Abstract
Accurate 3D vessel segmentation remains challenging due to varying intensity contrast, high noise level, topological complexity and large extension area. In this paper, we propose an efficient graph-based method for 3D vessel segmentation with the help of oriented flux analysis and direction coherence, which work both in the graph construction and energy function formulation. To address the shrinking problem and seed sensitivity in conventional graph-based methods, new metrics based on hand-draft features are designed to encode vessel-dedicated information as prior probability into the optimization framework and to guide the segmentation towards elongated structures. Optimal vessel segmentation results can then be obtained with the random walker implementation efficiently. For evaluation, the proposed method is compared with classical random walker and region growing. We also conduct the comparison with a Hessian-enhanced graph-based method by providing the same graph construction and optimization strategy. The results demonstrate that our method performs better on both synthetic and real images and has higher robustness when the noise level increases.
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We would like to acknowledge the financial support of the Hong Kong Research Grants Council under grant 16203115.
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Zhang, Q., Chung, A.C.S. (2016). 3D Vessel Segmentation Using Random Walker with Oriented Flux Analysis and Direction Coherence. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_25
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DOI: https://doi.org/10.1007/978-3-319-43775-0_25
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