Non-fluorescent eye fundus image based automatic segmentation method for retinal blood vessels

Abstract

The invention relates to a non-fluorescent eye fundus image based automatic segmentation method for retinal blood vessels. The method comprises following steps: step one, an image is preprocessed, blood vessel characteristics are enhanced, and background noise is weakened; step two, morphological characteristics of the retinal image are extracted with a multi-scale linear operator, and a refined blood vessel framework is segmented with two-dimensional Gabor wavelet transform; step three, after the refined blood vessel framework is segmented, the image is subjected to path morphology filtering, the maximum path length is combined with geometrical characteristics of a blood vessel region, and an adjacent map is constructed; step four, a binaryzation process is performed with regional connectivity analysis and a hysteresis threshold technology for auxiliary segmentation of microvessels, and the segmentation is finished. Compared with the prior art, the method has the advantages as follows: the blood vessel location accuracy is higher, the adhesion phenomena are reduced, and a retinal blood vessel network of the eye fundus image is effectively segmented.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an automatic retinal blood vessel segmentation method based on non-fluorescence fundus images. Background technique [0002] In 1989, Chaudhuri et al. published the first two-dimensional matched filter method in "Detection of blood vessels in retinal images using two-dimensional matched filters". This method assumes that blood vessels are some equal-width line segments, and the width of blood vessels is between 2- 10 pixels, and the gray distribution of the blood vessel cross-section can be approximated by a Gaussian curve. Based on this, 12 Gaussian templates in different directions are constructed to perform matching filtering on the image, and the maximum response result is output as the filtering result. At that time, this method achieved excellent enhancement effects on blood vessels in retinal images, but the disadvantage was that when the image was doped with nois...

AI Technical Summary

Problems solved by technology

The supervised segmentation method uses the standard image information of retinal blood vessels, and the segmentation results are satisfactory, but it requires a large number of standard images segmented by manual experts, and the segmentation efficiency is not high

[0005] At present, the methods proposed in the relevant literature generally have the problems of low segmentation accuracy of small blood vessels and prone to adhesion.

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