Abstract
An extension of the mutual information metric to a three-variate cost function for driving the registration of a volume to pair of co-registered volumes is presented. While mutual information has typically been applied to pairs of variables, it is possible to compute multi-variate mutual information. The implementation of multi-variate mutual information is described. This metric is demonstrated using the problem of registering a deformed t2 slice of the visible male magnetic resonance data set to either a single t1 slice or a pair of co-registered t1 and proton density slices. Two-variable and three-variable metric registration results are compared. Adding the extra proton density information to the registration cost metric leads to faster optimization convergence and better final accuracy. Multi-variate mutual information has potential application in problems where the addition of more information can lead to solution convergence or improve accuracy.
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References
Collignon, A., Vandermeulen, D., Suetens, P., Marchal, G.: 3D multimodality medical image registration using feature space clustering. In: Ayache, N. (ed.) CVRMed 1995. LNCS, vol. 905, pp. 195–204. Springer, Heidelberg (1995)
Viola, P., Wells, W.M.: Alignment by maximization of mutual information. In: 5th Int’l Conf. on Computer Vision, Cambridge, MA, pp. 16–23. IEEE, Los Alamitos (1995)
Kim, B., Boes, J.L., Frey, K.A., Meyer, C.R.: Information for automated multimodal image warping. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 349–354. Springer, Heidelberg (1996)
Wells, W., Viola, P., Atsumi, H., Hakajima, S., Kikinis, R.: Multimodal volume registration by maximization of mutual information. Medical Image Analysis 1(1), 35–51 (1996)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
Hill, D.L., Maurer Jr., C.R., Studholme, C., Fitzpatrick, J.M., Hawkes, D.J.: Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm. Journal of Computer Assisted Tomography 22(2), 317–323 (1998)
Meyer, C.R., Boes, J.L., Kim, B., Bland, P., Zasadny, K.R., Kison, P.V., Koral, K., Frey, K.A., Wahl, R.L.: Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin plate spline warped geometric deformations. Medical Image Analysis 1(3), 195–206 (1997)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge (1988)
Izenman, A.J.: Recent developments in nonparametric density estimation. Journal of the American Statistical Society 86(413), 205–224 (1991)
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© 1999 Springer-Verlag Berlin Heidelberg
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Boes, J.L., Meyer, C.R. (1999). Multi-variate Mutual Information for Registration. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_65
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DOI: https://doi.org/10.1007/10704282_65
Publisher Name: Springer, Berlin, Heidelberg
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