Normalized mutual information-based registration using K-means clustering-based histogram binning

Zeger F. Knops; J. B. Antoine Maintz; Max A. Viergever; Josien P. W. Pluim

doi:10.1117/12.480458

15 May 2003 Normalized mutual information-based registration using K-means clustering-based histogram binning

Zeger F. Knops, J. B. Antoine Maintz, Max A. Viergever, Josien P. W. Pluim

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480458
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Zeger F. Knops, J. B. Antoine Maintz, Max A. Viergever, and Josien P. W. Pluim "Normalized mutual information-based registration using K-means clustering-based histogram binning", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480458

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