth in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known devices, structures, and techniques associated with this application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0067] Unless the context requires otherwise, throughout the specification and claims, the word "comprise" and variations such as "include" and "have" should be construed in an open, inclusive sense, that is, should be interpreted to mean "including, but not limited to."

[0068] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0069] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be noted that the term "or" is generally employed in its sense including "or / and" unless the context clearly dictates otherwise.

[0070] In the following description, in order to clearly show the structure and working mode of the present invention, many directional words will be used for description, but words such as "front", "back", "left", "right", "outside", "inside", "outward", "inward", "up", and "down" should be understood as convenient terms and should not be understood as restrictive terms.

[0071] The first embodiment of the present invention relates to a method for acquiring macular edema features in retinal images, which is applied to electronic devices. The electronic devices can be common computer devices, such as desktop hosts, laptop computers, etc., or mobile phones.

[0072] like Figure 1 FIG. 1 is a specific flow chart of the method for acquiring macular edema features in retinal images according to this embodiment.

[0073] Step 101: Acquire a current retinal image to be identified from a plurality of retinal images.

[0074] Specifically, the retinal image is an OCT image of the user's fundus collected by an OTC device (i.e., an optical coherence tomography scanner). The computer device can be directly connected to the OCT device to obtain a DICOM format file sent by the OCT device, or it can be connected to other intermediate data collection devices or storage devices to obtain a DICOM format file collected by the OCT device; the DICOM format file includes the retinal OCT image and various other information related to the retinal OCT image, such as: shooting date, serial number, patient ID, device serial number, shooting date, pixel spacing, image spacing, etc. The computer device can parse the acquired DICOM format file through DICOM file recognition software to obtain the retinal image and the information of each retinal image. The retinal image can be stored in a common format (such as JPG, PNG, etc.) to facilitate subsequent image processing; the information of each retinal image is converted into elements that can be accessed through the tag name. Such as Figure 2 The following is a retinal OCT image extracted from a DICOM format file, which is the original retinal image. Figure 3 , is a schematic diagram of the layered structure of the eye in a retinal image, from top to bottom: ILM (Internal Limiting Membrane), RNFL (Retinal Nerve Fiber Layer), GCL (Ganglion Cell Layer), IPL (Inner Plexiform Layer), INL (Inner Nuclear Layer), OPL (Outer Plexiform Layer), ONL (Outer Nuclear Layer), ELM (External Limiting Membrane), PR (Photoreceptor Layers), RPE (Retinal Pigment Epithelium), BM (Bruch's Membrane), CC (Choriocapillaris), CS (Choroidal Stroma).

[0075] Thus, the multiple retinal images acquired by the computer device are obtained by slicing a single three-dimensional image of the user. Image recognition is performed on these multiple retinal images in sequence. After each retinal image recognition is completed, the next retinal image to be identified (i.e., the next image obtained by slicing) is determined, i.e., the current retinal image. The brightness value of each pixel in the current retinal image can be read first. Among the multiple retinal images obtained by slicing the single three-dimensional image of the user, the image spacing between any two adjacent retinal images is also known. This image spacing can be fixed or non-fixed, i.e., the image spacing between any two adjacent retinal images can be the same or different.

[0076] Step 102 : determining an edema brightness threshold of the current retinal image based on the brightness values ​​of pixels in the vitreous region of the current retinal image.

[0077] Specifically, based on the similarity between the brightness of the macular edema area and the brightness of the vitreous area in the retina, the brightness threshold of the macular edema area can be determined with reference to the brightness of the vitreous area in the retina, which is recorded as the edema brightness threshold.

[0078] The vitreous region in the current retinal image is the region on the ILM layer. The specific method for determining the vitreous region in the current retinal image is as follows:

[0079] First, the effective area of ​​the current retinal image is obtained to remove the black or white border at the edge of the current retinal image caused by exceeding the imaging area during OCT shooting.

[0080] The specific process is to search for the black or white border that exceeds the imaging area row by row and column by column starting from the edge of the current retinal image to obtain the effective area of ​​the current retinal image; taking the upper edge of the current retinal image as an example, starting from the first row of the upper edge, calculate the average brightness of all pixels contained in the row. If the average brightness value of the row is greater than 50 or less than 5, then the row is determined to be a black or white border that exceeds the imaging area, and continue to determine whether the average brightness of all pixels in the second row is greater than 50 or less than 5, until reaching the row where the average brightness of the pixels is not greater than 50 or less than 5. Similarly, the rows and / or columns where the four edge portions of the current retinal image exceed the imaging area (i.e., the effective area) can be determined. These rows and columns form the border of the effective area, and the image within the border is the effective area of ​​the current retinal image.

[0081] Then, the vitreous area is determined in the effective area of ​​the current retinal image. The determination method is: use a sampling frame of a set size to sample the effective area of ​​the current retinal image in sequence, and if the average brightness value of the pixels in the sampling area currently selected by the sampling frame is within the preset brightness range, then the sampling area currently selected by the sampling frame is determined to be the vitreous area; the sampling frame can be set as needed, generally a square or rectangular frame, for example, a square frame with a side length of 50 pixels. The size of the sampling frame should not be too large to avoid collecting white pixels in non-vitreous areas, which affects the identification of the vitreous area.

