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Proceedings Paper

Normalized mutual information-based registration using K-means clustering-based histogram binning
Author(s): Zeger F. Knops; J. B. Antoine Maintz; Max A. Viergever; Josien P. W. Pluim
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Paper Abstract

A new method for the estimation of the intensity distributions of the images prior to normalized mutual information (NMI) based registration is presented. Our 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. Registering clinical MR-CT and MR-PET images with K-means clustering based intensity distribution estimation shows that a significant reduction is computational time without loss of accuracy as compared to the standard equidistant binning based registration is possible. Further inspection shows a reduction in the NMI variance and a reduction in local maxima for K-means clustering based NMI registration as opposed to equidistant binning based NMI registration.

Paper Details

Date Published: 15 May 2003
PDF: 9 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480458
Show Author Affiliations
Zeger F. Knops, Utrecht Univ. (Netherlands)
J. B. Antoine Maintz, Utrecht Univ. (Netherlands)
Max A. Viergever, Utrecht Univ. (Netherlands)
Univ. Medical Ctr. Utrecht (Netherlands)
Josien P. W. Pluim, Univ. Medical Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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