Diabetic retinal vessel image segmentation method

Abstract

The invention discloses a diabetic retinal vessel image segmentation method and belongs to the technical field of image processing. The method comprises steps as follows: step 1, a preprocessed eye fundus image is subjected to binaryzation processing according to preset binaryzation threshold values, a center line and the edge of the eye fundus image subjected to binaryzation processing are extracted, and a vessel tree is obtained; step 2, bifurcation of the vessel tree is subjected to disconnection processing, vessel sections are obtained, each vessel section is linearly segmented, vessels are obtained, and an original vessel set is obtained; step 3, mistakenly segmented vessels are determined and removed from the original vessel set, and a global vessel set is obtained. After the original vessel set is obtained, the mistakenly segmented vessels are further determined by use of the background and the shape of vessels, and the mistakenly segmented vessels caused by annular light reflection due to shooting, non-vascular step edges on the periphery of the optic disk, porphyritic lesion, bleeding lesion and other causes can be effectively removed.

Description

technical field [0001] The invention relates to an image segmentation method, in particular to a retinal vessel image segmentation method for diabetics, and belongs to the technical field of image processing. Background technique [0002] The retina is the only deep blood vessel that can be directly observed in a non-invasive way in the human body, and the retinal blood vessel is the most important anatomical structure visible in fundus images, and the changes in its structural characteristics directly reflect the impact of cardiovascular diseases such as diabetes, hypertension and arteriosclerosis Influence of network morphology structure. Studies have shown that through periodic fundus screening and early diagnosis, the risk of cardiovascular disease can be greatly reduced. Considering the objectivity, repeatability, accuracy, and large-scale requirements of fundus screening results, there are urgent clinical requirements for automatic detection and quantitative analysis ...

AI Technical Summary

Problems solved by technology

However, through the analysis of the existing methods, it is found that the existing methods have the problem of low segmentation accuracy and low real-time performance, which makes it difficult to be widely used in clinics that have high real-time requirements.