[0082] The specific process is as follows: the sampling frame traverses the valid area of ​​the current retinal image from right to left and from top to bottom. At each sampling, the sampling frame selects a sampling area within the valid area of ​​the current retinal image. The average brightness value of all pixels in the sampling area is then calculated. If the average brightness value is within the preset brightness range, the sampling area is determined to be the vitreous area. If the average brightness value of the sampling area is outside the preset brightness range, the sampling area is determined to be not the vitreous area. The sampling is then moved to the next position for sampling until a vitreous area is determined. The preset brightness range is, for example, 30-40.

[0083] It should be noted that the vitreous region determined in this embodiment is only a portion of the vitreous region, not the complete vitreous region.

[0084] Then, the edema brightness threshold of the current retinal image can be determined based on the brightness value of the pixels in the vitreous area of ​​the current retinal image. Please refer to Figure 4 , step 102 specifically includes the following sub-steps:

[0085] Sub-step 1021 , obtaining an initial brightness threshold of the brightness values ​​of pixels greater than a preset percentage of all pixels in the vitreous body area, where the preset percentage is greater than 50%.

[0086] Sub-step 1022, searching for target pixels in the vitreous area whose brightness values ​​are within a set brightness value range, and counting the number of target pixels with each target brightness value based on the target brightness value of each target pixel; wherein the set brightness value range is a brightness value range with the initial brightness threshold as the midpoint.

[0087] Sub-step 1023 , searching among all target brightness values ​​for a target brightness value having the smallest difference in the number of target pixels between the target brightness value and the initial brightness threshold as the minimum change brightness threshold.

[0088] Sub-step 1024 : determining an edema brightness threshold based on the initial brightness threshold and the minimum change brightness threshold.

[0089] Specifically, in the aforementioned step 102, a vitreous body area is determined. All pixels in the vitreous body area can be arranged in ascending order of brightness, and the brightness value that can cover a preset percentage of pixels in the vitreous body area is set as the initial brightness threshold; if the preset percentage is 95%, for example, the determined initial brightness threshold is greater than the brightness values ​​of 95% of the pixels in the vitreous body area.

[0090] A set brightness value range is obtained with the initial brightness threshold as the center. For example, the brightness range of the initial brightness threshold plus or minus 10 brightness is used as the set brightness value range; for example, if the initial brightness threshold is 35, the set brightness value range is (25, 45).

[0091] Then, among all the pixels in the vitreous area, pixels whose brightness values ​​are within the set brightness value range are searched and recorded as target pixels. Each target pixel has a corresponding brightness value recorded as the target brightness value, and then the number of target pixels corresponding to each target brightness value is counted; the initial brightness threshold also has its corresponding number of target pixels, and then the difference between the number of target pixels of the initial brightness threshold and the number of target pixels of each target brightness value is calculated respectively, and the target brightness value corresponding to the minimum difference is selected as the minimum change brightness threshold.

[0092] Then, the product of the average value between the initial brightness threshold and the minimum change brightness threshold and the preset correction coefficient is obtained as the edema brightness threshold. The preset correction coefficient is greater than 0.5 to reduce excessive identification of subsequent edema areas; for example, the preset correction coefficient is 0.8, the edema brightness threshold P = 0.8×(L1+L2) / 2, L1 represents the initial brightness threshold, L2 represents the minimum change brightness threshold, and the edema brightness threshold obtained by setting the preset correction coefficient to 0.8 can improve the accuracy of the subsequently identified edema areas to a certain extent.

[0093] Step 103 : determining an edema area in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, wherein the brightness value of the pixels in the edema area is less than the edema brightness threshold.

[0094] Specifically, based on the edema brightness threshold, the position of the characteristic pixels with a brightness value less than the edema brightness threshold is searched in the current retinal image, and then the area composed of the found characteristic pixels is used as the edema area; that is, based on the edema brightness threshold as the boundary, the current retinal image is binary judged to distinguish the characteristic pixels of macular edema from the noise pixels of non-macular edema in the current retinal image, thereby realizing the feature extraction of the edema area of ​​the current retinal image. The feature extraction here can be understood as the determination of pixels belonging to the edema area; for example, the pixels in the current retinal image are binarized based on the edema brightness threshold as the boundary, and the pixels with a brightness value greater than the edema brightness threshold are the background area and are assigned a value of 0; the pixels with a brightness value less than or equal to the edema brightness threshold are the possible edema area and are assigned a value of 1; and a binary image of the current retinal image can be obtained. Exemplarily, when the feature extraction of the edema area of ​​the current retinal image is performed, it can also be performed only on the valid area in the current retinal image.

[0095] The positions of pixels mentioned in this embodiment and subsequent embodiments are the coordinates of the pixels in the image. A coordinate system is constructed with the pixel in the lower left corner of the image as the origin, the bottom row of pixels as the X-axis, and the leftmost column of pixels as the Y-axis. The positions of all pixels in the image can be coordinated.

