Fundus image retina arteriosclerosis detection method based on improved encoding and decoding network

Abstract

The invention provides a fundus image retina arteriosclerosis detection method based on an improved encoding and decoding network, and the method comprises the steps: 1) collecting a fundus image, diagnosed as retina arteriosclerosis, of an ophthalmologist, fitting diseased blood vessels in the fundus image, and calculating and counting reflection parameters, so as to determine a retina arteriosclerosis detection threshold value; 2) improving a coding and decoding network by utilizing an Inception ResnetV2 module and a residual attention mechanism module, and applying the coding and decoding network to segmentation of artery blood vessels and artery reflective tapes; and 3) screening an effective area, sampling the effective area, fitting by utilizing a four-section Gaussian model to obtain a blood vessel gray scale distribution curve, calculating a reflection parameter according to a fitting result, comparing the reflection parameter with a threshold value, and judging whether the patient suffers from retinal arteriosclerosis or not. According to the method, the quantitative detection threshold of the retinal arteriosclerosis is determined, the deep learning technology is utilized, the problem that fundus arteriovenous blood vessels and artery reflective tapes cannot be accurately segmented through a traditional method is solved, and detection of the retinal arteriosclerosis is completed.

Description

technical field [0001] The invention relates to a method for detecting retinal arteriosclerosis in fundus images based on an improved encoding and decoding network, which solves the problem that the traditional method cannot accurately segment fundus arteriovenous vessels and arterial reflective bands, and reduces the reflection of light and other fundus tissue characteristics on arteries The interference brought by band segmentation improves the transmission efficiency of feature and gradient information, realizes high-accuracy segmentation, determines the detection threshold of retinal arteriosclerosis, and realizes high-accuracy detection of retinal arteriosclerosis. It belongs to the fields of image processing, deep learning and medical imaging. Background technique [0002] Retinal arteriosclerosis is related to the degree of systemic arteriosclerosis. Since the fundus blood vessels are the only blood vessels that can be directly observed non-invasively, regular detect...

AI Technical Summary

Problems solved by technology

Although the segmentation of blood vessels in fundus images has been widely concerned, there are few studies on the segmentation of arteries and arterial reflective tapes.

At the same time, because the shape and trend of venous vessels are similar to arterial vessels, when the quality ...

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