Sparse Multi-Scale Diffeomorphic Registration: the Kernel Bundle Framework

Abstract : In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation compactly at the relevant scales only. This paper presents the kernel bundle extension of the LDDMM framework that allowsmultiple kernels at multiple scales to be incorporated in the registration. We combine sparsity priors with the kernel bundle resulting in compact representations across scales, and we present the mathematical foundation of the framework with derivation of the KB-EPDiff evolution equations. Through examples, we illustrate the influence of the kernel scale and show that the method achieves the important property of sparsity across scales. In addition, we demonstrate on a dataset of annotated lung CT images how the kernel bundle frameworkwith a compact representation reaches the same accuracy as the standard method optimally tuned with respect to scale.
Liste complète des métadonnées

Littérature citée [23 références]  Voir  Masquer  Télécharger
Contributeur : Project-Team Asclepios <>
Soumis le : jeudi 18 avril 2013 - 15:12:51
Dernière modification le : jeudi 7 février 2019 - 15:25:44
Document(s) archivé(s) le : vendredi 19 juillet 2013 - 02:55:09


Fichiers produits par l'(les) auteur(s)




Stefan Sommer, François Lauze, Mads Nielsen, Xavier Pennec. Sparse Multi-Scale Diffeomorphic Registration: the Kernel Bundle Framework. Journal of Mathematical Imaging and Vision, Springer Verlag, 2013, 46 (3), pp.292-308. 〈10.1007/s10851-012-0409-0〉. 〈hal-00813868〉



Consultations de la notice


Téléchargements de fichiers