Texture feature analysis of neighboring colon wall for colorectal polyp classification

Marc Pomeroy; Almas Abbasi; Kevin Baker; Matthew A. Barish; Samuel Stanley; Kenneth Ng; Perry J. Pickhardt; Zhengrong Liang

doi:10.1117/12.2513154

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13 March 2019 Texture feature analysis of neighboring colon wall for colorectal polyp classification

Marc Pomeroy, Almas Abbasi, Kevin Baker, Matthew A. Barish, Samuel Stanley, Kenneth Ng, Perry J. Pickhardt, Zhengrong Liang

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Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109502W (2019) https://doi.org/10.1117/12.2513154
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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Marc Pomeroy, Almas Abbasi, Kevin Baker, Matthew A. Barish, Samuel Stanley, Kenneth Ng, Perry J. Pickhardt, and Zhengrong Liang "Texture feature analysis of neighboring colon wall for colorectal polyp classification", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502W (13 March 2019); https://doi.org/10.1117/12.2513154

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