期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 32, 期 2, 页码 376-386出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2012.2227120
关键词
Context prior; global optimization; graph search; image segmentation; optical coherence tomography (OCT); retina; shape prior
类别
资金
- National Science Foundation (NSF) [CCF-0830402, CCF-0844765]
- National Institutes of Health (NIH) [R01-EB004640, K25-CA123112]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0844765] Funding Source: National Science Foundation
Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 +/- 1.58 mu m) was improved to 5.14 +/- 0.99 mu m when employing our new method with shape and context priors.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据