Active shape models—their training and application
Computer Vision and Image Understanding
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Semiautomatic segmentation with compact shape prior
Image and Vision Computing
Star Shape Prior for Graph-Cut Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Probabilistic Modeling and Visualization of the Flexibility in Morphable Models
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
Curvature guided level set registration using adaptive finite elements
Proceedings of the 29th DAGM conference on Pattern recognition
Graph cuts framework for kidney segmentation with prior shape constraints
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Sparse shape composition: A new framework for shape prior modeling
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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This paper proposes a novel segmentation method combining shape knowledge obtained from a constrained Statistical Model (SM) into the well known Markov Random Field (MRF) segmentation framework. The employed SM based on Probabilistic Principal Component Analysis (PPCA) allows to compute local information about the remaining variance i.e. uncertainty about the correct segmentation boundary. This knowledge about the local segmentation uncertainty is then used to construct a prior with a non-linear shape update mechanism, where a high cost is incurred in locations with little uncertainty and a low cost for shifting the segmentation boundary in locations with high uncertainty. Experimental results for segmenting the masseter muscle from CT data are presented showing the advantage of including the knowledge about local segmentation uncertainties into the segmentation framework.