A method and apparatus for blood vessel segmentation of fundus oculi image

Abstract

The invention provides a blood vessel segmentation method and a system of fundus image. The blood vessel segmentation method of fundus image comprises the following steps: step S1, constructing a neural network training set of fundus image for neural network training; step S2, amplifying the neural network training set to obtain the amplify neural network training set; step S3, constructing a neural network model for blood vessel segmentation of fundus oculi image based on depth learning; step S4, training the neural network model by using the expanded neural network training set; step S5, obtaining a target fundus image, and performing blood vessel segmentation on the target fundus image by using the trained neural network model. The blood vessel segmentation method and the blood vessel segmentation system of the fundus oculi image of the invention can overcome the limitation that the number of the blood vessel segmentation public data sets is low and can be adapted to the fundus oculi image of poor quality. The invention greatly simplifies the network structure, and can effectively improve the segmentation speed without losing the larger accuracy rate.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method and system for processing fundus images to segment blood vessels. Background technique [0002] Fundus blood vessels are blood vessels that can be directly observed in a non-invasive way. The structure and state of fundus blood vessels are an important reference for the health of the eyes and many organs of the body. [0003] At present, there are many kinds of algorithms for blood vessel segmentation, which are mainly divided into: 1. Unsupervised methods, mainly including: matched filter method, blood vessel tracking method, and methods based on deformation models. 2. Supervised methods mainly refer to machine learning and deep learning methods. [0004] Due to the limitation of the imaging quality of the fundus camera, the changeable shape and complex structure of the fundus blood vessel itself, the low contrast of the edge, and the difficulty in distinguishing the b...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the problem that the amount of fundus blood vessel data sets is too small, the present invention mainly improves in two aspects: 1. A method of removing the background and constructing a standardized training set is proposed to adapt to various fundus pictures

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