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Detection of protruding lesion in wireless capsule endoscopy videos of small intestine
Author(s): Chengliang Wang; Zhuo Luo; Xiaoqi Liu; Jianying Bai; Guobin Liao
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Paper Abstract

Wireless capsule endoscopy (WCE) is a developed revolutionary technology with important clinical benefits. But the huge image data brings a heavy burden to the doctors for locating and diagnosing the lesion images. In this paper, a novel and efficient approach is proposed to help clinicians to detect protruding lesion images in small intestine. First, since there are many possible disturbances such as air bubbles and so on in WCE video frames, which add the difficulty of efficient feature extraction, the color-saliency region detection (CSD) method is developed for extracting the potentially saliency region of interest (SROI). Second, a novel color channels modelling of local binary pattern operator (CCLBP) is proposed to describe WCE images, which combines grayscale and color angle. The CCLBP feature is more robust to variation of illumination and more discriminative for classification. Moreover, support vector machine (SVM) classifier with CCLBP feature is utilized to detect protruding lesion images. Experimental results on real WCE images demonstrate that proposed method has higher accuracy on protruding lesion detection than some art-of-state methods.

Paper Details

Date Published: 27 February 2018
PDF: 10 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057513 (27 February 2018); doi: 10.1117/12.2293303
Show Author Affiliations
Chengliang Wang, Chongqing Univ. (China)
Zhuo Luo, Chongqing Univ. (China)
Xiaoqi Liu, Chongqing Univ. (China)
Jianying Bai, Third Military Medical Univ. (China)
Guobin Liao, Third Military Medical Univ. (China)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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