Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Shape Detection and Description via Model-Based Region Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Shape Recognition with Mixtures of Point Distribution Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Non-linear Local Registration of Functional Data
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Deformable Modeling for Characterizing Biomedical Shape Changes
DGCI '00 Proceedings of the 9th International Conference on Discrete Geometry for Computer Imagery
Active blobs: region-based, deformable appearance models
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Morphing of image represented objects using a physical methodology
Proceedings of the 2004 ACM symposium on Applied computing
Symbolic Signatures for Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lung nodule detection via Bayesian voxel labeling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Structural shape characterization via exploratory factor analysis
Artificial Intelligence in Medicine
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We present a framework for analyzing the shape deformation of structures within the human brain. A mathematical model is developed describing the deformation of any brain structure whose shape is affected by both gross and detailed physical processes. Using our technique, the total shape deformation is decomposed into analytic modes of variation obtained from finite element modeling, and statistical modes of variation obtained from sample data. Our method is general, and can be applied to many problems where the goal is to separate out important from unimportant shape variation across a class of objects. In this paper, we focus on the analysis of diseases that affect the shape of brain structures. Because the shape of these structures is affected not only by pathology but also by overall brain shape, disease discrimination is difficult. By modeling the brain's elastic properties, we are able to compensate for some of the nonpathological modes of shape variation. This allows us to experimentally characterize modes of variation that are indicative of disease processes. We apply our technique to magnetic resonance images of the brains of individuals with schizophrenia, Alzheimer's disease, and normal-pressure hydrocephalus, as well as to healthy volunteers. Classification results are presented.