Sparse Multi-Scale Diffeomorphic Registration: the Kernel Bundle Framework - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Journal of Mathematical Imaging and Vision Année : 2013

Sparse Multi-Scale Diffeomorphic Registration: the Kernel Bundle Framework

Résumé

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.
Fichier principal
Vignette du fichier
Sommer_JMIV2013.pdf (1.27 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00813868 , version 1 (18-04-2013)

Identifiants

Citer

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

Altmetric

Partager

Gmail Facebook X LinkedIn More