Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformation Analysis for Shape Based Classification
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A Minimum Description Length Approach to Statistical Shape Modelling
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
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
Fast Linear Elastic Matching Without Landmarks
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
Discriminative Analysis for Image-Based Studies
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A surface-based approach for classification of 3D neuroanatomic structures
Intelligent Data Analysis
Improving the reliability of shape comparison by perturbation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Heat diffusion based dissimilarity analysis for schizophrenia classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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Shape comparisons of two groups of objects often have two goals: to create a classifier to separate the groups and to provide information that shows differences between classes. We examine issues that are important for shape analysis in a study comparing schizophrenic patients to normal subjects. For this study, non-linear classifiers provide large accuracy gains over linear ones. Using volume information directly in the classifier provides gains over a classifier that normalizes the data for volume. We compare two different representations of shape: displacement fields and distance maps. We show that the classifier based on displacement fields outperforms the one based on distance maps. We also show that displacement fields provide more information in visualizing shape differences than distance maps.