title: Contourlet-based features for computerized tumor detection in capsule endoscopy images.
abstract: This article presents a computer-aided detection system for capsule endoscopy (CE) images using contourlet-based color textural features to recognize tumors in the digestive tract. As tumor exhibits rich information in color texture, a novel color texture feature based on contourlet transform is proposed to describe characteristics of tumor in CE images. The proposed features are a hybrid of contourlet transform and uniform local binary pattern, yielding detailed and robust color texture features in multi-directions for CE images. Sequential floating forward search approach is further applied to refine the proposed features. With support vector machine for classification, comprehensive experiments on our present data reveal an encouraging accuracy of 93.6% for tumor detection in CE images using the proposed features.
authors.affiliation: Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, China. bpli@ee.cuhk.edu.hk
authors.forename: Baopu
authors.initials: B
authors.lastname: Li
authors.forename: Max Q-H
authors.initials: MQ
authors.lastname: Meng
mesh_terms: D000465:Algorithms; D053704:Capsule Endoscopy; D003952:Diagnostic Imaging / Q000379:methods*; D005260:Female; D005770:Gastrointestinal Neoplasms / Q000175:diagnosis*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D012680:Sensitivity and Specificity; D060388:Support Vector Machine