Abstract
Adenomatous polyps in the colon have a high probability of developing into subsequent colorectal carcinoma, the second leading cause of cancer deaths in United States. In this paper, we propose a new method for computer-aided diagnosis of polyps. Initial work with shape detection has shown high sensitivity for polyp detection, but at a cost of too many false positive detections. We present a statistical approach that uses support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue, and subsequently uses this information for the classification of the new cases. One of the main contributions of the paper is a new 3-D pattern analysis approach, which combines the information from many random images to generate reliable signatures of the shapes. At 80% polyp detection rate, the proposed system reduces the false positive rate by 80% compared of previous work.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Wingo P.J., Cancer Statistics, Ca Cancer Journal Clin, 1995; 45:8–30.
Thoeni R.F., Laufer I. “Polyps and cancer,” Textbook of Gastrointestinal Radiology, Philadelphia: W.B. Saunders, 1994; 1160.
Winawer S.J., Zauber A.G., Ho M.N., O’Brien M.J., Gottlieb L.S., Sternberg S.S., Waye.D., et. al. “Prevention of colorectal cancer by colonoscopic polypectomy,” The national polyp study workgroup, N Engl J Med. 1993; 329:1977–1981.
Dachman A.H., Kuniyoshi J.K., Boyle C.M., Samara Y., Hoffman K.R., Rubin D.T., Hanan I., “Interactive CT colonography with three-dimensional problem solving for detection of colonic polyps,” American Journal of Roentgenology, 1998; 171:989–995.
Summers R.M., Beaulieu C.F., Pusanik L.M., Malley J.D., Jeffrey R.B., Glazer D.I., Napel S., “Automated polyp detector for CT colonography: feasibility study,” Radiology, 216(1) 284–90, 2000.
Paik D.S., Beaulieu C.F., Jeffrey R.B.,Jr., Karadi C.A., Napel S., “Detection of Polyps in CT Colonography: A Comparison of a Computer-Aided Detection Algorithm to 3D Visualization Methods,” Radiological Society of North America 85th Scientific Sessions, Chicago, November 1999.
H. Yoshida, Y. Masutani, P.M. MacEneaney, K. Doi, Y. Kim, A.H. Dachman, “Detection of colonic polyps in CT colonography based on geometric features,” Radiology, vol. 217(SS), pp. 582–582, November 2000.
Göktürk S.B., Tomasi C., “A graph method for the conservative detection of polyps in the colon,” 2 nd International Symposium on Virtual Colonoscopy, Boston, October 2000.
Hu M.K., “Visual pattern recognition by moment invariants,” IRE transactions on information theory, vol. IT-8, pp 179–187, 1962.
A. Gersho and R.M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Press, 1992.
Vapnik V., Statistical Learning Theory, New York, 1998.
Hermanek P., “Dysplasia-carcinoma sequence, types of adenomas and early colo-rectal carcinoma,” European Journal of Surgery, 1987, 13:141–3.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Göktürk, S.B., Tomasi, C., Acar, B., Paik, D., Beaulieu, C., Napel, S. (2001). A Learning Method for Automated Polyp Detection. In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3_11
Download citation
DOI: https://doi.org/10.1007/3-540-45468-3_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42697-4
Online ISBN: 978-3-540-45468-7
eBook Packages: Springer Book Archive