Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Qi Song; Yinxiao Liu; Yunlong Liu; Punam K Saha; Milan Sonka; Xiaodong Wu

doi:10.1007/978-3-642-15711-0_22

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Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Book chapter

Open access

Peer reviewed

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Qi Song, Yinxiao Liu, Yunlong Liu, Punam K Saha, Milan Sonka and Xiaodong Wu

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, pp.172-180

Lecture Notes in Computer Science, Springer Berlin Heidelberg

2010

DOI: 10.1007/978-3-642-15711-0_22

PMID: 20879397

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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.

Appearance Information

Kernel Principal Component Analysis

Neighboring Column

Shape Constraint

Simultaneous Segmentation

Details

Title: Subtitle: Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation
Creators: Qi Song - University of Iowa
Yinxiao Liu - University of Iowa
Yunlong Liu - University of Iowa
Punam K Saha - University of Iowa
Milan Sonka - University of Iowa
Xiaodong Wu - University of Iowa
Resource Type: Book chapter
Publication Details: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, pp.172-180
Series: Lecture Notes in Computer Science
DOI: 10.1007/978-3-642-15711-0_22
PMID: 20879397
eISSN: 1611-3349
ISSN: 0302-9743
Publisher: Springer Berlin Heidelberg; Berlin, Heidelberg
Language: English
Date published: 2010
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984186700502771

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