[0096] In one example, based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the edema area in the current retinal image is determined, including: in the closed area composed of pixels in the current retinal image whose brightness value is less than the edema brightness threshold, the closed area whose area meets the preset conditions is selected as the edema area; the preset condition is that the area of ​​the closed area is less than the product of the effective area of ​​the current retinal image and the set threshold and is greater than the preset pixel number area, the set threshold is greater than 0 and less than 0.5, for example, 0.2, and the preset pixel number area is the area of ​​a certain number of pixels, such as the area of ​​two pixels; thereby, the misidentification of the vitreous area can be reduced and noise reduction can be achieved. Please refer to Figure 5 , the red pixels marked in the current retinal image are the identified edema areas.

[0097] In one example, after step 103, the method further includes:

[0098] Step 104 : For each edema region, the area of ​​the edema region is determined based on the pixel spacing information of the current retinal image and the number of pixels in the edema region.

[0099] Specifically, after determining the edema areas within the current retinal image, the number of pixels within each edema area is counted. Based on the pixel spacing information of the current retinal image, the area of ​​a single pixel in the current retinal image can be calculated. The area of ​​each edema area is then multiplied by the number of pixels within each edema area by the area of ​​the single pixel to obtain the area of ​​each edema area. Thus, the areas of all edema areas are added together to obtain the total area of ​​the edema areas in the current retinal image.

[0100] Among them, the pixel spacing information of the current retinal image can be extracted from the DICOM file of the current retinal image. The pixel spacing information represents the length of each pixel in the actual space, including the two pixel spacings (x1, y1) of the x-axis and the y-axis. x1 can be equal to y1, indicating a square pixel, and x1 can also be equal to y1, indicating a rectangular pixel; the pixel spacing is, for example, (0.5, 0.5) mm, and a certain edema area includes K pixels, then the area of ​​the edema area is 0.25K, in square millimeters.

[0101] Step 105 : Obtaining the volume of each edema region in the current retinal image based on the area of ​​each edema region in the current retinal image and the image distance information between the current retinal image and adjacent retinal images.

[0102] Specifically, image spacing information can be extracted from the DICOM file of the current retinal image. The image spacing information represents the distance between the current retinal image and the adjacent previous retinal image slice. The area of ​​each edema region on the current retinal image has been obtained in step 104. For each edema region, the area of ​​the edema region is integrated using the above-mentioned image spacing information to obtain the volume of the edema region. The integration method is, for example, the Simpson integration method. Repeating the above process can obtain the volume of all edema regions on the current retinal image relative to the previous retinal image.

[0103] By repeating the above process, the volume of the edema area of ​​the second to last retinal image relative to the corresponding previous retinal image can be obtained. The volume of all edema areas is added together to obtain the macular edema volume of the current user; that is, the area of ​​each edema area can be integrated separately with the help of image spacing information, so that the calculated edema volume has a higher accuracy.

[0104] Furthermore, after determining the edema areas in all retinal images, etdrs partitioning is first performed on the current retinal image to obtain the edema area within the etdrs partition. For example, multiple circles are made with the fovea centralis as the center to further segment the edema area. Specifically, the fovea centralis is used as the center of the circle with diameters of 1mm, 3mm, and 6mm, and then the circle with a diameter of 6mm is divided into four equal parts. In this way, the edema area can be divided into fan ring areas with diameters of 1mm, 1-3mm, and 3-6mm. Furthermore, the quartering line can be extended to divide the area outside the 6mm diameter circle into four areas, and the edema in these areas is also included in the subsequent edema area calculation. Figure 6 For example, area 0 is a circle with a diameter of 1 mm, areas 0 to 4 form a circle with a diameter of 3 mm, areas 0 to 8 form a circle with a diameter of 6 mm, and the rectangular frame is the extension area of ​​the circle with a diameter of 6 mm, which is a 6 mm × 6 mm square. Areas 9-12 in the square will also be included in the subsequent edema area calculation; in some examples, the edema area mark outside the circle with a diameter of 6 mm can also be deleted.

[0105] Subsequent calculations of the area and volume of the edema area are performed on the ETDRS partition (circular area) and the edema area within the extended area (6mm×6mm square). If the edema area marker outside the circle is removed, the area and volume calculations can be performed only on the edema area within the ETDRS partition (circular area). ETDRS partitioning is a macular zoning method defined by the Early Treatment of Diabetic Retinopathy Research Institute. ETDRS partitioning of retinal images can identify clinically significant edema areas. Therefore, calculating the area and volume of the edema area within the ETDRS partition can make the calculated edema area and volume more clinically significant.

[0106] In steps 104 to 105, after determining all edema areas in the current retinal image, the area and volume of the edema area in the current retinal image can be calculated in combination with the pixel spacing information and image spacing information of the current retinal image, that is, the characteristics of the edema area in the retinal image are quantified, thereby achieving quantitative analysis of the edema area and providing accurate quantitative analysis results for reference.

[0107] After calculating the edema area and volume of the current retinal image, the annotation information of the current retinal image can be further enriched, and an OCT image group with the final edema area marked on the current retinal image can be output, including the shooting date, serial number, patient ID, equipment serial number, shooting date, edema area array, edema volume file, the original OCT image of the current retinal image, and the OCT image with the final edema area marked.

