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)


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.


Mutual Information Median Error Optimization Step Intensity Inhomogeneity Natural Cluster 
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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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