Arteriovenous retinal blood vessel classification method based on eye fundus image

Abstract

The invention discloses an arteriovenous retinal blood vessel classification method based on an eye fundus image. The method includes the steps that first, a global blood vessel set and optic disk positioning information of the fundus image are acquired, the global blood vessel set is a set of all blood vessels in the fundus image, and the optic disk positioning information comprises the optic disk center of the fundus image; second, main blood vessels are determined according to the global blood vessel set and the optic disk positioning information and classified so that main blood vessel classification information can be obtained; third, the main blood vessel classification information is used for classifying the blood vessels in the global blood vessel set through a breadth first-search algorithm based on SAT so that global classification information can be obtained. According to the method, the classification information of the main blood vessels around an optic disk is first obtained, external expansion diffusion is performed from the main blood vessels through the breadth first-search algorithm based on SAT so that all the blood vessels can be obtained, a complete automatic blood vessel classification method is achieved, manual intervention is not needed, and classification precision is high.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a method for classifying arteriovenous retinal blood vessels based on fundus images. Background technique [0002] With the rapid development of the field of artificial intelligence in computer technology, computer-aided diagnosis technology is also gradually developed. Computer-aided diagnosis technology refers to the use of imaging, medical image processing technology and other possible physiological and biochemical means, combined with computer analysis and calculation, to assist radiologists in finding lesions and improving the accuracy of diagnosis. [0003] Usually, computer-aided diagnosis in medical imaging is divided into three steps, as follows: the first step is to extract the lesion from the normal structure; the second step is to quantify the image features; the third step is to process the data and draw conclusions . [0004] Because the computer ...

AI Technical Summary

Problems solved by technology

[0007] Blood vessel segmentation, optic disc positioning and vessel classification (arteriovenous split) on fundus images are the basis of retinal vessel lesion detection. The existing blood vessel segmentation methods need to manually add labeling information, and the degree of automation is not high.

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