Elements of information theory
Elements of information theory
Active shape models—their training and application
Computer Vision and Image Understanding
Boundary Finding with Prior Shape and Smoothness Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elastic Model Based Non-rigid Registration Incorporation Statistical Shape Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Convex Optimization
Multiscale 3D shape analysis using spherical wavelets
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A novel 3d partitioned active shape model for segmentation of brain MR images
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Artificial enlargement of a training set for statistical shape models: application to cardiac images
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
Discriminative Shape Alignment
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Local regression based statistical model fitting
Proceedings of the 32nd DAGM conference on Pattern recognition
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A core part of many medical image segmentation techniques is the point distribution model, i.e., the landmark-based statistical shape model which describes the type of shapes under consideration. To build a proper model, that is flexible and generalizes well, one typically needs a large amount of landmarked training data, which can be hard to obtain. This problem becomes worse with increasing shape complexity and dimensionality. This work presents a novel methodology applicable to principal component-based shape model building and similar techniques. The main idea of the method is to make regular PCA shape modelling more flexible by usingmerely covariances between neighboring landmarks. The remaining unknown second order moments are determined using the maximum entropy principle based on which the full covariance matrix--as employed in the PCA--is determined using matrix completion. The method presented can be applied in a variety of situations and in conjunction with other technique facilitating model building. The experiments on point distributions demonstrate that improved shape models can be obtained using this localized maximum entropy modelling.