Active shape models—their training and application
Computer Vision and Image Understanding
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)
Determining the Geometry of Boundaries of Objects from Medial Data
International Journal of Computer Vision
Multi-figure anatomical objects for shape statistics
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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
Statistical Multi-Object Shape Models
International Journal of Computer Vision
Particle-Based Shape Analysis of Multi-object Complexes
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Joint sulci detection using graphical models and boosted priors
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Multi-figure anatomical objects for shape statistics
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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
Histogram statistics of local model-relative image regions
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
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We present a methodology for estimating the probability of multi-object anatomic complexes that reflects both the individual objects' variability and the variability of the inter-relationships between objects. The method is based on m-reps and the idea of augmenting medial atoms from one object's m-rep to the set of atoms of an object being described. We describe the training of these probabilities, and we present an example of calculating the statistics of the bladder, prostate, rectum complex in the male pelvis. Via examples from the real world and from Monte-Carlo simulation, we show that this means of representing multi-object statistics yields samples that are nearly geometrically proper and means and principal modes of variations that are intuitively reasonable.