A fuzzy clustering vessel segmentation method incorporating line-direction information

Zhimin Wang; Wei Xiong; Weimin Huang; Jiayin Zhou; Sudhakar K. Venkatesh

doi:10.1117/12.919106

14 February 2012 A fuzzy clustering vessel segmentation method incorporating line-direction information

Zhimin Wang, Wei Xiong, Weimin Huang, Jiayin Zhou, Sudhakar K. Venkatesh

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83143I (2012) https://doi.org/10.1117/12.919106
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Zhimin Wang, Wei Xiong, Weimin Huang, Jiayin Zhou, and Sudhakar K. Venkatesh "A fuzzy clustering vessel segmentation method incorporating line-direction information", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143I (14 February 2012); https://doi.org/10.1117/12.919106

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