Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation

Ning Zhu, Albert C.S. Chung; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2219-2226

Abstract


In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT ), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low computational cost. Different with other graph-based vessel segmentation methods, the proposed method does not depend on any skeleton and ROI extraction method. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved with the proposed method thanks to vessel data fidelity obtained with TMT . The proposed method is compared with some classical graph-based image segmentation methods and two up-to-date 3D vessel segmentation methods, and is demonstrated to be more accurate than these methods for 3D vessel tree segmentation. Although the segmentation is done without ROI extraction, the computational cost for the proposed method is low (within 20 seconds for 256*256*144 image).

Related Material


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[bibtex]
@InProceedings{Zhu_2013_CVPR,
author = {Zhu, Ning and Chung, Albert C.S.},
title = {Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}