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Proceedings Paper

Texture feature analysis of neighboring colon wall for colorectal polyp classification
Author(s): Marc Pomeroy; Almas Abbasi; Kevin Baker; Matthew A. Barish; Samuel Stanley; Kenneth Ng; Perry J. Pickhardt; Zhengrong Liang
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Paper Abstract

Colorectal cancer (CRC) remains one of the leading causes of cancer deaths today. Since precancerous colorectal polyps slowly progress into cancer, screening methods are highly effective in reducing the overall mortality rate of CRC by removing them before developing into later stages. Virtual colonoscopy has been shown to be a practical screening method and provide a high sensitivity and specificity for diagnosis between hyperplastic polyps and precancerous adenomas or adenocarcinomas through the use of texture feature analysis. We hypothesize that effects from nonhyperplastic polyps, such as angiogenesis from adenocarcinomas, may result in changes to the texture of the colon wall that could help with computer aided diagnosis of the colorectal polyps. Here we present the preliminary results of incorporating the texture features of neighboring colon wall tissue into the diagnostic classification. We use gray level co-occurrence matrices to calculate the established Haralick features and a set of supplemental features for colorectal polyp regions of interest, as well as for the neighboring colon wall environment of the polyp. A random forest package was then used to perform the classification tests on different sets of features, with and without the inclusion of the environment to obtain an area under the curve (AUC) value of the receiver operating characteristic (ROC). Experiments show approximately a 1% increase in overall classification performance with the inclusion of the environment features.

Paper Details

Date Published: 13 March 2019
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502W (13 March 2019); doi: 10.1117/12.2513154
Show Author Affiliations
Marc Pomeroy, State Univ. of New York, Stony Brook Univ. (United States)
Almas Abbasi, State Univ. of New York, Stony Brook Univ. (United States)
Kevin Baker, State Univ. of New York, Stony Brook Univ. (United States)
Matthew A. Barish, State Univ. of New York, Stony Brook Univ. (United States)
Samuel Stanley, Washington Univ. in St. Louis (United States)
Kenneth Ng, State Univ. of New York, Stony Brook Univ. (United States)
Perry J. Pickhardt, Univ. of Wisconsin Medical School (United States)
Zhengrong Liang, State Univ. of New York, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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