Computational Anatomy aims for the study of the statistical variability in anatomical structures. Variability is encoded by the transformations existing among populations of anatomical images. These transformations are usually computed from diffeomorphic registration based on the large deformation paradigm. In this framework diffeomorphisms are usually computed as end points of paths on the Riemannian manifold of diffeomorphisms parameterized by nonstationary vector fields. Recently, an alternative parameterization based on stationary vector fields has been developed. In this article we propose to use this stationary parameterization for diffeomorphic registration. We formulate the variational problem related to this registration scenario and derive the associated Euler-Lagrange equations. We evaluate the performance of the non-stationary vs the stationary parameterizations in real and synthetic 3D-MRI datasets. Compared to the non-stationary parameterization, our proposal provides similar accuracy in terms of image matching and deformation smoothness while drastically reducing memory and time requirements.
Hernandez, M, Bossa, MN & Olmos, S 2007, Registration of anatomical images using geodesic paths of diffeomorphisms parameterized with stationary vector fields. in 2007 IEEE 11th International Conference on Computer Vision. 2007 IEEE 11th International Conference on Computer Vision, ICCV, Rio de Janeiro, Brazil, 14/10/07. https://doi.org/10.1109/ICCV.2007.4409126
Hernandez, M., Bossa, M. N., & Olmos, S. (2007). Registration of anatomical images using geodesic paths of diffeomorphisms parameterized with stationary vector fields. In 2007 IEEE 11th International Conference on Computer Vision https://doi.org/10.1109/ICCV.2007.4409126
@inproceedings{553c52fd429d4d40af06c63aba19abe6,
title = "Registration of anatomical images using geodesic paths of diffeomorphisms parameterized with stationary vector fields",
abstract = "Computational Anatomy aims for the study of the statistical variability in anatomical structures. Variability is encoded by the transformations existing among populations of anatomical images. These transformations are usually computed from diffeomorphic registration based on the large deformation paradigm. In this framework diffeomorphisms are usually computed as end points of paths on the Riemannian manifold of diffeomorphisms parameterized by nonstationary vector fields. Recently, an alternative parameterization based on stationary vector fields has been developed. In this article we propose to use this stationary parameterization for diffeomorphic registration. We formulate the variational problem related to this registration scenario and derive the associated Euler-Lagrange equations. We evaluate the performance of the non-stationary vs the stationary parameterizations in real and synthetic 3D-MRI datasets. Compared to the non-stationary parameterization, our proposal provides similar accuracy in terms of image matching and deformation smoothness while drastically reducing memory and time requirements.",
author = "Monica Hernandez and Bossa, {Matias N.} and Salvador Olmos",
year = "2007",
month = dec,
day = "1",
doi = "10.1109/ICCV.2007.4409126",
language = "English",
booktitle = "2007 IEEE 11th International Conference on Computer Vision",
note = "2007 IEEE 11th International Conference on Computer Vision, ICCV ; Conference date: 14-10-2007 Through 21-10-2007",
}