Active shape models—their training and application
Computer Vision and Image Understanding
Skin: a constructive approach to modeling free-form shapes
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Teddy: a sketching interface for 3D freeform design
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Segmentation of Medical Image Objects Using Deformable Shape Loci
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Skeletal Methods of Shape Manipulation
SMI '99 Proceedings of the International Conference on Shape Modeling and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Three-dimensional shape description using the symmetric axis transform
Three-dimensional shape description using the symmetric axis transform
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)
Medial Models Incorporating Object Variability for 3D Shape Analysis
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Segmentation of Single-Figure Objects by Deformable M-reps
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
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This paper presents a Bayesian multi-scale three dimensional deformable template approach based on a medial representation for the segmentation and shape characterization of anatomical objects in medical imagery. Prior information about the geometry and shape of the anatomical objects under study is incorporated via the construction of exemplary templates. The anatomical variability is accommodated in the Bayesian framework by defining probabilistic transformations on these templates. The modeling approach taken in this paper for building exemplary templates and associated transformations is based on a multi-scale medial representation. The transformations defined in this framework are parameterized directly in terms of natural shape operations, such as thickening and bending, and their location. Quantitative validation results are presented on the automatic segmentation procedure developed for the extraction of the kidney parenchyma-including the renal pelvis-in subjects undergoing radiation treatment for cancer. We show that the segmentation procedure developed in this paper is efficient and accurate to within the voxel resolution of the imaging modality.