Normalized mutual information based PET-MR registration using K-Means clustering and shading correction

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

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

4 Citations (Scopus)

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. © Springer-Verlag Berlin Heidelberg 2003.
Original languageEnglish
Title of host publicationBiomedical Image Registration : Second InternationalWorkshop, WBIR 2003, Philadelphia, PA, USA, June 23-24, 2003. Revised Papers
EditorsJ.C. Gee, J.B.A. Maintz, M.W. Vannier
Place of PublicationBerlin
PublisherSpringer
Pages31-39
ISBN (Print)978-3-540-20343-8
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science
Volume2717
ISSN (Print)0302-9743

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