Sparse Multi-Scale Diffeomorphic Registration: The Kernel Bundle Framework

Authors:
Stefan Sommer;François Lauze;Mads Nielsen;Xavier Pennec
Affiliations:
The Image Group, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark;The Image Group, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark;The Image Group, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark and BiomedIQ A/S, Copenhagen, Denmark;Asclepios Project-Team, INRIA Sophia-Antipolis, Sophia Antipolis, France
Venue:
Journal of Mathematical Imaging and Vision
Year:
2013

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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 allows multiple 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 framework with a compact representation reaches the same accuracy as the standard method optimally tuned with respect to scale.