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Proceedings Paper

Integration of optical and virtual colonoscopy images for enhanced classification of colorectal polyps
Author(s): Marc Pomeroy; Yi Wang; Anushka Banerjee; Almas Abbasi; Matthew Barish; Edward Sun; Juan Carlos Bucobo; 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. The two current screening modalities, optical colonoscopy (OC) and virtual tomographic colonography (CTC), are both effective at detecting polyps, but the diagnostic performance from each has lagged behind detection. In this paper, we propose a texture analysis-based approach for integrating the complementary information from these two screening modalities. We use a set of well-established texture features including gray-level co-occurrence matrix features, gray-level run-length matrix features, local binary pattern features, first order histogram features, and more. To maximize the amount of textures extracted to examine the tissue heterogeneities between polyp pathologies, these textures are also computed on the higher order derivative images of the CTC polyp images and on the Hue/Saturation/Value color-space of the optical polyp images. The dataset used consisted of 165 polyps taken from 113 patients who underwent standard clinical prep prior to the procedures. Patients first had the CTC scan followed by the OC procedure, where the polyps where registered between imaging modalities and were pathologically confirmed for ground truth. Using a random forest classifier with a greedy feature selection algorithm, we find that the combination of using both CTC and OC texture features can improve the diagnostic performance by area under the receiver operating characteristic (AUC) score by upwards of 3%.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140N (16 March 2020); doi: 10.1117/12.2551394
Show Author Affiliations
Marc Pomeroy, Stony Brook Univ. (United States)
Yi Wang, Tianjin Univ. (China)
Anushka Banerjee, Stony Brook Univ. (United States)
Almas Abbasi, Stony Brook Univ. (United States)
Matthew Barish, Stony Brook Univ. (United States)
Edward Sun, Stony Brook Univ. (United States)
Juan Carlos Bucobo, Stony Brook Univ. (United States)
Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States)
Zhengrong Liang, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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