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An Adaptive Multiscale Similarity Measure for Non-rigid Registration

  • Veronika A. Zimmer
  • Gemma Piella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8545)

Abstract

Popular intensity-based similarity measures such as (normalized) mutual information estimate statistics over the entire image, neglecting spatial relationships and local image properties. In this work, we present an adaptive multiscale image similarity measure for non-rigid registration which combines image statistics at multiple scales for a multiscale representation of regional image similarities. We validated the proposed similarity measure on simulated and clinical MR brain datasets. Results show that our approach achieves higher registration accuracy and robustness than conventional global measures or their local variations at a single scale.

Keywords

Similarity Measure Mutual Information Image Registration Normalize Mutual Information Medical Image Registration 
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

  • Veronika A. Zimmer
    • 1
  • Gemma Piella
    • 1
  1. 1.Simulation, Imaging and Modelling in Biomedical Systems (SIMBioSys), Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain

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