Automatic classification of digestive organs in wireless capsule endoscopy videos

ABSTRACT

Wireless Capsule Endoscopy (WCE) allows a physician to examine the entire small intestine without any surgical operation. With the miniaturization of wireless and camera technologies the ability comes to view the entire gestational track with little effort. Although WCE is a technical break-through that allows us to access the entire intestine without surgery, it is reported that a medical clinician spends one or two hours to assess a WCE video, It limits the number of examinations possible, and incur considerable amount of costs. To reduce the assessment time, it is critical to develop a technique to automatically discriminate digestive organs such as esophagus, stomach, small intestinal (i.e., duodenum, jejunum, and ileum) and colon. In this paper, we propose a novel technique to segment a WCE video into these anatomic parts based on color change pattern analysis. The basic idea is that the each digestive organ has different patterns of intestinal contractions that are quantified as the features. We present the experimental results that demonstrate the effectiveness of the proposed method.

References

1. J. Berens, M. Mackiewicz, and D. Bell. Stomach, intestine and colon tissue discriminators for wireless capsule endoscopy images. In Proc. of SPIE Conference on Medical Imaging, volume 5747, pages 283--290, Bellingham, WA, 2005.
2. G. Bresci, G. Parisi, M. Bertoni, T. Emanuele, and A. Capria. Video capsule endoscopy for evaluating obscure gastrointestinal bleeding and suspected small-bowel pathology. J Gastroenterol, 39(8):803--806, August 2004.
3. Y. Du, C.-I. Chang, and P. D. Thouin. Unsupervised approach to color video thresholding. Optical Engineering, 43(2):282--289, 2004.
4. W. B. Frakes and R. Baeza-Yates. Information Retrieval - Data Structures and Algorithms. Prentice Hall, Englewood Cliffs, 1992.
5. Y. Gong, H. Chua, and X. Guo. Image indexing and retrieval based on color histogram. In Proc. of Int'l Conf. Multimedia Modeling, pages 115--126, Singapore, Nov. 1995.
6. J. Hafner and et al. Efficient color histogram indexing for quadratic from distance function. In IEEE Transaction on Pattern Analysis and Machine Intelligence, pages 729--736, 1995.
7. P. Hubka, V. Rosik, J. Zdinak, M. Tysler, and I. Hulin. Independent Component Analysis of Electrogastrographic Signals. MEASUREMENT SCIENCE REVIEW, 5(2):21--24, 2005.
8. S. Hwang, J. Oh, J. Cox, S. J. Tang, and H. F. Tibbals. Blood detection in wireless capsule endoscopy using expectation maximization clustering, volume 6144. SPIE, 2006.
9. J. Lee, J.-H. Oh, and S. Hwang. Scenario based dynamic video abstractions using graph matching. In ACM Multimedia, pages 810--819, Singapore, November 2005.
10. M. Masaru Suzuki, S. Hori, T. Funabiki, T. Masaoka, R. S. Hirota, and N. Aikawa. Electrocardiogram Signal De-noising Using Multiple Auto-filtering. Acad Emerg Med, 13(5):S187, 2006.
11. P. Masri and A. Bateman. Improved modelling of attack transient in music analysis-resynthesis. University of Bristol., 1996.
12. P. Spyridonos, F. Vilarino, J. Vitria, F. Azpiroz, and P. Radeva. Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions. In Proceedings of the 9th MICCAI, Copenhagen, Denmark, October 2006.
13. P. Swain. Wireless capsule endoscopy. Internation Journal of Gastroenterology and Hepatology, 52(90004):48iv-50, June 2003.
14. S. Tang and G. Haber. Capsule endoscopy in obscure gastrointestinal bleeding. Gastrointest Endoscopy Clinics of North America, 14:87--100, 2004.
15. F. Vilarino, L. I. Kuncheva, and P. Radeva. ROC curves and video analysis optimization in intestinal capsule endoscopy. Pattern Recognition Letters, 27:875--881, 2006.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Video segmentation

      • Computer vision tasks

        • Scene understanding

        • Video summarization