Active shape models—their training and application
Computer Vision and Image Understanding
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Intuitive, Localized Analysis of Shape Variability
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Medial Models Incorporating Object Variability for 3D Shape Analysis
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Hybrid Boundary-Medial Shape Description for Biologically Variable Shapes
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Registration Assisted Image Smoothing and Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Discriminative Analysis for Image-Based Studies
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Gyral Parcellation of the Cortical Surface Using Geodesic Voronoï Diagrams
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Performance Issues in Shape Classification
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Kernel Fisher for Shape Based Classification in Epilepsy
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Automatic and Robust Computation of 3D Medial Models Incorporating Object Variability
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
A multivariate statistical analysis of the developing human brain in preterm infants
Image and Vision Computing
Statistical Multi-Object Shape Models
International Journal of Computer Vision
A surface-based approach for classification of 3D neuroanatomic structures
Intelligent Data Analysis
Principal Geodesic Analysis for the Study of Nonlinear Minimum Description Length
Medical Imaging and Informatics
Fast image registration by hierarchical soft correspondence detection
Pattern Recognition
Technical Section: Fourier method for large-scale surface modeling and registration
Computers and Graphics
Probabilistic models for shapes as continuous curves
Journal of Mathematical Imaging and Vision
Feature-based fusion of medical imaging data
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Local Kernel for Brains Classification in Schizophrenia
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
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
Lung nodule detection via Bayesian voxel labeling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Heat diffusion based dissimilarity analysis for schizophrenia classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Surface alignment of 3d spherical harmonic models: application to cardiac MRI analysis
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A prediction framework for cardiac resynchronization therapy via 4d cardiac motion analysis
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Image and Vision Computing
Map-based exploration of intrinsic shape differences and variability
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Computer Methods and Programs in Biomedicine
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Standard practice in quantitative structural neuroimaging is a segmentation into bram tissue, subcortical structures, fluid space and lesions followed by volume calculations of gross structures. On the other hand, it is evident that object characterization by size does only capture one of multiple aspects of a full structural characterization. Desirable parameters are local and global parameters like length, elongation, bending, width, complexity, bumpiness and many more. In neuroimaging research there is increasing evidence that shape analysis of brain structures provides new information which is not available by conventional volumetric measurements. This motivates development of novel morphometry analysis techniques answering clinical research questions which have been asked for a long time but which remained unanswered due to the lack of appropriate measurement tools. Challenges are the choice of biologically meaningful shape representations, robustness to noise and small perturbations, and the ability to capture the shape properties of populations that represent natural biological shape variation. This paper describes experiments with two different shape representation schemes, a fine-scale, global surface characterization using spherical harmonics, and a coarsely sampled medial representation (3D skeleton). Driving applications are the detection of group differences of amhygdala-hippocampal shapes in schizophrenia and the analysis of ventricular shape similarity in a mono/dizygotic twin study. The results clearly demonstrate that shape captures information on structural similarity or difference which is not accessible by volume analysis. Improved global and local structure characterization as proposed herein might help to explain pathological changes in neurodevelopment/neurodegeneration in terms of their biological meaning.