Normalized Mutual Information Based PET-MR Registration Using K-Means Clustering and Shading Correction
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.
KeywordsMutual Information Median Error Optimization Step Intensity Inhomogeneity Natural Cluster
Unable to display preview. Download preview PDF.
- 1.Collignon, F.M., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multimodality image registration based on information theory. In: Bizais, Y., Barillot, C., Di Paolo, R. (eds.) Information Processing in Medical Imaging, pp. 263–274. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
- 2.Collignon, F.M.: Multi-modality medical image registration by maximization of mutual information, Ph.D. thesis, Catholic Universtity of Leuven, Leuven, Belgium (1998)Google Scholar
- 4.Viola, P.: Alignment by maximization of mutual information, Ph.D. thesis, Massachusetts Institute of Technology, Boston, MA, USA (1995)Google Scholar
- 5.Wells III, W.M., Viola, P., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. In: Medical Robotics and Computer Assisted Surgery, pp. 55–62. John Wiley & Sons, New York (1995)Google Scholar
- 8.MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Le Cam, M., Neyman, J. (eds.) proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Statistics, vol. I. University of California Press, Berkeley (1967)Google Scholar
- 9.Knops, Z.F., Maintz, J.B.A., Viergever, M.A., Pluim, J.P.W.: Normalized mutual information based registration using K-means clustering based histogram binning. In: proc. SPIE Medical Imaging 2003 (2003) (in press)Google Scholar
- 10.Studholme, C.: Measures of 3D medical image alignment, Ph.D. thesis. University of London, London, UK (1997)Google Scholar
- 12.Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C. Cambridge University Press, Cambridge (1992)Google Scholar