A method for segmentation of paint cracks on an ICGA image based on a conditional generative adversarial network

Abstract

A method for segmentation of paint cracks on an ICGA image based on a conditional generative adversarial network includes: (1), collecting an original ICGA image, extracting a complete fundus oculography image, labeling it with gold standard, normalizing fundus oculography image and gold standard, splicing it into a group of images as sample data, distributing the sample into a training set and atest set according to proportion; (2) based on the principle of conditional generative countermeasure network, constructing the network of generators and discriminators; (3) inputting that data of thetraining set into the network for adversarial train, defining a loss function, and generating a paint crack image correspond to the original picture by the training generator; (4) in the testing phase, inputting the data of the test set, and getting the corresponding paint crack segmentation result diagram through the trained generator G. The segmentation method provided by the invention can be used for solving the problems that the sample size of the ICGA image is small and the acquisition of the contrast image is difficult, and has the characteristics of high accuracy of the segmentation result.

Description

technical field [0001] The invention relates to a method for segmenting paint cracks on an ICGA image based on a conditional generative confrontation network, and belongs to the technical field of image processing. Background technique [0002] In recent years, with the rapid development of big data, deep learning networks have been widely used in computer vision, artificial intelligence and other fields. Among them, the generative confrontation network (GAN) is an important network tool to solve the problem of image translation. It is called "the coolest idea in the field of machine learning in the past 20 years". [0003] Compared with traditional graphical models, GAN is a better generative model, which in a sense avoids the Markov chain learning mechanism, which makes it different from traditional probabilistic generative models. Traditional probability generation models generally require Markov chain sampling and inference, but GAN avoids this process of extremely high...

AI Technical Summary

Problems solved by technology

[0005] The most important point is that when the probability density cannot be calculated, some generative models that traditionally rely on the natural interpretation of data cannot be learned and applied on it.

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