Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
Liver segmentation from computed tomography scans: A survey and a new algorithm
Artificial Intelligence in Medicine
3D α expansion and graph cut algorithms for automatic liver segmentation from CT images
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
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Liver segmentation in computerized tomography (CT) images has been widely studied in recent years, of which the graph cut models demonstrate a great potential with the advantage of global optima and practical efficiency. In this paper, a graph-cut based model for liver CT segmentation is presented. The image is interpreted as a graph, that the segmentation problem is then casted as an optimal cut on the graph. An energy function is then formulated for minimization, which combines both regional properties and boundary smoothness. The prior knowledge on liver is unified into the energy function via fuzzy similarity measure. Finally, the optimal cut can be computed through the max-flow algorithm. Experiments on a variety of CT images show its effectiveness and efficiency.