Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation
The segmentation of soft tissues in medical images is a challenging problem due to the weak boundary, large deformation and serious mutual influence. We present a novel method incorporating both the shape and appearance information in a 3-D graph-theoretic framework to overcome those difficulties for simultaneous segmentation of prostate and bladder. An arc-weighted graph is constructed corresponding to the initial mesh. Both the boundary and region information is incorporated into the graph with learned intensity distribution, which drives the mesh to the best fit of the image. A shape prior penalty is introduced by adding weighted-arcs in the graph, which maintains the original topology of the model and constraints the flexibility of the mesh. The surface-distance constraints are enforced to avoid the leakage between prostate and bladder. The target surfaces are found by solving a maximum flow problem in low-order polynomial time. Both qualitative and quantitative results on prostate and bladder segmentation were promising, proving the power of our algorithm
Qi Song Yinxiao Liu Yunlong Liu Punam K.Saha Milan Sonka Xiaodong Wu
Department of Electrical & Computer Engineering, Department of Electrical & Computer Engineering,Department of Radiation Oncology,Department of Ophth Department of Electrical & Computer Engineering, Department of Radiation Oncology,
国际会议
北京
英文
172–180
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）