Abstract
Mutual information has been widely used in image registration as an effective similarity measure. It has attracted a lot of attention to the effective use of the spatial information. Here we propose a new measure that includes the mean of the neighborhood region of each pixel as one variable of the two-dimension normal distribution assumed in our method. The experimental results show that our method can not only improve the robustness of mutual information, but also reduce the affect of noise in image registration.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Phys. Med. Biol., R1–R45 (2001)
Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21(11), 977–1000 (2003)
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multi-modality image registration based on information theory. In: Proc. of Information Processing in Medical Imaging, pp. 263–274. Kluwer, Norwell (1995)
Viola, P., Wells, W.M.: Alignment by maximization of mutual information. In: Proc. of 5th Int. Conf. Computer Vision, Boston, MA, pp. 16–23 (1995)
Maintz, J.B.A., Viergever, M.A.: A survey of Medical Image Registration. Medical Image Analysis 2(1), 1–36 (1998)
Penney, G.P., Weese, J., Little, J.A., Desmedt, P., Hill, D.L.G., Hawkes, D.J.: A comparison of similarity measures for use in 2D-3D medical image registration. IEEE Transactions on Medical Imaging 17(4), 586–595 (1999)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image Registration by Maximization of Combined Mutual Information and Gradient Information. IEEE Transactions on Medical Imaging 19(8), 809–814 (2000)
Rueckert, D., Clarkson, M.J., Hill, D.L.G., Hawkes, D.J.: Non-rigid registration using higher-order mutual information. In: Proc. SPIE Medical Imaging 2000: Image Processing, San Diego, CA, 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)
Russakoff, D.B., Tomasi, C., Rohlfing, T., Maurer, C.R.: Image Similarity Using Mutual Information of Regions. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 596–607. Springer, Heidelberg (2004)
Tomazevie, D., Likar, B., Pernus, F.: Multi-Feature Mutual Information. In: Proc. of SPIE, vol. 5370, pp. 143–154 (2004)
Sabuncu, M.R., Ramadge, P.J.: Spatial Information in Entropy-Based Image Registration. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 132–141. Springer, Heidelberg (2003)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, C., Jiang, T., Wang, J., Zheng, L. (2006). A Neighborhood Incorporated Method in Image Registration. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_31
Download citation
DOI: https://doi.org/10.1007/11812715_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37220-2
Online ISBN: 978-3-540-37221-9
eBook Packages: Computer ScienceComputer Science (R0)