A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Active shape models—their training and application
Computer Vision and Image Understanding
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Computer Graphics and Applications
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
Segmentation of sub-cortical structures by the graph-shifts algorithm
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Efficient kernel density estimation of shape and intensity priors for level set segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
3D active shape model segmentation with nonlinear shape priors
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Computer Vision and Image Understanding
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Human brain labeling using image similarities
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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A suitable object model is crucial in guiding an object segmentation method of three-dimensional medical images to avoid difficulties such as complex object structures, inter-subject variability and ambiguous boundaries between organs. The main challenge is to make the model sufficiently complex to represent a wide range of variations effectively, while maintaining compatibility with the segmentation methodology. To address this problem, we propose a new segmentation method based on a hierarchical Markov random field (H-MRF). The H-MRF is composed of local-level MRFs based on adaptive local priors which model local variations of shape and appearance and a global-level MRF enforcing consistency of the local-level MRFs. The proposed method can successfully model large object variations and weak boundaries and is readily combined with well-established MRF optimization techniques. Furthermore, it works well with limited training data and does not require a complex training model or non-rigid registration. The performance of the proposed method is evaluated for bone and cartilage from knee magnetic resonance (MR) images, the liver from body computed tomography images, and the hippocampus from brain MR images. Both qualitative and quantitative evaluations demonstrate that the proposed method provides robust and accurate segmentation results.