Dynamic scale distribution-based retinal vessel extraction method and system

Abstract

The present invention discloses a dynamic scale distribution-based retinal vessel extraction method and system. The method includes the following steps of: retinal image preprocessing: contrast enhancement is performed on the green channel component of a color retinal image; image segmentation: the preprocessed retinal image is segmented into a set number of sub-images; vessel classification: the vessels of each sub-image are divided into three categories, namely, a large category, a medium category and a small category; dynamic scale allocation: filters of different scales are dynamically selected to enhance vessels of different widths; multi-scale matched filtering; threshold processing: vascular structures are extracted, nonvascular structure was removed, the extraction results of all the sub-images are re-spliced, so that a retinal vessel network binary image can be obtained; and post-processing: post-processing is carried out, so that a high-segmentation accuracy retinal vessel network image can be obtained. With the method and system of the invention adopted, the vessel extraction of the retinal image can be realized; excessive estimation of the widths of the vessels can be avoided when complex nonvascular structures are removed; and simpler and more accurate retinal vessel extraction can be realized.

Description

technical field [0001] The invention relates to a method and system for extracting retinal blood vessels based on dynamic scale allocation. Background technique [0002] So far, the commonly used retinal vessel automatic extraction algorithms are: [0003] 1. Algorithm based on retinal blood vessel tracking method. This type of method can extract the retinal blood vessel network relatively completely, but the complexity of the algorithm is high and the amount of calculation is large. In addition, for some retinal blood vessel images with low contrast, the extraction accuracy of such algorithms is not enough. Among them, the typical retinal blood vessel tracking algorithm is based on the fuzzy C-means clustering algorithm proposed by Tolias in 1998, which selects a suitable seed point at the beginning of the blood vessel (optic disc), and thus performs the whole retinal blood vessel network. track. Establish a one-dimensional model of the retinal blood vessel cross-sectio...

AI Technical Summary

Problems solved by technology

However, this method still has certain limitations: it uses three filters of different scales to extract retinal blood vessels. For the blood vessel widths that vary greatly, there are still some blood vessel widths that cannot be well enhanced. Therefore, It is difficult to accurately segment all blood vessels; in addition, for some retinal images with lesion areas, there will be wrongly segmented pixels

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