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Proceedings Paper

False positive reduction for wall thickness-based detection of colonic flat polyps via CT colonography
Author(s): Marc Pomeroy; Lihong C. Li; Hao Han; Xinzhou Wei; Perry J. Pickhardt; Zhengrong Liang
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Paper Abstract

Computer-aided detection (CAD) of flat polyps, in contrast to other polyp types, is challenging due to their lack of projections from the colonic surface and limited geometrical features that can be extracted from such polyps. In this paper, we present a new approach for CAD of flat polyps via colon wall thickness mapping, texture feature extraction and analysis. First, we integrated our previous work of detecting flat polyp candidates via colon wall thickness mapping into this study for automated detection of initial polyp candidates (IPCs). The colon wall segmentation is established on a coupled level-set method after the lumen is electronically cleansed by a sophisticated statistical algorithm, which considers the partial volume effect to preserve the mucosa layer details. The IPC detection was performed based on the wall thickness local pattern. From each IPC volume, we extracted the 14 Haralick texture features and 16 additional features that were previously demonstrated to improve polyp classification performance. Then, we adopted the Rpackage “randomForest” to classify the features for false positive (FP) reduction. We evaluated our method via 16 patient datasets. The proposed scheme achieved a high capacity in terms of the well-known area under the curve value of 0.930. The FPs was reduced to less than 3 FPs/per polyp. The experiment results demonstrate the feasibility of our method in achieving computer aided detection of flat polyps, therefore, improving the screening capability of computed tomography cololongraphy.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013434 (3 March 2017); doi: 10.1117/12.2254565
Show Author Affiliations
Marc Pomeroy, Stony Brook Univ. (United States)
Lihong C. Li, City Univ. of New York, College of Staten Island (United States)
Hao Han, Stony Brook Univ. (United States)
Xinzhou Wei, New York City College of Technology (United States)
Perry J. Pickhardt, Univ. of Wisconsin School of Medicine and Public Health (United States)
Zhengrong Liang, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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