Diffeomorphic image matching with left-invariant metrics
Abstract
The geometric approach to diffeomorphic image registration known as "large deformation by diffeomorphic metric mapping" (LDDMM) is based on a left action of diffeomorphisms on images, and a right-invariant metric on a diffeomorphism group, usually defined using a reproducing kernel. We explore the use of left-invariant metrics on diffeomorphism groups, based on reproducing kernels defined in the body coordinates of a source image. This perspective, which we call Left-LDM, allows us to consider non-isotropic spatially-varying kernels, which can be interpreted as describing variable deformability of the source image. We also show a simple relationship between LDDMM and the new approach, implying that spatially-varying kernels are interpretable in the same way in LDDMM. We conclude with a discussion of a class of kernels that enforce a soft mirror-symmetry constraint, which we validate in numerical experiments on a model of a lesioned brain.
Cite
@article{arxiv.1401.3609,
title = {Diffeomorphic image matching with left-invariant metrics},
author = {Tanya Schmah and Laurent Risser and François-Xavier Vialard},
journal= {arXiv preprint arXiv:1401.3609},
year = {2014}
}
Comments
19 pages, 4 figures