Image-flow computation: an estimation-theoretic framework and a unified perspective
CVGIP: Image Understanding
Spline-Based Image Registration
International Journal of Computer Vision
A non-rigid registration algorithm for dynamic breast MR images
Artificial Intelligence - Special issue on applications of artificial intelligence
Image Registration Using Wavelet-Based Motion Model
International Journal of Computer Vision
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Fast parametric elastic image registration
IEEE Transactions on Image Processing
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With the growing acceptance of nonrigid registration as a useful tool to perform clinical research, and in particular group studies, the storage space needed to hold the resulting transforms is deemed to become a concern for vector field based approaches, on top of the traditional computation time issue. In a recent study we lead, which involved the registration of more than 22,000 pairs of T1 MR volumes, this constrain appeared critical indeed. In this paper, we propose to decompose the vector field on a wavelet basis, and let the registration algorithm minimize the number of non-zero coefficients by introducing an L1 penalty. This enables a sparse representation of the vector field which, unlike parametric representations, does not confine the estimated transform into a small parametric space with a fixed uniform smoothness : nonzero wavelet coefficients are optimally distributed depending on the data. Furthermore, we show that the iconic feature registration framework allows to embed the non-differentiable L1 penalty into a C1 energy that can be efficiently minimized by standard optimization techniques.