A fundus retinal blood vessel segmentation method and system based on K-Means clustering annotation and naive Bayesian model

Abstract

The invention provides a fundus retinal blood vessel segmentation method and system based on K-Means clustering annotation and naive Bayesian model. The method of the invention comprises the followingsteps: randomly extracting color fundus images in a data set to construct a training set and a test set; extracting the gray image of G channel of the color fundus image as the object of feature extraction; carrying out feature extraction on the gray image, and representing each pixel in the image by a multi-dimensional feature vector; for each image in the training set after feature extraction,using K-Means clustering algorithm to label eigenvectors by clustering; training a naive Bayesian model based on training set data labelled based on K-Means clustering; segmenting the blood vessels ofeach image in the test set using the trained naive Bayesian model. The invention regards the result of clustering as the label with supervised training, and trains the naive Bayesian classification model for retinal blood vessel segmentation by using the label. The whole process does not require human to participate in the label, saves time and labor, and greatly improves the processing efficiency of the machine learning model.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a fundus retinal blood vessel segmentation method based on K-Means clustering annotation and naive Bayesian model. Background technique [0002] Retinal blood vessels are an important part of the systemic microcirculatory system, and changes in their morphology are closely related to the severity of cardiovascular diseases such as diabetes, hypertension, coronary arteriosclerosis, and cerebrovascular sclerosis. Therefore, the research on retinal image vessel segmentation technology is of great significance for clinical application of medicine. [0003] The current methods of fundus retinal vessels mainly have two directions: rule-based vessel segmentation methods and learning-based vessel segmentation methods. The rule-based blood vessel segmentation method mainly uses image processing technology, and some rules are designed according to the characteristics of blo...

AI Technical Summary

Problems solved by technology

If only a small number of labeled examples are used, it is often difficult for the learning system trained by them to have strong generalization ability; on the other hand, if only a small number of "expensive" labeled Unmarked examples are a great waste of data resources

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