Image classification method for chronic venous disease based on multi-scale semantic features

Abstract

The invention discloses an image classification method of chronic venous disease based on multi-scale semantic features, includes multi-scale division of chronic vein disease image to be measured, classification of image block by concept classifier corresponding to each scale on each scale, and obtaining concept class of each image block in the chronic vein disease image to be measured; the frequency of each concept category on each scale is taken as the global representation feature, and the global representation feature on each scale is serially connected to obtain the multi-scale semantic representation of the chronic venous disease images to be measured. The feature selection method based on high-order correlation obtains the best feature subset from the multi-scale semantic representation, and inputs the feature subset into the scene classifier to obtain the classification results of the chronic venous disease images to be tested. The classification result of the invention has high accuracy and strong reliability.

Description

technical field [0001] The invention belongs to the field of medical image classification based on artificial intelligence, and more specifically relates to a method for classifying images of chronic venous diseases based on multi-scale semantic features. Background technique [0002] With the rapid development of modern medical imaging technology, automatic medical image classification plays an extremely important role in the medical field. Various machine learning methods including support vector machines (SVM), deep learning, Bayesian networks, rule-based classification methods, decision-level fusion, etc. are used to construct automatic classification models for medical images. However, most of these traditional medical image classification methods are constructed based on low-level image features such as color, texture, shape, etc. These low-level image features cannot reflect some hidden, high-level, and more discriminative information in medical images, thus causing ...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a chronic venous disease image classification method based on multi-scale semantic features, thereby solving the problem of poor discrimination performance of a single local feature and ignoring local label information in the prior art , The technical issues of spatial information, scale information and semantic information are not considered as a whole

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