An Energy-Based Framework for Dense 3D Registration of Volumetric Brain Images
Abstract
In this paper we describe a new method for medical image registration. The registration is formulated as a minimization problem involving robust estimators. We propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. Furthermore we introduce a methodology to constrain the registration with landmarks such as anatomical structures. The performances of this method are objectively evaluated on simulated data and its benefits are demonstrated on a large database of real acquisitions.
Cite
Text
Hellier et al. "An Energy-Based Framework for Dense 3D Registration of Volumetric Brain Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000. doi:10.1109/CVPR.2000.854805Markdown
[Hellier et al. "An Energy-Based Framework for Dense 3D Registration of Volumetric Brain Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000.](https://mlanthology.org/cvpr/2000/hellier2000cvpr-energy/) doi:10.1109/CVPR.2000.854805BibTeX
@inproceedings{hellier2000cvpr-energy,
title = {{An Energy-Based Framework for Dense 3D Registration of Volumetric Brain Images}},
author = {Hellier, Pierre and Barillot, Christian and Mémin, Étienne and Pérez, Patrick},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2000},
pages = {2270-2275},
doi = {10.1109/CVPR.2000.854805},
url = {https://mlanthology.org/cvpr/2000/hellier2000cvpr-energy/}
}