Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
Probabilistic matching of deformed images
Probabilistic matching of deformed images
Spatial transformation and registration of brain images using elastically deformable models
Computer Vision and Image Understanding
Dynamic Programming Generation of Curves on Brain Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Prior Shape and Smoothness Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Versatile Support for Binding Native Code to Java
HPCN Europe 2000 Proceedings of the 8th International Conference on High-Performance Computing and Networking
Hierarchical Matching of Cortical Features for Deformable Brain Image Registration
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Registration of Cortical Anatomical Structures via Robust 3D Point Matching
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A New Approach to 3D Sulcal Ribbon Finding from MR Images
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Non-linear Cerebral Registration with Sulcal Constraints
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Finding 3D Parametric Representations of the Deep Cortical Folds
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Landmark matching via large deformation diffeomorphisms
IEEE Transactions on Image Processing
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This paper describes the design, implementation and preliminary results of a unified non-rigid feature registration method for the purpose of brain anatomical structure alignment. We combine different types of features together and fuse them into a common point representation. This enables the co-registration of all features using a new non-rigid point matching algorithm. In this way, the spatial interrelationships between different features are directly utilized to improve the registration accuracy. We also conducted a carefully designed synthetic study to compare some anatomical features' ability for non-rigid brain structure alignment. This study allows us to evaluate the relative improvements in registration accuracy when different features are combined.