Contourlet Based Feature Extraction and Classification for Wireless Capsule Endoscopic Images
Wireless Capsule Endoscopy (WCE) is a late-model noninvasive device to detect abnormalities in small intestine. The traditional diagnostic method that only depending on clinicians naked eyes is timeconsuming and labor-intensive. It is necessary to develop a computer-aided system to alleviate the burden of clinicians. In this paper, a new colortexture feature extraction method is proposed for the classification of normal and abnormal WCE tissue images. The Contourlet Transform is introduced and used for each color channel of each WCE image in HSV color space. Finally, we construct a 288-dimensions feature vector by calculating the 3-order color moments for each baseband generated by using the Contourlet Transform. Real experiments using different classifiers in various color spaces are implemented to evaluate the performance of the proposed method.
Wireless Capsule Endoscopy Feature Extraction and Classification The Contourlet Transform.
CHEN Junzhou HE Run ZHANG Li PENG Qiang GAN Tao
Department of Computer Science and Technology, Southwest Jiaotong University, ChengDu, China Digestive Endoscopic Center, West China Hospital, Sichuan University
国际会议
上海
英文
216-220
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）