Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Injectivity conditions of 2D and 3D uniform cubic B-spline functions
Graphical Models - Pacific Graphics '99 in Graphical Models
Landmark matching via large deformation diffeomorphisms
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration
Journal of Mathematical Imaging and Vision
Digital homeomorphisms in deformable registration
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Topology preservation evaluation of compact-support radial basis functions for image registration
Pattern Recognition Letters
A log-euclidean polyaffine framework for locally rigid or affine registration
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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Many types of transformations are used to model deformations in medical image registration. While some focus on modeling local changes, some on continuity and invertibility, there is no closed-form nonlinear parametric approach that addresses all these properties. This paper presents a class of nonlinear transformations that are local, continuous and invertible under certain conditions. They are straightforward to implement, fast to compute and can be used particularly in cases where locally affine deformations need to be recovered. We use our new transformation model to demonstrate some results on synthetic images using a multi-scale approach to multi-modality mutual information based image registration. The original images were deformed using B-splines at three levels of scale. The results show that the proposed method can recover these deformations almost completely with very few iterations of a gradient based optimizer.