Commenced in January 2007
Paper Count: 32128
Motion Analysis for Duplicate Frame Removal in Wireless Capsule Endoscope Video
Abstract:Wireless capsule Endoscopy (WCE) has rapidly shown its wide applications in medical domain last ten years thanks to its noninvasiveness for patients and support for thorough inspection through a patient-s entire digestive system including small intestine. However, one of the main barriers to efficient clinical inspection procedure is that it requires large amount of effort for clinicians to inspect huge data collected during the examination, i.e., over 55,000 frames in video. In this paper, we propose a method to compute meaningful motion changes of WCE by analyzing the obtained video frames based on regional optical flow estimations. The computed motion vectors are used to remove duplicate video frames caused by WCE-s imaging nature, such as repetitive forward-backward motions from peristaltic movements. The motion vectors are derived by calculating directional component vectors in four local regions. Our experiments are performed on small intestine area, which is of main interest to clinical experts when using WCEs, and our experimental results show significant frame reductions comparing with a simple frame-to-frame similarity-based image reduction method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063447Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1558
 G. Iddan, G. Meron, A. Glukhovsky, P. Swain, "Wireless capsule endoscopy", Nature, vol. 405, Issue 6785, pp. 417-418, 2000.
 S. Hwang, J. H. Oh, J. Cox, S. J. Tang, H. F. Tibbals, "Blood detection in wireless capsule endoscopy using expectation maximization clustering", Proceedings of SPIE, vol. 6144, pp. 577-587, 2006.
 J. Berens, M. Mackiewicz, D. Bell, "Stomach, intestine and colon tissue discriminators for wireless capsule endoscopy images", Proceedings of SPIE, Conference on Medical Imaging, vol. 5747, pp. 283-290, 2005.
 B. Li, MQH. Meng, "Texture analysis for ulcer detection in capsule endoscopy images", Image and Vision Computing, vol. 27, pp. 1336-1342, 2009.
 B. Li, MQH. Meng, "Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments", Computer in Biology and Medicine, pp. 141-147, 2009.
 Berthold K. P. Horn, Brian G. Schunck, "Determining Optical Flow", Artificial Intelligence, pp. 185-203, 1981.
 P. Anandan, "A computational framework and an algorithm for the measurement of visual motion", International Journal of Computer Vision, 2, pp. 283-310, 1989.
 E. Memin, P. Perez, "Hierarchical estimation and segmentation of dense motion fields", International Journal of Computer Vision, 46(2), pp. 129-155, 2002.
 T. Brox, A. Bruhn, N. Papenberg, J. Weickert, "High accuracy optical flow estimation based on a theory for warping", European Conference on Computer Vision, LNCS 3024, pp. 25-36, 2004.
 S. Uras, F. Girosi, A. Verri, and V. Torre. "A computational approach to motion perception", Biological Cybernetics, 60, pp. 79-87, 1988.
 M. J.Black. P. Anandan, "The robust estimation of multiple motions: parametric and piecewise smooth flow fields", Computer Vision and Image Understanding, 63(1), pp. 75-104, 1996.
 E. Memin. P. Perez, "A multigrid approach for hierarchical motion estimation", In Proc. Sixth International Conference on Computer Vision, pp. 933-938, 1998
 L. I. Rudin, S. Osher, E. Fatemi, "Nonlinear total variation based noise removal algorithms", Physica D, 60, pp. 259-268, 1992.
 I. Cohen, "Nonlinear variational method for optical flow computation", Proc. Eighth Scan-dinavian Conference on Image Analysis, volume 1, pp. 523-530, 1993
 L. Alvarez, J. Esclarin, M. Lefebure, J. Sanchez, "A PDE model for computing the optical flow", In Proc. XVI Congreso de Ecuaciones Diferenciales y Aplicationes, pp.1349-1356, 1999., 1999.