An SVM-based distal lung image classification using texture descriptors

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• 作者：Chesner Dé ; sir^a ; Caroline Petitjean^a ; ^{Caroline.Petitjean@univ-rouen.fr} ; Laurent Heutte^a ; Luc Thiberville^a ; ^b ; Mathieu Salaü ; n^a ; ^b
• 关键词：Image classification ; Feature extraction ; Feature selection ; LBP ; SIFT ; Lung ; Alveoli
• 刊名：Computerized Medical Imaging and Graphics
• 出版年：2012
• 期刊代码：69_08956111
• 类别：med
• 出版时间：June, 2012
• 卷：36
• 期：4
• 页码：264-270
• 文件大小：2981 K

摘要

A novel imaging technique can now provide microscopic images of the distal lung in vivo, for which quantitative analysis tools need to be developed. In this paper, we present an image classification system that is able to discriminate between normal and pathological images. Different feature spaces for discrimination are investigated and evaluated using a support vector machine. Best classification rates reach up to 90%and 95%on non-smoker and smoker groups, respectively. A feature selection process is also implemented, that allows us to gain some insight about these images. Whereas further tests on extended databases are needed, these first results indicate that efficient computer based automated classification of normal vs. pathological images of the distal lung is feasible.