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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 91–98Cite as

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Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric

Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric

  • Hassan Rivaz19 &
  • D. Louis Collins19 
  • Conference paper
  • 4545 Accesses

  • 8 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

Extending mutual information (MI), which has been widely used as a similarity measure for rigid registration of multi-modal images, to deformable registration is an active field of research. We propose a self-similarity weighted graph-based implementation of α-mutual information (α-MI) for nonrigid image registration. The new Self Similarity \(\underline \alpha\)-MI (SeSaMI) metric takes local structures into account and is robust against signal non-stationarity and intensity distortions. We have used SeSaMI as the similarity measure in a regularized cost function with B-spline deformation field. Since the gradient of SeSaMI can be derived analytically, the cost function can be efficiently optimized using stochastic gradient descent. We show that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.

Keywords

  • Mutual Information
  • Nonrigid Registration
  • Stochastic Gradient Descent
  • Rigid Registration
  • Joint Histogram

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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Author information

Authors and Affiliations

  1. Montreal Neurological Institute, McGill University, Canada

    Hassan Rivaz & D. Louis Collins

Authors
  1. Hassan Rivaz
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  2. D. Louis Collins
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Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Rivaz, H., Collins, D.L. (2012). Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_12

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  • DOI: https://doi.org/10.1007/978-3-642-33454-2_12

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  • Print ISBN: 978-3-642-33453-5

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