Ms. Anushka Banerjee Profile

Anushka Banerjee

at Stony Brook Univ

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Proceedings Article | 16 March 2020 Presentation + Paper

Integration of optical and virtual colonoscopy images for enhanced classification of colorectal polyps

Marc Pomeroy, Yi Wang, Anushka Banerjee, Almas Abbasi, Matthew Barish, Edward Sun, Juan Carlos Bucobo, Perry Pickhardt, Zhengrong Liang

Proceedings Volume 11314, 113140N (2020) https://doi.org/10.1117/12.2551394

KEYWORDS: Image classification, Virtual colonoscopy, Colorectal cancer, Integrated optics, Cancer, Diagnostics, Tissue optics, Image processing, RGB color model

Read 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%.

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