Left-Invariant Metrics for Diffeomorphic Image Registration with Spatially-Varying Regularisation

Tanya Schmah¹, Laurent Risser², and François-Xavier Vialard³

¹Rotman Research Institute, Baycrest, Toronto, Canada

²CNRS, Institut de Mathématiques de Toulouse (UMR 5219), France

³CEREMADE (UMR 7534), Université Paris Dauphine, France

Abstract. We present a new framework for diffeomorphic image registration which supports natural interpretations of spatially-varying metrics. This framework is based on left-invariant diffeomorphic metrics (LIDM) and is closely related to the now standard large deformation diffeomorphic metric mapping (LDDMM). We discuss the relationship between LIDM and LDDMM and introduce a computationally convenient class of spatially-varying metrics appropriate for both frameworks. Finally, we demonstrate the effectiveness of our method on a 2D toy example and on the 40 3D brain images of the LPBA40 dataset.

LNCS 8149, p. 203 ff.

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