A retinal blood vessel image segmentation method based on a multi-scale feature convolutional neural network

Abstract

The invention belongs to the technical field of image processing, in order to realize automatic extraction and segmentation of retinal blood vessels, improve the anti-interference ability to factors such as blood vessel shadow and tissue deformation, and make the average accuracy rate of blood vessel segmentation result higher. The invention relates to a retinal blood vessel image segmentation method based on a multi-scale feature convolutional neural network. Firstly, retinal images are pre-processed appropriately, including adaptive histogram equalization and gamma brightness adjustment. Atthe same time, aiming at the problem of less retinal image data, data amplification is carried out, the experiment image is clipped and divided into blocks, Secondly, through construction of a multi-scale retinal vascular segmentation network, the spatial pyramidal cavity pooling is introduced into the convolutional neural network of the encoder-decoder structure, and the parameters of the model are optimized independently through many iterations to realize the automatic segmentation process of the pixel-level retinal blood vessels and obtain the retinal blood vessel segmentation map. The invention is mainly applied to the design and manufacture of medical devices.

Description

technical field [0001] The invention belongs to the field of artificial intelligence combined with the field of medical image processing, and relates to a retinal blood vessel segmentation method based on a convolutional neural network combined with multi-scale features, which can automatically extract and segment blood vessel tree images. Background technique [0002] Among many fundus diseases, cataract, glaucoma, age-related macular degeneration and diabetic retinopathy are the four major causes of blindness, with high incidence and serious harm. Since the fundus is the only part of the human body where blood vessels can be directly observed, the analysis and processing of fundus images has become the main way to prevent and diagnose fundus diseases. Among them, the blood vessel segmentation of fundus images is an important method for quantitative analysis of diseases, and many studies have been carried out on the blood vessel segmentation of fundus images. However, the ...

AI Technical Summary

Problems solved by technology

However, the traditional supervision method requires manual design of relevant features, which requires a lot of energy and time in parameter optimization, and this method is greatly affected by the subjective experience of the designer.

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