Optimal and efficient segmentation for 3D vascular forest structure with graph cuts
In this paper, we propose an optimal segmentation method for vascular forest structure based on graph cuts framework, which has widely been used in recent years because of its global optimal object segmentation property. However, shrinking bias, a classical issue of the graph cuts methods, sets up a barrier for the use of these methods on elongated structures such as blood vessels, especially the complex vascular tree and forest structures. To deal with this problem, a new graph construction method and a new energy function are proposed in this paper. The global optimal segmentation of vascular forest structure can be obtained more efficiently, while the shrinking bias can be overcome by the proposed method. The method is compared with a classical graph cuts method [1] and two methods [2, 3] for vascular tree structure segmentation, and is demonstrated to be more accurate on both the synthetic and clinical images, especially on noisy images. Different from many other tree structure segmentation methods, the proposed method does not have to consider the bifurcations explicitly.