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A new fractional order derivative based active contour model for colon wall segmentation
Author(s): Bo Chen; Lihong C. Li; Huafeng Wang; Xinzhou Wei; Shan Huang; Wensheng Chen; Zhengrong Liang
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Paper Abstract

Segmentation of colon wall plays an important role in advancing computed tomographic colonography (CTC) toward a screening modality. Due to the low contrast of CT attenuation around colon wall, accurate segmentation of the boundary of both inner and outer wall is very challenging. In this paper, based on the geodesic active contour model, we develop a new model for colon wall segmentation. First, tagged materials in CTC images were automatically removed via a partial volume (PV) based electronic colon cleansing (ECC) strategy. We then present a new fractional order derivative based active contour model to segment the volumetric colon wall from the cleansed CTC images. In this model, the regionbased Chan-Vese model is incorporated as an energy term to the whole model so that not only edge/gradient information but also region/volume information is taken into account in the segmentation process. Furthermore, a fractional order differentiation derivative energy term is also developed in the new model to preserve the low frequency information and improve the noise immunity of the new segmentation model. The proposed colon wall segmentation approach was validated on 16 patient CTC scans. Experimental results indicate that the present scheme is very promising towards automatically segmenting colon wall, thus facilitating computer aided detection of initial colonic polyp candidates via CTC.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057517 (27 February 2018); doi: 10.1117/12.2293677
Show Author Affiliations
Bo Chen, Stony Brook Univ. (United States)
Shenzhen Univ. (China)
Lihong C. Li, College of Staten Island (United States)
Huafeng Wang, North China Univ. of Technology (China)
Xinzhou Wei, New York City College of Technology (United States)
Shan Huang, Shenzhen Univ. (China)
Wensheng Chen, Shenzhen Univ. (China)
Zhengrong Liang, State Univ. of New York (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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