@inproceedings{5955c5cd31674c318aef5000feaffb74,
title = "Normalized mutual information based PET-MR registration using K-Means clustering and shading correction",
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. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.",
author = "Z.F. Knops and J.B.A. Maintz and M.A. Viergever and J.P.W. Pluim",
year = "2003",
doi = "10.1007/978-3-540-39701-4\_4",
language = "English",
isbn = "978-3-540-20343-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "31--39",
editor = "J.C. Gee and J.B.A. Maintz and M.W. Vannier",
booktitle = "Biomedical Image Registration : Second InternationalWorkshop, WBIR 2003, Philadelphia, PA, USA, June 23-24, 2003. Revised Papers",
address = "Germany",
}