Retinal vessel segmentation method and system based on retinal fundus image

Abstract

The invention discloses a method and system for segmenting retinal blood vessels based on retinal fundus images, belonging to the technical field of image processing, including: acquiring a retinal fundus image to be detected; constructing a basic module according to the characteristics of the retinal fundus image; cascading N basic modules as In the final network model, the retinal fundus image to be detected is used as the input of the overall network model to obtain the segmentation result of retinal blood vessels. By passing the foreground features of the previous basic module and the original picture into the next basic module, the latter basic module can inherit the learning experience of the previous basic module, thus speeding up the training process and effectively solving the problem of data imbalance. Problem: Using the retinal fundus image to be detected as the input of the overall model S-UNet, the segmentation result of retinal blood vessels is more accurate.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a retinal blood vessel segmentation method and system based on retinal fundus images. Background technique [0002] Ophthalmologists usually evaluate the clinical status of retinal blood vessels by examining retinal fundus images, which are important indicators for diagnosing various ophthalmic diseases. However, manual labeling of retinal vessels in these images is time-consuming, tedious, and requires extensive clinical experience. Therefore, real-time automatic segmentation of retinal blood vessels is very necessary, and it has attracted much attention in recent years. [0003] Existing retinal blood vessel segmentation methods can be divided into unsupervised and supervised methods. For unsupervised methods, feature extraction rules need to be manually designed according to the given data samples, so as to distinguish blood vessels and background tissues according t...

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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