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.
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Knops, Z.F., Maintz, J.B.A., Viergever, M.A., Pluim, J.P.W. (2003). Normalized Mutual Information Based PET-MR Registration Using K-Means Clustering and Shading Correction. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds) Biomedical Image Registration. WBIR 2003. Lecture Notes in Computer Science, vol 2717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39701-4_4
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DOI: https://doi.org/10.1007/978-3-540-39701-4_4
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