Eye fundus image blood vessel recognition method and device, electronic equipment and storage medium
A fundus image and recognition method technology, applied in the field of image processing, can solve the problem of low blood vessel recognition accuracy in fundus images, and achieve the effects of improving image clarity, high segmentation accuracy and obvious graphic features
Pending Publication Date: 2021-06-11
BEIJING UNIV OF TECH
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[0004] The present invention provides a method, device, electronic equipment and storage medium for identifying blood vessels in fundus images, which are used to solve the defect of low recognition accuracy of blood vessels in fundus images in the prior art
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[0057] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
[0058]The effect of the blood vessel segmentation model trained in the prior art has a great relationship with the image quality of the training samples, that is, the classification results of the model trained by the training samples with poor image quality are poor, which makes some fuzzy pathological images with poor vascular characteristics trained The classification result obtained by th...
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The invention relates to an eye fundus image blood vessel recognition method and device, electronic equipment and a storage medium. The method comprises the steps of: obtaining a to-be-detected eye fundus image; based on the detection operator, extracting a first feature image and a second feature image of the to-be-detected eye fundus image; based on the semantic segmentation model, extracting a spatial shape feature image of the to-be-detected eye fundus image; according to the first feature image, the second feature image and the spatial shape feature image, reconstructing a to-be-detected eye fundus image; inputting the reconstructed to-be-detected eye fundus image into a blood vessel segmentation model to obtain a blood vessel segmentation image of the to-be-detected eye fundus image, wherein the semantic segmentation model is obtained by training according to a fundus image training set; and obtaining the blood vessel segmentation model by training according to the reconstructed fundus image training set. According to the method, the to-be-detected eye fundus image is reconstructed, the image definition is improved, the graphic features are more obvious, the reconstructed fundus image is input into the blood vessel segmentation model for blood vessel recognition, and the blood vessel segmentation image with higher segmentation precision is obtained.
Description
technical field [0001] The present invention relates to the technical field of image processing, in particular to a method, device, electronic equipment and storage medium for identifying blood vessels in fundus images. Background technique [0002] Fundus image analysis is an important issue in medical image analysis. Due to the objectivity, repeatability, accuracy, and large-scale requirements of fundus screening results, an effective method is adopted to automatically extract the vascular structure in retinal fundus images, which is helpful for the analysis of retinal fundus images. Early diagnosis and treatment tracking of fundus diseases have important clinical application value. [0003] Image feature extraction is an essential step in image processing and related fields of computer vision. During model training, the selection and extraction of image features will directly affect the learning effect of the model, thereby affecting the accuracy and learning rate of the...
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Login to View More IPC IPC(8): G06K9/00G06K9/62G06T7/00G06T7/10
CPCG06T7/0012G06T7/10G06T2207/20081G06T2207/30041G06V40/193G06F18/214
Inventor 吕思锐李鹏智杨鑫李建强
Owner BEIJING UNIV OF TECH