[0108] In this embodiment, the edema brightness threshold of the retinal image can be determined based on the brightness values ​​of the pixels in the vitreous area of ​​the retinal image, and then the pixels in the retinal image can be binary judged as edema and noise based on the edema brightness threshold, which can remove non-edema noise in the retinal image and realize feature extraction of macular edema in the retinal image; that is, based on the pixel-level information of the retinal image, the feature extraction of macular edema can be performed automatically, and the macular edema area of ​​the retinal image can be obtained by using only a computer through automatic encoding and compilation technology. Compared with macular edema recognition through machine learning, it does not require training through data or high-performance hardware, and is simpler and easier to use; it achieves a higher signal-to-noise ratio, has the characteristics of high accuracy, fast recognition speed and low hardware requirements, and is suitable for a variety of macular edema feature extraction scenarios.

[0109] The second embodiment of the present invention relates to a method for acquiring macular degeneration edema features in a retinal image. Compared with the first embodiment, this embodiment adds correction of the edema area in the current retinal image.

[0110] like Figure 7 FIG. 1 is a specific flow chart of the method for acquiring macular edema features in retinal images according to this embodiment.

[0111] Step 201, obtaining a current retinal image to be identified from a plurality of retinal images, is substantially the same as step 101 in the first embodiment and will not be described in detail here.

[0112] Step 202 : performing various preprocessing operations on the current retinal image to obtain a plurality of reference retinal images that have undergone various preprocessing operations.

[0113] Specifically, the current retinal image is preprocessed in different ways, including but not limited to: non-local mean processing, automatic contrast adjustment processing, sharpening processing, and Otsu's method processing.

[0114] Take the current retinal image as an example after the above four preprocessing steps:

[0115] Method 1: Non-local mean processing, such as the fast non-local mean denoising method, which takes into account the self-similarity of the image, makes full use of the redundant information in the image, and can maintain the detailed features of the image to the greatest extent while denoising; the non-local mean processing will lose the edge information of the image, and the image obtained by the non-local mean processing will generally be sharpened to enhance the edge information of the image. The sharpening method is, for example, an image convolution operation, such as using filter2D convolution, and using a 3*3 matrix operator (for example, [0, -1, 0], [-1, 5, -1], [0, -1, 0]) to convolve the image. Figure 8 As shown, Figure 8a is the original image of the current retinal image, Figure 8 b is the reference retinal image obtained by non-local mean processing of the current retinal image, Figure 8 c is Figure 8 The reference retinal image in b is sharpened.

[0116] Method 2: Automatic contrast adjustment processing, which automatically enhances the contrast of the current retinal image and improves the contrast between the bright and dark areas in the image, thereby making the brightness difference between the edema area and the surrounding medium in the current retinal image more obvious, and obtaining the corresponding reference retinal image. Figure 9 As shown, Figure 9 a is the original image of the current retinal image, Figure 9 b is the reference retinal image obtained by contrast enhancement of the current retinal image.

[0117] Method 3: Sharpening process, directly sharpen the current retinal image to enhance the edge information between the bright and dark parts of the image. For details, please refer to the sharpening process of method 1, which will not be described here. Figure 10 As shown, Figure 10 a is the original image of the current retinal image, Figure 10 b is the reference retinal image obtained by sharpening the current retinal image.

[0118] Method 4: Otsu method processing, the current retinal image is processed by Otsu method, using Otsu threshold (Otsu), according to the distribution law of pixels of different brightness, the darker peak is defined as the background, the brighter peak is defined as the foreground, and the brightness that maximizes the inter-class variance of the foreground and background images is selected as the brightness threshold. Otsu method processing is not affected by brightness and contrast, and can effectively highlight the foreground information in the current retinal image. Figure 11 As shown, Figure 11 a is the original image of the current retinal image, Figure 11 b is the reference retinal image obtained by processing the current retinal image with the Otsu method.

[0119] Step 203 : determining the edema area in each reference retinal image based on the brightness value of each pixel in each reference retinal image and the edema brightness threshold of each reference retinal image.

[0120] Specifically, the edema area in each reference retinal image is determined, wherein the specific method of determining the edema brightness threshold of the reference retinal image and the edema area in the reference retinal image is similar to the method of determining the edema area of ​​the current retinal image in steps 102 and 103 of the first embodiment, and will not be repeated here.

[0121] Step 204, based on the brightness values ​​of the pixels in the vitreous region of the current retinal image, determines the edema brightness threshold of the current retinal image. This is substantially the same as step 102 in the first embodiment and will not be described in detail here.

[0122] Step 205, based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, determines the edema area in the current retinal image. This is substantially the same as step 103 in the first embodiment and will not be repeated here.

[0123] Step 206 : determining the effective edema area in the current retinal image based on the edema areas of the reference retinal images and the edema area of ​​the current retinal image and preset weight parameters, wherein the preset weight parameters include weights of the reference retinal images and the current retinal image.

[0124] After the above process, the current retinal image has undergone the above four preprocessing processes to obtain four reference retinal images. After the four reference retinal images and the current retinal image are subjected to edema area identification, five images containing possible edema areas are obtained. The pixels in these five images are binarized, and the value of the pixels in the edema area is 1, and the value of the pixels in the background area is 0. The value of the pixel at each position in the five images is then multiplied by the weight corresponding to each image and a sum is calculated to obtain a score for the pixel at each position. For each pixel at each position, if the score of the pixel is greater than a preset score (for example, 70), the pixel is determined to belong to a valid edema area; otherwise, the pixel is determined not to belong to a valid edema area.

