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Normalized Mutual Information Based PET-MR Registration Using K-Means Clustering and Shading Correction

  • Z. F. Knops
  • J. B. Antoine Maintz
  • M. A. Viergever
  • J. P. W. Pluim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2717)

Abstract

A method for the efficient re-binning and shading based correction of intensity distributions of the images prior to normalized mutual information based registration is presented. Our intensity distribution re-binning method is based on the K-means clustering algorithm as opposed to the generally used equidistant binning method. K-means clustering is a binning method with a variable size for each bin which is adjusted to achieve a natural clustering. Furthermore, a shading correction method is applied to reduce the effect of intensity inhomogeneities in MR images. Registering clinical shading corrected MR images to PET images using our method shows that a significant reduction in computational time without loss of accuracy as compared to the standard equidistant binning based registration is possible.

Keywords

Mutual Information Median Error Optimization Step Intensity Inhomogeneity Natural Cluster 
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-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Z. F. Knops
    • 1
  • J. B. Antoine Maintz
    • 1
  • M. A. Viergever
    • 1
    • 2
  • J. P. W. Pluim
    • 2
  1. 1.Department of Computer ScienceUtrecht UniversityUtrechtThe Netherlands
  2. 2.Image Sciences Institute, E01.335University Medical Center UtrechtUtrechtThe Netherlands

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