Abstract
This paper presents a novel measure of image similarity, called quantitative-qualitative measure of mutual information (Q-MI), for multi-modal image registration. Conventional information measure, i.e., Shannon’s entropy, is a quantitative measure of information, since it only considers probabilities, not utilities of events. Actually, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence. Therefore, it is important to consider both quantitative and qualitative (i.e., utility) information simultaneously for image registration. To achieve this, salient voxels such as white matter (WM) voxels near to brain cortex will be assigned higher utilities than the WM voxels inside the large WM regions, according to the regional saliency values calculated from scale-space map of brain image. Thus, voxels with higher utilities will contribute more in measuring the mutual information of two images under registration. We use this novel measure of mutual information (Q-MI) for registration of multi-modality brain images, and find that the successful rate of our registration method is much higher than that of conventional mutual information registration method.
This work is supported by NSFC (National Science Foundation of China) 60271033.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multi-modality image registration based on information theory. IPMI, 263–274 (1995)
Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kiknis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1, 35–51 (1996)
Holden, M., et al.: Voxel similarity measures for 3-D serial MR brain image registration. IEEE Trans. Med. Imag. 19, 94–102 (2000)
Rodriguez-Carranza, C.E., Loew, M.H.: A Weighted and Deterministic Entropy Measure for Image Registration Using Mutual Information. In: Proceedings of SPIE conference on Image Processing, pp. 155–166 (Febraury 1998)
Pluim, J.P., Maintz, J.B., Viergever, M.A.: Image Registration by Maximization of Combined Mutual Information and Gradient Information. IEEE Trans. Med. Imag. 19, 809–814 (2000)
Gan, R., Chung, A.: Multi-dimensional Mutual Information Based Robust Image Registration Using Maximum Distance-Gradient-Magnitude. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 210–221. Springer, Heidelberg (2005)
Rueckert, D., Clarkson, M.J., Hill, D.L., Hawakes, D.J.: Non-rigid registration using higher-order mutual information. In: Proceedings of SPIE conference on Image Processing, pp. 438–447 (2000)
Holden, M., Griffin, L.D., Saeed, N., Hill, D.L.G.: Multi-channel Mutual Information Using Scale Space. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 797–804. Springer, Heidelberg (2004)
Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans. on Med. Imaging 21(11), 1421–1439 (2002)
Kadir, T., Brady, M.: Saliency, Scale and Image Descriptions. International Journal of Computer Vision 45, 83–105 (2001)
Huang, X., Sun, Y., Metaxas, D., Sauer, F., Xu, C.: Hybrid Image Registration based on Con-figural Matching of Scale-Invariant Salient Region Features. In: Conference on Computer Vi-sion and Pattern Recognition Workshop (June 2004)
Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J.G., Kabani, N.L., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imag. 17, 463–468 (1998)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
Belis, M., Guiasu, S.: A quantitative-qualitative measure of information in cybernetic sys-tems. IEEE Trans. Inform. Theory 14, 593–594 (1968)
Taneja, H.C., Tuteja, R.K.: Characterization of a quantitative-qualitative measure of relative information. Inform. Sci. 33, 217–222 (1984)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vettering, W.T.: Numerical Recipes in C. Cambridge Univ. Press, Cambridge (1992)
Zhu, Y.-M., Cochoff, S.M.: Likelihood Maximization Approach to Image registration. IEEE Trans. on Image Processing 11(12), 1417–1426 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luan, H., Qi, F., Shen, D. (2005). Multi-modal Image Registration by Quantitative-Qualitative Measure of Mutual Information (Q-MI). In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_38
Download citation
DOI: https://doi.org/10.1007/11569541_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
Online ISBN: 978-3-540-32125-5
eBook Packages: Computer ScienceComputer Science (R0)