[0125] Each of the four reference retinal images and the current retinal image has a corresponding weight. For example, the weight of the reference retinal image obtained by non-local means processing in method 1 is 40, the weight of the reference retinal image obtained by automatic contrast adjustment processing in method 2 is 20, the weight of the reference retinal image obtained by sharpening processing in method 3 is 50, and the weight of the reference retinal image obtained by Otsu's method in method 4 is 20; the weight of the current retinal image is 40. For example, if the values ​​of a pixel at a certain position on the five images are 0, 1, 1, 0, and 1, respectively, then the score of the pixel = (40×0)+(20×1)+(50×1)+(20×0)+(40×1)=110. 110 is greater than the preset score of 70, and the pixel is determined to belong to the effective edema area.

[0126] Based on the above process, it is possible to determine whether each pixel in the current retinal image belongs to a valid edema area, and thus the valid edema area in the current retinal image can be obtained.

[0127] Step 207 : For each edema region, determine the area of ​​the edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region.

[0128] Step 208 : obtaining the volume of each edema region in the current retinal image based on the area of ​​each edema region in the current retinal image and the image spacing information of the current retinal image.

[0129] Step 207 and step 208 are similar to step 104 and step 105 in the first embodiment and are not described in detail here. The main difference is that step 207 and step 208 are aimed at the effective edema area determined in step 206.

[0130] Subsequently, the reference retinal image obtained by the four pre-processing steps may be saved, and the identified edema area, sampling area, etc. may be marked therein; similarly, the current retinal image with the final edema area marked therein may be saved.

[0131] In this embodiment, the current retinal image is subjected to multiple preprocessing methods, and different preprocessing methods can highlight different image features. Then, the edema area in the current retinal image is adjusted in combination with the edema area in the reference retinal image obtained after preprocessing the current retinal image to obtain the final effective edema area, thereby achieving a better denoising effect and further improving the accuracy of edema area feature extraction, that is, further improving the segmentation accuracy of the edema area in the retinal image.

[0132] The third embodiment of the present invention relates to a method for acquiring macular edema features in retinal images. Compared with the first embodiment, this embodiment adds a denoising process based on RPE layer identification.

[0133] like Figure 12 FIG. 1 is a specific flow chart of the method for acquiring macular edema features in retinal images according to this embodiment.

[0134] Step 301, obtaining a current retinal image to be identified from a plurality of retinal images, is substantially the same as step 101 in the first embodiment and will not be described in detail here.

[0135] Step 302, based on the brightness values ​​of the pixels in the vitreous region of the current retinal image, determines the edema brightness threshold of the current retinal image. This is substantially the same as step 102 in the first embodiment and will not be described in detail here.

[0136] Step 303: Identify the RPE layer of the current retinal image based on the brightness value of each pixel in the current retinal image.

[0137] Specifically, the effective area of ​​the current retinal image is first obtained, and the relevant content in the first embodiment can be referred to for details. Then, the target pixel with the highest brightness is searched by column within the effective area of ​​the current retinal image. For the target pixel in the current column within the searched effective area of ​​the current retinal image, if the difference in the longitudinal position between the target pixel in the current column and the target pixel in the previous column adjacent to the current column is greater than the first difference threshold, the longitudinal position of the target pixel in the current column is determined based on the longitudinal position of the target pixel in the previous column adjacent to the current column.

[0138] In one example, starting from any middle column of the valid area of ​​the current retinal image, the target pixel with the highest brightness in each column is searched to the left and right respectively. For example, the middle column is selected from multiple columns in the valid area of ​​the current retinal image (if the total number of columns is even, any one of the two middle columns is selected), and identification is performed column by column starting from the middle column to the right. For the current column, the target pixel with the highest brightness in the current column is first found, and then the Y-axis position of the target pixel is compared with the Y-axis position of the target pixel in the previous column. If the difference between the Y-axis positions is less than a first difference threshold, it is determined that the target pixel in the current column belongs to the RPE layer; if the difference between the Y-axis positions is greater than the first difference threshold (for example, 20 pixels), it indicates that there may be other highlighted positions on the image causing interference, and it is determined that the target pixel in the current column does not belong to the RPE layer, and the Y-axis position of the target pixel in the previous column is used as the Y-axis position of the target pixel in the current column, and the X-axis position of the target pixel in the current column remains unchanged. The algorithm then repeats the process, starting from the middle column of the active area of ​​the retinal image and working its way leftward. This process then identifies the target pixels belonging to the RPE layer in each column to the left of the middle column. Starting pixel recognition from the middle column of the active area of ​​the retinal image prevents image edge blur from affecting RPE layer recognition accuracy.

[0139] From the above, the target pixels belonging to the RPE layer in each column of the effective area of ​​the current retinal image can be determined. It should be noted that during the recognition process, if there are multiple columns (the specific number can be determined based on the total number of columns in the effective area, such as one-quarter of the total number of columns) in the effective area of ​​the current retinal image, and the difference between the vertical axis position of the target pixel in the previous column is greater than the first difference threshold, it is necessary to consider whether there is a problem with the vertical axis position of the target pixel in the column starting to be recognized; or whether there is too much highlight interference in the effective area of ​​the current retinal image.

