Method for segmenting capillaries in microscopic image based on generative adversarial network

Abstract

The invention discloses a method for segmenting capillaries in a microscopic image based on a generative adversarial network. The objective of the invention is to solve the problems that a vascular fracture phenomenon often occurs in an algorithm segmentation result and vascular branch details are redundant due to the limitation of an algorithm or low actual imaging contrast of a microvascular image. The method comprises the steps of establishing a training model and a sample set based on a generative adversarial network; inputting the color fundus image in the sample set into a generation model, extracting image feature information, and outputting a microvascular probability image under a microscopic image as a generation sample; carrying out enhancement processing on the microscopic image comparison adaptive histogram equalization; increasing the data volume of training, and carrying out enhancement processing on the microscopic image processed by the preprocessing unit again; distinguishing the real sample from the generated sample; inputting a to-be-segmented retinal vessel color image into the segmentation model, and outputting a vessel segmentation result. The method is applied to capillary segmentation in microscopic images.

Description

technical field [0001] The invention relates to a method for segmenting microvessels in microscopic images based on a generative confrontation network. Background technique [0002] Microvascular images under microscopic images have uneven gray distribution, complex vascular structures, low contrast between target blood vessels and image background, and image noise. Microvascular segmentation under microscopic images is facing great challenges. Traditional segmentation methods include methods based on pattern recognition (supervised classification and unsupervised classification), methods based on matched filtering, methods based on mathematical morphology, and methods based on tracking. [0003] Generative Adversarial Network (GAN), the problem to be solved is how to generate new samples that conform to the probability distribution of real samples. An adversarial network can be thought of as consisting of a generative model and a discriminative model. During the training ...

AI Technical Summary

Problems solved by technology

[0004] The above method can extract most of the microvascular images. Due to the limitation of the algorithm or the low contrast of the actual imaging, the segmentation results of the algorithm often show the phenomenon of blood vessel breakage and the details of blood vessel branches are redundant.

Images