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Spatially-Varying Metric Learning for Diffeomorphic Image Registration: A Variational Framework

  • François-Xavier Vialard
  • Laurent Risser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

This paper introduces a variational strategy to learn spatially-varying metrics on large groups of images, in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. Spatially-varying metrics we learn not only favor local deformations but also correlated deformations in different image regions and in different directions. In addition, metric parameters can be efficiently estimated using a gradient descent method. We first describe the general strategy and then show how to use it on 3D medical images with reasonable computational ressources. Our method is assessed on the 3D brain images of the LPBA40 dataset. Results are compared with ANTS-SyN and LDDMM with spatially-homogeneous metrics.

Keywords

Image Registration Dimensionality Reduction Method Grid Step Size Simple Gradient Descent Target Overlap 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • François-Xavier Vialard
    • 1
  • Laurent Risser
    • 2
  1. 1.Université Paris Dauphine, CEREMADE (UMR 7534)France
  2. 2.CNRS, Institut de Mathématiques de Toulouse (UMR 5219)France

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