[0140] Then, the target pixels in each column within the effective area of ​​the current retinal image are combined to obtain the RPE layer of the current retinal image. The positions of all target pixels in the RPE layer are then recorded to form a corresponding coordinate array.

[0141] In addition, if the current retinal image is associated with multiple specified retinal images, the position of the RPE layer of the current retinal image can also be corrected based on the position of the RPE layer of these multiple specified retinal images; the specific process is as follows: the multiple specified retinal images associated with the current retinal image refer to multiple OCT images (OCT images in the same group) acquired continuously from the same patient during one OCT image acquisition. Among these multiple OCT images, except the current retinal image, all other OCT images can be used as designated retinal images; the positions of the RPE layers of these multiple OCT images should be similar.

[0142] For these multiple specified retinal images, based on a similar process as described above, the position of the RPE layer in the effective area of ​​each specified retinal image is determined respectively; then, based on the average height of the pixels included in the RPE layer of the multiple specified retinal images, the pixel reference height of the RPE layer is determined; that is, for the RPE layer of each specified retinal image, the mean of the longitudinal axis position coordinates of all pixels of the RPE layer of the specified retinal image is calculated as the pixel average height of the RPE layer of the specified retinal image; thus, the pixel average height of the RPE layer of all specified retinal images can be calculated. Then, the mean of the pixel average heights of the RPE layer of all specified retinal images is calculated as the pixel reference height of the RPE layer; however, not limited to this, the median of the pixel average heights of the RPE layer of all specified retinal images can also be selected as the pixel reference height of the RPE layer.

[0143] After determining the pixel reference height of the RPE layer, the pixel average height of the pixels included in the RPE layer of the current retinal image is compared with the pixel reference height. If the difference between the pixel average height of the pixels included in the RPE layer of the current retinal image and the pixel reference height is greater than a second difference threshold, it indicates that the position of the RPE layer in the effective area of ​​the determined current retinal image is inaccurate. Based on the longitudinal axis position of the pixels included in the RPE layer of a specified retinal image adjacent to the current retinal image, the RPE layer of the current retinal image is determined; that is, the longitudinal axis position of the pixels included in the RPE layer of the specified retinal image adjacent to the current retinal image is used as the longitudinal axis position of the pixels of the RPE layer of the current retinal image, thereby re-determining the RPE layer in the current retinal image. Wherein, the specified retinal image adjacent to the current retinal image can be a specified retinal image that is closest to the acquisition time of the current retinal image.

[0144] After the RPE layer of the current retinal image is determined through the above process, the RPE layer can be marked on the current retinal image. It is impossible for the area below the RPE layer to have an edema area. Therefore, based on the position of the RPE layer in the retinal image, the area below the RPE layer can be avoided from being misidentified as a macular edema area.

[0145] Furthermore, after step 303, the following steps are further included:

[0146] Step 304: Acquire multiple specified retinal images associated with the current retinal image, and determine a pixel reference height of the RPE layer based on an average height of pixels included in the RPE layer of the multiple specified retinal images; if the difference between the average pixel height of pixels included in the RPE layer of the current retinal image and the pixel reference height is greater than a second difference threshold, determine the RPE layer of the current retinal image based on the longitudinal axis position of pixels included in the RPE layer of a specified retinal image adjacent to the current retinal image.

[0147] That is, the RPE layer of multiple specified retinal images (e.g., 5 images on the left and right of the current retinal image) adjacent to the current retinal image among the multiple retinal images obtained by slicing a three-dimensional stereoscopic image of the user are respectively identified to obtain the RPE layer of the multiple specified retinal images, and then the pixel average height of the pixels contained in the RPE layer in each specified retinal image is calculated to obtain the pixel average height of the RPE layer in the multiple specified retinal images, and the pixel reference height is determined based on the multiple pixel average heights. For example, the median of the pixel average heights of the RPE layer in the multiple specified retinal images is selected as the pixel reference height, or the mean is calculated as the pixel reference height. reference height; then comparing the average pixel height of the pixels in the RPE layer of the current retinal image with the pixel reference height; if the difference between the two is greater than a second difference threshold (for example, 20 pixels), it is determined that an error occurs in the RPE layer recognition of the current retinal image; if an error occurs in the RPE layer recognition of the current retinal image, the coordinates of each pixel in the RPE layer in the previous or next specified retinal image adjacent to the current retinal image are used as the coordinates of the RPE layer in the current retinal image, thereby being able to re-determine an RPE layer in the current retinal image; if the difference between the two is less than the second difference threshold, it is determined that no error occurs in the RPE layer recognition of the current retinal image.

[0148] The height of the pixel is the Y-axis coordinate of the pixel. The coordinate system takes the pixel in the lower left corner of the retinal image as the origin, the Y-axis is upward from the origin, and the X-axis is to the right of the origin.

[0149] Step 305 , based on the RPE layer of the current retinal image, the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the edema area in the current retinal image is determined, and the pixels in the edema area are all located above the RPE layer.

