Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spline-Based Image Registration
International Journal of Computer Vision
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Isotropic Energies, Filters and Splines for Vector Field Regularization
Journal of Mathematical Imaging and Vision
A New Class of Elastic Body Splines for Nonrigid Registration of Medical Images
Journal of Mathematical Imaging and Vision
Diffeomorphic nonlinear transformations: a local parametric approach for image registration
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Landmark matching via large deformation diffeomorphisms
IEEE Transactions on Image Processing
Fast parametric elastic image registration
IEEE Transactions on Image Processing
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
A robust hybrid method for nonrigid image registration
Pattern Recognition
SUPIR: surface uncertainty-penalized, non-rigid image registration for pelvic CT imaging
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
International Journal of Computer Vision
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We introduce a new approximation approach for landmark-based elastic image registration using Gaussian elastic body splines (GEBS). We formulate an extended energy functional related to the Navier equation under Gaussian forces which allows to individually weight the landmarks according to their localization uncertainties. These uncertainties are characterized either by scalar weights or by weight matrices representing isotropic or anisotropic errors. Since the approach is based on a physical deformation model, cross-effects in elastic deformations can be taken into account. Moreover, with Gaussian forces we have a free parameter to control the locality of the transformation for improved registration of local geometric image differences. We demonstrate the applicability of our scheme based on analytic experiments, 3D CT images from the Truth Cube experiment, as well as 2D MR images of the brain. From the experiments it turned out that the new approximating GEBS approach achieves more accurate registration results in comparison to previously proposed interpolating GEBS as well as interpolating and approximating TPS.