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. © 2013 Springer-Verlag.
CITATION STYLE
Schmah, T., Risser, L., & Vialard, F. X. (2013). Left-invariant metrics for diffeomorphic image registration with spatially-varying regularisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8149 LNCS, pp. 203–210). https://doi.org/10.1007/978-3-642-40811-3_26
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