Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Embedding Gestalt Laws in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Shape versus Size: Improved Understanding of the Morphology of Brain Structures
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Wavelets, statistics, and biomedical application
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Multiscale Medial Loci and Their Properties
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Deformable solid modeling via medial sampling and displacement subdivision
Deformable solid modeling via medial sampling and displacement subdivision
A Markov random field approach to multi-scale shape analysis
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Statistics of shape via principal geodesic analysis on lie groups
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A unifying approach to registration, segmentation, and intensity correction
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Estimating the statistics of multi-object anatomic geometry using inter-object relationships
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Shape based segmentation of anatomical structures in magnetic resonance images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Model Completion via Deformation Cloning Based on an Explicit Global Deformation Model
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Deep structure of images in populations via geometric models in populations
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
A multiple object geometric deformable model for image segmentation
Computer Vision and Image Understanding
Augmentation of paramedian 3D ultrasound images of the spine
IPCAI'13 Proceedings of the 4th international conference on Information Processing in Computer-Assisted Interventions
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The shape of a population of geometric entities is characterized by both the common geometry of the population and the variability among instances. In the deformable model approach, it is described by a probabilistic model on the deformations that transform a common template into various instances. To capture shape features at various scale levels, we have been developing an object-based multi-scale framework, in which a geometric entity is represented by a series of deformations with different locations and degrees of locality. Each deformation describes a residue from the information provided by previous steps. In this paper, we present how to build statistical shape models of multi-object complexes with such properties based on medial representations and explain how this may lead to more effective shape descriptions as well as more efficient statistical training procedures. We illustrate these ideas with a statistical shape model for a pair of pubic bones and show some preliminary results on using it as a prior in medical image segmentation.