Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Statistical Computing on Manifolds: From Riemannian Geometry to Computational Anatomy
Emerging Trends in Visual Computing
Asymmetric Image-Template Registration
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Contributions to 3D diffeomorphic atlas estimation: application to brain images
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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
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Diffusion tensor imaging provides information about deep white matter anatomy that structural magnetic resonance images typically fail to resolve. Non-linear registration of diffusion tensor images, for which a few methods already exist, allows us to capture the deformations of these structures that would otherwise go unobserved. Here, we build on an existing method for diffeomorphic registration of diffusion tensor images, so that it fully incorporates the useful log-domain parameterization of diffeomorphisms. Initially, this allows us to easily include a registration symmetry constraint that is highly desirable for pair-wise registration. More importantly, the parameterization allows simple and proper calculation of statistics on the transformations obtained. We show that the symmetric log-domain method exhibits the most preferable trade-off between image correspondence and deformation smoothness on real data and also achieves the best recovery of synthetic warps.