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

Z. F. Knops, J.B.A. (Antoine) Maintz, M.A. Viergever, J.P.W. Pluim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)


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 in 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.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsM. Sonka, J.M. Fitzpatrick
Number of pages9
Volume5032 II
Publication statusPublished - 2003
Externally publishedYes
Event2003 Medical Imaging : Image Processing - San Diego, United States
Duration: 17 Feb 200320 Feb 2003


Conference2003 Medical Imaging : Image Processing
Country/TerritoryUnited States
CitySan Diego


Dive into the research topics of 'Normalized mutual information based registration using K-means clustering based histogram binning'. Together they form a unique fingerprint.

Cite this