[0150] This is substantially the same as step 103 in the first embodiment and will not be described in detail here. The main difference is that the edema area feature extraction of the current retinal image can be performed based on the position of the RPE layer in the current retinal image and denoising can be performed. The specific method is as follows:

[0151] Method 1: After determining the position of the RPE layer in the current retinal image, the portion below the RPE layer in the current retinal image is adjusted to black. This is because the area below the RPE layer is close to the macular edema area in terms of brightness and shape. Therefore, by blackening, the area below the RPE layer can be prevented from being mistakenly identified as the macular edema area. Figure 13 ,in Figure 13 a is the original forward retinal image; Figure 13 b is the current retinal image with the portion below the RPE layer adjusted to black. At this time, the edema area in the current retinal image is determined based on the brightness value of each pixel in the current retinal image with the portion below the RPE layer adjusted to black and the edema brightness threshold of the current retinal image. That is, when determining the edema area in the current retinal image, the current retinal image with the portion below the RPE layer adjusted to black is targeted. Please refer to Figure 14 , the part below the RPE layer is adjusted to black, and the red pixels in the current retinal image are the identified edema areas. It can be seen that the noise below the RPE layer is eliminated.

[0152] Method 2: First, the features of the edema area in the current retinal image are extracted to determine the edema area in the current retinal image. Then, based on the position of the RPE layer in the current retinal image and the position of each pixel in the determined edema area, the edema area below the RPE layer is removed. This can also prevent the area below the RPE layer from being misidentified as the macular edema area.

[0153] Step 306: For each edema region, the area of ​​the edema region is determined based on the pixel spacing information of the current retinal image and the number of pixels in the edema region. This is substantially the same as step 104 in the first embodiment and will not be described in detail here.

[0154] Step 307: Based on the area of ​​each edema region in the current retinal image and the image spacing information between the current retinal image and adjacent retinal images, the volume of each edema region in the current retinal image is obtained. This process is substantially the same as step 105 in the first embodiment and will not be repeated here.

[0155] It should be noted that this embodiment can also be used as an improvement based on the second embodiment. Specifically, after obtaining multiple reference retinal images that have undergone various preprocessing processes, the RPE layer of each reference retinal image can be determined based on the method of this embodiment. Based on the position of the RPE layer of each reference retinal image, the edema area in each determined reference retinal image is denoised to avoid the area below the RPE layer being misidentified as a macular edema area.

[0156] A fourth embodiment of the present invention relates to an electronic device, which may be a common computer device, such as a desktop host, a laptop computer, etc., or a mobile phone.

[0157] The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to execute the method for acquiring macular lesion edema characteristics in retinal images of any one of the first to third embodiments.

[0158] Since the first embodiment and this embodiment correspond to each other, this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and the technical effects achieved in the first embodiment can also be achieved in this embodiment. To reduce repetition, they will not be repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0159] It should be noted that the memory may include random access memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. Similarly, the processor may also be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0160] A third embodiment of the present invention relates to a computer-readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, on which a computer program is stored. The invention is characterized in that when the computer program is run by a processor, the method for obtaining macular lesion edema characteristics in retinal images of any one of the first to third embodiments is executed.

[0161] Since the first embodiment and this embodiment correspond to each other, this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and the technical effects achieved in the first embodiment can also be achieved in this embodiment. To reduce repetition, they will not be repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0162] While preferred embodiments of the present invention have been described in detail above, it should be understood that aspects of the embodiments can be modified, if necessary, to employ aspects, features and concepts of the various patents, applications and publications to provide further embodiments.

[0163] These and other changes can be made to the embodiments in light of the above detailed description.In general, in the claims, the terms used should not be construed as limited to the specific embodiments disclosed in the specification and claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which these claims are entitled.

Claims

1. A method for acquiring macular edema features in retinal images, characterized in that: include: Acquiring a current retinal image to be identified from a plurality of retinal images, wherein the retinal image is an OCT image of the fundus; determining an edema brightness threshold of the current retinal image based on brightness values ​​of pixels within the vitreous region of the current retinal image; determining an edema area in the current retinal image based on a brightness value of each pixel in the current retinal image and an edema brightness threshold of the current retinal image, wherein the brightness value of the pixels in the edema area is less than the edema brightness threshold; Wherein, determining the edema brightness threshold of the current retinal image based on the brightness values ​​of pixels in the vitreous region of the current retinal image includes: Obtaining an initial brightness threshold value of the brightness values ​​of pixels greater than a preset percentage of all pixels in the vitreous body area, wherein the preset percentage is greater than 50%; Searching for target pixels in the vitreous region whose brightness values ​​are within a set brightness value range, and counting the number of target pixels having each target brightness value based on the target brightness value of each target pixel; wherein the set brightness value range is a brightness value range with the initial brightness threshold as the midpoint; Searching for a target brightness value having the smallest target pixel number difference from the initial brightness threshold among all the target brightness values ​​as a minimum change brightness threshold; The edema brightness threshold is determined based on the initial brightness threshold and the minimum change brightness threshold.

2. The method for acquiring macular edema features in retinal images according to claim 1, characterized in that: The method further comprises: performing a plurality of preprocessing operations on the current retinal image to obtain a plurality of reference retinal images that have undergone the plurality of preprocessing operations; determining an edema area in each of the reference retinal images based on a brightness value of each pixel in each of the reference retinal images and an edema brightness threshold of each of the reference retinal images; Based on the edema area of ​​each reference retinal image and the edema area of ​​the current retinal image and preset weight parameters, the effective edema area in the current retinal image is determined, and the preset weight parameters include: the weights of each reference retinal image and the current retinal image.

