Medical Image Registration by Minimizing Divergence Measure Based on Tsallis Entropy

Authors: Shaoyan Sun, Liwei Zhang, Chonghui Guo

Abstract:

As the use of registration packages spreads, the number of the aligned image pairs in image databases (either by manual or automatic methods) increases dramatically. These image pairs can serve as a set of training data. Correspondingly, the images that are to be registered serve as testing data. In this paper, a novel medical image registration method is proposed which is based on the a priori knowledge of the expected joint intensity distribution estimated from pre-aligned training images. The goal of the registration is to find the optimal transformation such that the distance between the observed joint intensity distribution obtained from the testing image pair and the expected joint intensity distribution obtained from the corresponding training image pair is minimized. The distance is measured using the divergence measure based on Tsallis entropy. Experimental results show that, compared with the widely-used Shannon mutual information as well as Tsallis mutual information, the proposed method is computationally more efficient without sacrificing registration accuracy.

Keywords: Multimodality images, image registration, Shannonentropy, Tsallis entropy, mutual information, Powell optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076778

References:

[1] L. G. Brown, "A survey of image registration techniques," ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, 1992.
[2] H. M. Chen, and P. K. Varshney, "Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation," IEEE Trans. Med. Imaging, vol. 22, no. 9, pp. 1111-1119, 2003.
[3] R. P. Woods, J. C. Mazziotta, and S.R. cherry, "MRI-PET registration with automated algorithm," J. Comput. Assisted Tomography, vol. 17, pp. 536-546, 1993.
[4] D. L. G. Hill, C. Studholme, and D. J. Hawkes, "Voxel similarity measures for automated image registration," in Proc. Visualisation in Biomedical computing, U.S.A., 1994, pp. 205-216.
[5] J. P. W. Pluim, J. B. Antoine Maintz, and M. A. Viergever, "Mutualinformation- based registration of medical images: a survey," IEEE Trans. Med. Imaging, vol. 22, no. 8, pp. 986-1004, 2003.
[6] Y. He, A. B. Hamza, and H. Krim, "A generalized divergence measure for robust image registration," IEEE Trans. Sig. Processing, vol. 51, no. 5, pp. 1211-1220, 2003.
[7] M. P. Wachowiak, R. Smolikova, and T. M. Peters, "Multiresolution biomedical image registration using generalized information measures," in Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Montréal, 2003, pp. 846-853.
[8] S. Martin, G. Morison, W. Nailon, and T. Durrani, " ÔÇÿFast and accurate image registration using Tsallis entropy and simultaneous perturbation stochastic approximation," Electron. Lett., vol. 40, no. 10, pp.595-597, 2004.
[9] C. Tsallis, "Possible generalization of Boltzmann-Gibbs statistics," J. Stat. Phys., vol. 52, pp. 479-487, 1988.
[10] N. Cvejic, C N. Cangarajah, and Bull D. R, "Image fusion metric based on mutual information and Tsallis entropy," Electron. Lett., vol. 42, no. 11, pp.626-627, 2006.
[11] A. C. S. Chung, W. M. Wells III, A. Norbash, and W. E. L. Grimson, "Multi-modal image registration by minimizing Kullback-Leibler distance," in Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI),
[12] R. Gan, J. Wu, A. C. S. Chung, S.C.H. Yu, and W.M. Wells III, "Multiresolution image registration based on Kullback-Leibler distance," in Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Saint-Malo, 2004, pp. 599-606
[13] F. Maes, A. Collignon, D. Vandermeulen, G. Machal, and P. Suetens, "Multimodality image registration by maximization of mutual information, " IEEE Trans. Med. Imaging, vol. 16, no. 2, pp. 187-198, 1997.
[14] A. Collignon, F. Maes, and D. Delaere et al, "Automated multimodality medical image registration using mutual information theory," in Proc. 14th International Conference Information Processing in Medical Imaging, France 1995, 3, pp. 263-274.
[15] P. Viola, and W. M. Wells, "Alignment by maximization of mutual information," in Proc. 5th International Conference on Computer Vision, USA 1995, pp.16-23.
[16] W. H. Press, B. P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical recipes in C. Cambridge, U.K.: Cambridge Univ. Press, 1992
[17] D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans, "Design and construction of a realistic digital brain phantom," IEEE Trans. Med. Imaging, vol. 17, no. 3, pp. 463-468, 1998.
[18] McConnell Brain Image Centre Montreal Neurological Inst, McGill University, Montreal, QC, Canada. (Online). Available: http://www.bic.mni.mcgill.ca/brainweb/.
[19] Vanderbilt Univ., Nashville, TN. Retrospective Image Registration Evaluation (RIRE). (Online). Available: http://www.vuse.vanderbilt.edu/~image/registration/.