Variational problems on flows of diffeomorphisms for image matching
Quarterly of Applied Mathematics
Group Actions, Homeomorphisms, and Matching: A General Framework
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
International Journal of Computer Vision
Large deformation diffeomorphic registration using fine and coarse strategies
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
A log-euclidean framework for statistics on diffeomorphisms
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Diffeomorphic 3D Image Registration via Geodesic Shooting Using an Efficient Adjoint Calculation
International Journal of Computer Vision
Kernel bundle EPDiff: evolution equations for multi-scale diffeomorphic image registration
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Sparse Multi-Scale Diffeomorphic Registration: The Kernel Bundle Framework
Journal of Mathematical Imaging and Vision
Flexible Shape Matching with Finite Element Based LDDMM
International Journal of Computer Vision
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In this paper, we present a fine and coarse approach for the multiscale registration of 3D medical images using Large Deformation Diffeomorphic Metric Mapping (LDDMM). This approach has particularly interesting properties since it estimates large, smooth and invertible optimal deformations having a rich descriptive power for the quantification of temporal changes in the images. First, we show the importance of the smoothing kernel and its influence on the final solution. We then propose a new strategy for the spatial regularization of the deformations, which uses simultaneously fine and coarse smoothing kernels. We have evaluated the approach on both 2D synthetic images as well as on 3D MR longitudinal images out of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Results highlight the regularizing properties of our approach for the registration of complex shapes. More importantly, the results also demonstrate its ability to measure shape variations at several scales simultaneously while keeping the desirable properties of LDDMM. This opens new perspectives for clinical applications.