A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Geodesic Shooting for Computational Anatomy
Journal of Mathematical Imaging and Vision
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Large Deformation Diffeomorphic Metric Curve Mapping
International Journal of Computer Vision
Spherical Demons: Fast Surface Registration
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Sparse Approximation of Currents for Statistics on Curves and Surfaces
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Anatomy-Preserving Nonlinear Registration of Deep Brain ROIs Using Confidence-Based Block-Matching
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Cortical sulcal atlas construction using a diffeomorphic mapping approach
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Optimized Conformal Surface Registration with Shape-based Landmark Matching
SIAM Journal on Imaging Sciences
Optimization of Surface Registrations Using Beltrami Holomorphic Flow
Journal of Scientific Computing
Current-Based 4d shape analysis for the mechanical personalization of heart models
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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In this paper we present a new way of measuring brain variability based on the registration of sulcal lines sets in the large deformation framework. Lines are modelled geometrically as currents, avoiding then matchings based on point correspondences. At the end we retrieve a globally consistent deformation of the underlying brain space that best matches the lines. Thanks to this framework the measured variability is defined everywhere whereas a previous method introduced by P. Fillard requires tensors extrapolation. Evaluating both methods on the same database, we show that our new approach enables to describe different details of the variability and to highlight the major trends of deformation in the database thanks to a Tangent-PCA analysis.