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Proceedings Paper

Automated polyp measurement based on colon structure decomposition for CT colonography
Author(s): Huafeng Wang; Lihong C. Li; Hao Han; Hao Peng; Bowen Song; Xinzhou Wei; Zhengrong Liang
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Paper Abstract

Accurate assessment of colorectal polyp size is of great significance for early diagnosis and management of colorectal cancers. Due to the complexity of colon structure, polyps with diverse geometric characteristics grow from different landform surfaces. In this paper, we present a new colon decomposition approach for polyp measurement. We first apply an efficient maximum a posteriori expectation-maximization (MAP-EM) partial volume segmentation algorithm to achieve an effective electronic cleansing on colon. The global colon structure is then decomposed into different kinds of morphological shapes, e.g. haustral folds or haustral wall. Meanwhile, the polyp location is identified by an automatic computer aided detection algorithm. By integrating the colon structure decomposition with the computer aided detection system, a patch volume of colon polyps is extracted. Thus, polyp size assessment can be achieved by finding abnormal protrusion on a relative uniform morphological surface from the decomposed colon landform. We evaluated our method via physical phantom and clinical datasets. Experiment results demonstrate the feasibility of our method in consistently quantifying the size of polyp volume and, therefore, facilitating characterizing for clinical management.

Paper Details

Date Published: 20 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350B (20 March 2014); doi: 10.1117/12.2043648
Show Author Affiliations
Huafeng Wang, The State Univ. of New York (United States)
Lihong C. Li, College of Staten Island, SUNY (United States)
Hao Han, The State Univ. of New York (United States)
Hao Peng, The State Univ. of New York (United States)
Bowen Song, The State Univ. of New York (United States)
Xinzhou Wei, New York City College of Technology (United States)
Zhengrong Liang, The State Univ. of New York (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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