3. The method for acquiring macular edema features in retinal images according to claim 1, characterized in that: Before determining the edema area in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the method further includes: Identifying an RPE layer of the current retinal image based on a brightness value of each pixel in the current retinal image; Determining an edema area in the current retinal image based on a brightness value of each pixel in the current retinal image and an edema brightness threshold of the current retinal image includes: Based on the RPE layer of the current retinal image, the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the edema area in the current retinal image is determined, and all pixels in the edema area are located above the RPE layer.

4. The method for acquiring macular edema features in retinal images according to claim 1, wherein: Determining the edema brightness threshold based on the initial brightness threshold and the minimum change brightness threshold includes: The product of the average value between the initial brightness threshold and the minimum change brightness threshold and a preset correction coefficient is obtained as the edema brightness threshold, and the preset correction coefficient is greater than 0.

5.

5. The method for acquiring macular edema features in retinal images according to claim 1, characterized in that: Determining an edema area in the current retinal image based on a brightness value of each pixel in the current retinal image and an edema brightness threshold of the current retinal image includes: In the closed area formed by pixels in the current retinal image whose brightness value is less than the edema brightness threshold, a closed area whose area meets a preset condition is selected as an edema area; The preset condition is that the area of ​​the closed area is smaller than the product of the area of ​​the effective area in the current retinal image and a set threshold and is larger than the area of ​​a preset number of pixels, and the set threshold is greater than 0 and less than 0.

5.

6. The method for acquiring macular edema features in retinal images according to claim 1, characterized in that: After determining the edema area in the current retinal image based on the brightness value of each pixel in the current retinal image and the edema brightness threshold of the current retinal image, the method further includes: For each edema region, the area of ​​the edema region is determined based on the pixel spacing information of the current retinal image and the number of pixels in the edema region.

7. The method for acquiring macular edema features in retinal images according to claim 6, characterized in that: After determining the area of ​​each edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region, the method further includes: The volume of each edema region in the current retinal image is obtained based on the area of ​​each edema region in the current retinal image and image distance information between the current retinal image and adjacent retinal images.

8. The method for acquiring macular edema features in retinal images according to claim 6 or 7, characterized in that: Before determining the area of ​​each edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region, the method further includes: Performing etdrs partitioning on the current retinal image to obtain the edema area within the etdrs partition; For each edema region, determining the area of ​​the edema region based on the pixel spacing information of the current retinal image and the number of pixels in the edema region includes: For each of the edema regions within the etdrs partition, the area of ​​the edema region is determined based on the pixel spacing information of the current retinal image and the number of pixels within the edema region.

9. The method for acquiring macular edema features in retinal images according to claim 3, characterized in that: Identifying an RPE layer of the current retinal image based on a brightness value of each pixel in the current retinal image, comprising: Searching for a designated pixel with the highest brightness in a valid area of ​​the current retinal image by column, and determining, for a designated pixel in a current column within the valid area of ​​the current retinal image found to be in the current column, the vertical axis position of the designated pixel in the current column based on the vertical axis position of the designated pixel in the previous column adjacent to the current column if a vertical axis position difference between the designated pixel in the current column and the designated pixel in the previous column adjacent to the current column is greater than a first difference threshold; The RPE layer of the current retinal image is obtained by combining the designated pixels in each column within the effective area of ​​the current retinal image.

10. The method for acquiring macular edema features in retinal images according to claim 9, characterized in that: Searching for a target pixel with the highest brightness in a column within a valid area of ​​the current retinal image includes: Starting from any middle column in the effective area of ​​the current retinal image, search leftward and rightward respectively for the target pixel with the highest brightness in each column.

11. The method for acquiring macular edema features in retinal images according to claim 9, characterized in that: After obtaining the RPE layer of the current retinal image by combining the designated pixels in each column within the effective area of ​​the current retinal image, the method further includes: Acquire multiple designated retinal images associated with the current retinal image, and determine a pixel reference height of the RPE layer based on an average height of pixels included in the RPE layer of the multiple designated retinal images; If the difference between the average pixel height of the pixels included in the RPE layer of the current retinal image and the pixel reference height is greater than a second difference threshold, the RPE layer of the current retinal image is determined based on the longitudinal axis position of the pixels included in the RPE layer of the specified retinal image adjacent to the current retinal image.

12. The method for acquiring macular edema features in retinal images according to claim 1, characterized in that: The vitreous body area of ​​the current retinal image is determined as follows: The effective area of ​​the current retinal image is sampled in sequence using a sampling frame of a set size, and if the average brightness value of the pixels in the sampling area currently selected by the sampling frame is within a preset brightness range, it is determined that the sampling area currently selected by the sampling frame is the vitreous area.

13. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for acquiring macular lesion edema features in a retinal image according to any one of claims 1 to 12.

14. A computer-readable storage medium, wherein the computer-readable storage medium is a non-volatile storage medium or a non-transient storage medium, and a computer program is stored thereon, wherein: When the computer program is executed by a processor, the method for acquiring macular edema features in a retinal image according to any one of claims 1 to 12 is executed.

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