title: A Novel Colon Wall Flattening Model for Computed Tomographic Colonography: Method and Validation.
abstract: Computed tomographic colonography (CTC) has been developed for screening of colon cancer. Flattening the three-dimensional (3D) colon wall into two-dimensional (2D) image is believed to (1) provide supplementary information to the endoscopic views and further (2) facilitate colon registration, taniae coli (TC) detection, and haustral fold segmentation. Though the previously-used conformal mapping-based flattening methods can preserve the angular geometry, they have the limitations in providing accurate information of the 3D inner colon wall due to the lack of undulating topography. In this paper, we present a novel colon-wall flattening method using a strategy of 2.5D approach. Coupling with the conformal flattening model, the presented new approach builds an elevation distance map to depict the neighborhood characteristics of the inner colon wall. We validated the new method via two CTC applications: TC detection and haustral fold segmentation. Experimental results demonstrated the effectiveness of our strategy for CTC studies.
authors: Faculty of Dept. of Radiology, Stony Brook University, New York, USA, and School of Software, Beihang University of Beijing, China.
authors: Huafeng
authors: H
authors: Wang
authors: Master student of School of Software, Beihang University of Beijing, China.
authors: Yuexi
authors: Y
authors: Chen
authors: Faculty of College of Staten Island, Staten Island, New York, USA.
authors: Lihong
authors: L
authors: Li
authors: Faculty of School of Software, Beihang University of Beijing, China.
authors: Haixia
authors: H
authors: Pan
authors: Faculty of Dept. of Computer Science, Stony Brook University, New York, USA.
authors: Xianfeng
authors: X
authors: Gu
authors: Faculty of Dept. of Radiology, Stony Brook University, New York, USA.
authors: Zhengrong
authors: Z
authors: Liang
keywords: 2.5D representation; Conformal mapping; colon wall; computed tomographic colonography; medical imaging
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