Paper Info
Title
Graph search with appearance and shape information for 3-D prostate and bladder segmentation.
Abstract
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15711-0_22
MICCAI (3)
Keywords
Field
DocType
shape prior penalty,shape information,region information,graph search,arc-weighted graph,simultaneous segmentation,maximum flow problem,challenging problem,appearance information,3-d prostate,weak boundary,bladder segmentation,initial mesh
Graph,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Kernel principal component analysis,Artificial intelligence,Prostate,Maximum flow problem,Time complexity
Conference
Volume
Issue
ISSN
13
Pt 3
0302-9743
ISBN
Citations 
PageRank 
3-642-15710-6
8
0.73
References 
Authors
16
6
Name
Order
Citations
PageRank
Qi Song1635.44
Yinxiao Liu2405.75
Yunlong Liu3332.42
Punam K. Saha41477120.91
Milan Sonka52889254.20
Xiaodong Wu685977.06