Active shape models—their training and application
Computer Vision and Image Understanding
Shape modeling and analysis with entropy-based particle systems
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Automatic learning sparse correspondences for initialising groupwise registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Deformable segmentation via sparse shape representation
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A comparison study of inferences on graphical model for registering surface model to 3D image
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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In this article, the authors demonstrate that you can improve the performance of the registration of a point distribution model (PDM) by accurately estimating the structure of an undirected graphical model that represents the statistical shape model (SSM) of a target surface. Many existing methods for constructing SSMs determine the structure of the graphical model without analyzing the conditional dependencies among the points in PDM, though an edge in the PDM should link two nodes if and only if they are conditionally dependent. In this study, the authors employed four popular methods for estimating the structure of graphical model and obtained four different SSMs from an identical set of training surfaces. The registration performances of the SSMs were experimentally compared, and the results showed that the graphical lasso, which could estimate more accurate structure of the graphical model by avoiding the overfitting to the training data, outperformed the other methods.