Automatic retinal blood vessel segmentation for clinical diagnosis of glaucoma

Abstract

The invention provides an automatic retinal blood vessel segmentation method for clinical diagnosis of glaucoma. Through the method, the influence of bright regions such as optic disc and exudate is eliminated by fusing five models depending on different image processing technologies, namely a matched filter, a neural network, multi-scale line detection, scale space analysis and morphology. At thesame time, massive data is not needed to establish a retinal blood vessel segmentation model, so the method greatly reduces the amount and complexity of data to be processed, is easy to realize, andcan effectively improve the efficiency of retinal blood vessel segmentation. The method also utilizes the region growth method and the gradient information to carry out iterative growth on the background and the blood vessel region on the basis of a multimodal fusion result, the segmentation results exhibit better continuity and smoothness, more retinal vascular details and more complete retinal vascular network can be kept, and thus the method effectively assists ophthalmologists in diagnosing diseases and lightens the burden of ophthalmologists.

Description

technical field [0001] The invention relates to the fields of digital medical image processing and analysis and intelligent medical care, in particular to an automatic retinal blood vessel segmentation method for glaucoma clinical diagnosis. Background technique [0002] Glaucoma is the world's leading irreversible blinding eye disease, known as the "silent thief of sight". It is estimated that by 2020, the number of glaucoma patients worldwide will rise to 79.6 million. At present, the prevalence of open-angle glaucoma in people over 40 years old in my country has reached 2.6%, accounting for 2 / 3 of the total number of glaucoma patients, and the blindness rate is 15% to 30%, which is much higher than the average level of 8% in economically developed countries. Early detection, early diagnosis, and early treatment are very important to inhibit the development of glaucoma. Color fundus images can directly observe retinal vascular lesions and other lesions such as exudates a...

AI Technical Summary

Problems solved by technology

Compared with the traditional method, although this method can obtain better segmentation accuracy and robustness; however, deep learning is a data-driven model that requires massive levels of data for guarantee, which will seriously affect the accuracy of retinal vessel segmentation. efficiency, which limits its application in clinical practice

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