Reduction of Redundant Frames in Active Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) is a useful technique to detect the abnormal lesion such as bleeding and polyp location in human gastrointestinal (GI) tract. It also helps in examine the ulcer and cancer in gastrointestinal tract. Active WCE is a new technique to cope up with the problem of nonuniform motion of passive WCE. There are several numbers of similar frames in active WCE video due to natural peristalsis in gastrointestinal tract of the human body. Here, a reduction model for active WCE video for the reduction of visualization time by a clinician is proposed. This reduction model consists of two levels. In the first level, we apply intensity based skip-prediction method to find out duplicate frames. In the second level, random sample consensus (RANSAC) combined with Harris corner detection is applied to the remaining redundant frames.

[1]  Xie Xiang,et al.  Low-complexity and high-efficiency image compression algorithm for wireless endoscopy system , 2006, J. Electronic Imaging.

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[4]  P. Dario,et al.  Capsule Endoscopy: From Current Achievements to Open Challenges , 2011, IEEE Reviews in Biomedical Engineering.

[5]  Hans P. Moravec.  Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[6]  Pierre Kornprobst,et al.  Harris corners in the real world: A principled selection criterion for interest points based on ecological statistics , 2009, CVPR.

[7]  Yihua Lan,et al.  Trimming the Wireless Capsule Endoscopic Video by Removing Redundant Frames , 2012, 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing.

[8]  Max Q.-H. Meng,et al.  Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments , 2009, Comput. Biol. Medicine.

[9]  Byeong-Seok Shin,et al.  Reducing redundancy in wireless capsule endoscopy videos , 2013, Comput. Biol. Medicine.

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  Hong Liu,et al.  Wireless Capsule Endoscopy Video Reduction Based on Camera Motion Estimation , 2013, Journal of Digital Imaging.

[12]  Iain E. Garden Richardson,et al.  Low-complexity skip prediction for H.264 through Lagrangian cost estimation , 2006, IEEE Transactions on Circuits and Systems for Video Technology.