A retinal vessel segmentation method based on regional growth PCNN

Abstract

The invention discloses a retinal vessel segmentation method based on regional growth PCNN. The retinal vessel segmentation method comprises the following steps: selecting seed points from unmarked pixels of a target retinal vessel image; Increasing the connection strength of the PCNN model, and extracting blood vessel characteristics in the target retina blood vessel image by using the PCNN modelwith the increased connection strength and taking the seed point as a starting point until the increased connection strength is greater than a first preset threshold value; If the blood vessel characteristics extracted through iteration at this time do not meet the first preset condition and the second preset condition at the same time, marking pixels corresponding to the blood vessel characteristics extracted through iteration at this time with the same label until all pixels are marked with labels, Wherein the first preset condition is that the proportion of the number of the blood vessel edge pixels to the total number of the blood vessel pixels is smaller than or equal to a second preset threshold value, and the second preset condition is that the ratio of the blood vessel area to thearea of the whole image is smaller than or equal to a third preset threshold. Automatic growth of the blood vessel area is achieved, and the retinal blood vessel segmentation precision is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically, relates to a retinal vessel segmentation method based on region growing PCNN Background technique [0002] Studies have shown that various ophthalmic diseases and cardiovascular and cerebrovascular diseases will cause deformation and bleeding of retinal blood vessels in the fundus to varying degrees. Clinically, medical personnel can extract retinal blood vessels from color fundus images collected by ophthalmoscopes, and diagnose such diseases by analyzing the shape of blood vessels. [0003] Due to the limitation of fundus image acquisition technology, there is often a lot of noise in the image, and the complex and changeable structure of retinal blood vessels makes the segmentation of retinal blood vessels difficult. The traditional method relies on manual segmentation of retinal vessels, which not only has a huge workload, is seriously affected by subjective fa...

AI Technical Summary

Problems solved by technology

[0005] (1) The structural characteristics of blood vessels are complex

The bending degree and shape of retinal blood vessels are different, and they are distributed in a tree shape, which makes segmentation difficult. Traditional methods are not accurate enough for blood vessel segmentation;

[0006] (2) The existing method based on PCNN and region growth [1] has very few small blood vessels, and the broken parts of the blood vessel segmentation cannot continue to grow

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