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Proceedings Paper

Automatic polyp region segmentation for colonoscopy images using watershed algorithm and ellipse segmentation
Author(s): Sae Hwang; JungHwan Oh; Wallapak Tavanapong; Johnny Wong; Piet C. de Groen M.D.
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Paper Abstract

In the US, colorectal cancer is the second leading cause of all cancer deaths behind lung cancer. Colorectal polyps are the precursor lesions of colorectal cancer. Therefore, early detection of polyps and at the same time removal of these precancerous lesions is one of the most important goals of colonoscopy. To objectively document detection and removal of colorectal polyps for quality purposes, and to facilitate real-time detection of polyps in the future, we have initiated a computer-based research program that analyzes video files created during colonoscopy. For computer-based detection of polyps, texture based techniques have been proposed. A major limitation of the existing texture-based analytical methods is that they depend on a fixed-size analytical window. Such a fixed-sized window may work for still images, but is not efficient for analysis of colonoscopy video files, where a single polyp can have different relative sizes and color features, depending on the viewing position and distance of the camera. In addition, the existing methods do not consider shape features. To overcome these problems, we here propose a novel polyp region segmentation method primarily based on the elliptical shape that nearly all small polyps and many larger polyps possess. Experimental results indicate that our proposed polyp detection method achieves a sensitivity and specificity of 93% and 98%, respectively.

Paper Details

Date Published: 29 March 2007
PDF: 12 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65141D (29 March 2007); doi: 10.1117/12.709835
Show Author Affiliations
Sae Hwang, Univ. of Texas at Arlington (United States)
JungHwan Oh, Univ. of North Texas (United States)
Wallapak Tavanapong, Iowa State Univ. (United States)
Johnny Wong, Iowa State Univ. (United States)
Piet C. de Groen M.D., Mayo Clinic and Foundation (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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