Retinal vessel segmentation method and system based on retinal fundus image

Abstract

The invention discloses a retinal vessel segmentation method and system based on a retinal fundus image, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected retinal fundus image; constructing a basic module according to the retinal fundus image features; cascading N basic modules to serve as a final network model, the to-be-detected retinal fundus image serving as input of the whole network model, and obtaining a segmentation result of retinal vessels. The foreground characteristics of the previous basic module and the originalpicture are transmitted to the next basic module together, so that the rear basic module can inherit the learning experience of the front basic module, the training process is accelerated, and the problem of data imbalance is effectively solved; the to-be-detected retinal fundus image is used as the input of the overall model S-UNet, and the obtained segmentation result of the retinal blood vessel is more accurate.

Description

Technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and system for retinal blood vessel segmentation based on retinal fundus images. Background technique [0002] Ophthalmologists usually check the retinal fundus images to assess the clinical condition of retinal blood vessels, which are important indicators for diagnosing various eye diseases. However, manual marking of retinal blood vessels in these images is time-consuming and cumbersome, and requires rich clinical experience. Therefore, real-time automatic segmentation of retinal blood vessels is very necessary, and it has also attracted much attention in recent years. [0003] Existing retinal vessel segmentation methods can be divided into unsupervised and supervised methods. For unsupervised methods, it is necessary to manually design feature extraction rules based on given data samples, so as to distinguish between blood vessels and background tissues b...

AI Technical Summary

Problems solved by technology

However, these patch-based methods have slow convergence speed, long testing time, and cannot obtain real-time results, so they are less applicable in clinical applications

For small data sets, previous methods have adopted a variety of data enhancement methods, which apply spatial adaptive contrast enhancement technology to retinal fundus images for blood vessel segmentation, and use static wavelet transform (SWT) to perform retinal fundus image segmentation. preprocessing, but SWT preprocessing is complicated and slow

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