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Random Walks with Adaptive Cylinder Flux Based Connectivity for Vessel Segmentation
Ning Zhu and Albert C.S. Chung
Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kongnzhu@cse.ust.hk
achung@cse.ust.hk
Abstract. In this paper, we present a novel graph-based method for segmenting the whole 3D vessel tree structures. Our method exploits a new adaptive cylinder flux (ACF) based connectivity framework, which is formulated based on random walks [8]. To avoid the shrinking problem of elongated structure, all existing graph-based energy optimization methods for vessel segmentation rely on skeleton or ROI extraction. As a result, the performance of these vessel segmentation methods then depends heavily on the skeleton extraction results. In this paper, with the help of ACF based connectivity framework, a global optimal segmentation result can be obtained without extracting skeleton or ROI. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved effectively with the proposed method thanks to the connectivity framework.
LNCS 8150, p. 550 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013