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Proceedings Paper

A fuzzy clustering vessel segmentation method incorporating line-direction information
Author(s): Zhimin Wang; Wei Xiong; Weimin Huang; Jiayin Zhou; Sudhakar K. Venkatesh
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Paper Abstract

A data clustering based vessel segmentation method is proposed for automatic liver vasculature segmentation in CT images. It consists of a novel similarity measure which incorporates the spatial context, vesselness information and line-direction information in a unique way. By combining the line-direction information and spatial information into the data clustering process, the proposed method is able to take care of the fine details of the vessel tree and suppress the image noise and artifacts at the same time. The proposed algorithm has been evaluated on the real clinical contrast-enhanced CT images, and achieved excellent segmentation accuracy without any experimentally set parameters.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143I (14 February 2012); doi: 10.1117/12.919106
Show Author Affiliations
Zhimin Wang, A*STAR Institute for Infocomm Research (Singapore)
Wei Xiong, A*STAR Institute for Infocomm Research (Singapore)
Weimin Huang, A*STAR Institute for Infocomm Research (Singapore)
Jiayin Zhou, A*STAR Institute for Infocomm Research (Singapore)
Sudhakar K. Venkatesh, National Univ. of Singapore School of Medicine (Singapore)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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