Medical Image Registration by Minimizing Divergence Measure Based on Tsallis Entropy
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.
[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/.
[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/.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:60211", author = "Shaoyan Sun and Liwei Zhang and Chonghui Guo", title = "Medical Image Registration by Minimizing Divergence Measure Based on Tsallis Entropy", 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.", volume = "2", number = "1", pages = "38-6", }
