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
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
Image Segmentation by Branch-and-Mincut
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-driven interactive 3D medical image segmentation based on structured patch model
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We propose a fully automatic method for segmenting knee cartilage in 3-D MR images which consists of bone segmentation, bone-cartilage interface (BCI) classification, and cartilage segmentation. For bone segmentation, we propose a modified version of the recently presented branch-and-mincut method, and for classifying the BCI, we propose a voxel classification method based on binary classifiers of position and local appearance. The core contribution of this paper is the cartilage segmentation method where localized Markov random fields (MRF) are separately constructed and optimized for local image patches. The region and boundary potentials of the MRFs are computed from the retrieved segmentation results of training images that are relevant to each local patch. Here, local shape and appearance cues are adaptively combined depending on the local image characteristics. For experimentation, a dataset comprising MR images of ten different subjects and another comprising the baseline and two-year follow-up scans for nine different subjects are constructed. Both qualitative and quantitative comparisons of the results of the proposed method with semi-automatic segmentation methods demonstrate the potential of the proposed method for clinical application.