X-ray image classification using Random Forests with Local Binary Patterns
ICMLC(2010)
摘要
This paper presents a novel algorithm for the efficient classification of X-ray images to enhance the accuracy and performance. As for describing the characteristics of X-ray image, new Local Binary Patterns (LBP) is employed that allows simple and efficient feature extraction for texture information. To achieve fast and accurate classification task, Random Forests that is decision tree based ensemble classifier is applied. Comparing with other feature descriptors and classifiers, the testing results show that the proposed method improves accuracy, especially the speed for either training or testing.
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关键词
feature classifiers,feature descriptors,random forests,efficient classification,texture information,ensemble classifier,feature extraction,image classification,local binary patterns,image texture,x-ray image classification,x-ray imaging,decision tree,medical image processing,random forest,local binary pattern,classification algorithms,biomedical imaging,histograms
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