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. © 2013 Springer-Verlag.
CITATION STYLE
Zhu, N., & Chung, A. C. S. (2013). Random walks with adaptive cylinder flux based connectivity for vessel segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8150 LNCS, pp. 550–558). https://doi.org/10.1007/978-3-642-40763-5_68
Mendeley helps you to discover research relevant for your work.