Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2007, Article ID 93479, 10 pages
http://dx.doi.org/10.1155/2007/93479
Research Article

Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information

1Medical Image Processing Group, Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Science, P.O. Box 2728, Beijing 100080, China
2Life Science Center, Xidian University, Xi'an, Shaanxi 710071, China

Received 10 May 2006; Revised 13 January 2007; Accepted 22 January 2007

Academic Editor: Robert R. Edelman

Copyright © 2007 Jiangang Liu and Jie Tian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187–198, 1997. View at Publisher · View at Google Scholar
  2. P. Viola and W. M. Wells III, “Alignment by maximization of mutual information,” in Proceedings of the 5th IEEE International Conference on Computer Vision (ICCV '95), pp. 16–23, Cambridge, Mass, USA, June 1995. View at Publisher · View at Google Scholar
  3. F. Maes, D. Vandermeulen, and P. Suetens, “Medical image registration using mutual information,” Proceedings of the IEEE, vol. 91, no. 10, pp. 1699–1721, 2003. View at Publisher · View at Google Scholar
  4. J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Interpolation artefacts in mutual information-based image registration,” Computer Vision and Image Understanding, vol. 77, no. 2, pp. 211–232, 2000. View at Publisher · View at Google Scholar
  5. J. Tsao, “Interpolation artifacts in multimodality image registration based on maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 22, no. 7, pp. 854–864, 2003. View at Publisher · View at Google Scholar
  6. T. Butz and J.-P. Thiran, “Affine registration with feature space mutual information,” in Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '01), vol. 2208 of Lecture Notes in Computer Science, pp. 549–556, Utrecht, The Netherlands, October 2001.
  7. J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Image registration by maximization of combined mutual information and gradient information,” IEEE Transactions on Medical Imaging, vol. 19, no. 8, pp. 809–814, 2000. View at Publisher · View at Google Scholar
  8. E. Haber and J. Modersitzki, “Intensity gradient based registration and fusion of multi-modal images,” Tech. Rep. TR-2004-027-A, Department of Mathematics and Computer Science, Emory University, Atlanta, Ga, USA, June 2004. View at Google Scholar
  9. J. B. A. Maintz, P. A. van den Elsen, and M. A. Viergever, “Comparison of edge-based and ridge-based registration of CT and MR brain images,” Medical Image Analysis, vol. 1, no. 2, pp. 151–161, 1996. View at Publisher · View at Google Scholar
  10. F. Maes, Segmentation and registration of multimodal medical images: from theoty, implementation and validation to a useful tool in clinical practice, Ph.D. thesis, Catholic University of Leuven, Leuven, Belgium, 1998.
  11. C. Studholme, D. L. G. Hill, and D. J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment,” Pattern Recognition, vol. 32, no. 1, pp. 71–86, 1999. View at Publisher · View at Google Scholar
  12. X. Wang and J. Tian, “Image registration based on maximization of gradient code mutual information,” Image Analysis and Stereology, vol. 24, pp. 1–7, 2005. View at Google Scholar
  13. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, China Machine Press, Beijing, China, 2nd edition, 2004.
  14. J. A. Nelder and R. Mead, “A simplex method for function minimization,” Computer Journal, vol. 7, pp. 308–313, 1965. View at Google Scholar
  15. F. Maes, D. Vandermeulen, and P. Suetens, “Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information,” Medical Image Analysis, vol. 3, no. 4, pp. 373–386, 1999. View at Publisher · View at Google Scholar
  16. J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, “Convergence properties of the Nelder-Mead simplex method in low dimensions,” SIAM Journal on Optimization, vol. 9, no. 1, pp. 112–147, 1998. View at Publisher · View at Google Scholar
  17. J. West, J. M. Fitzpatrick, M. Y. Wang et al., “Comparison and evaluation of retrospective intermodality brain image registration techniques,” Journal of Computer Assisted Tomography, vol. 21, no. 4, pp. 554–566, 1997. View at Publisher · View at Google Scholar
  18. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Publishing House of Electronics Industry, Beijing, China, 2nd edition, 2002.
  19. T. Hartkens, D. L. G. Hill, A. D. Castellano-Smith et al., “Using points and surfaces to improve voxel-based non-rigid registration,” in Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '02), vol. 2489 of Lecture Notes in Computer Science, pp. 565–572, Tokyo, Japan, September 2002.
  20. P. Hellier and C. Barillot, “Coupling dense and landmark-based approaches for nonrigid registration,” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 217–227, 2003. View at Publisher · View at Google Scholar
  21. H. J. Johnson and G. E. Christensen, “Consistent landmark and intensity-based image registration,” IEEE Transactions on Medical Imaging, vol. 21, no. 5, pp. 450–461, 2002. View at Publisher · View at Google Scholar
  22. P. Thevenaz and M. Unser, “A pyramid approach to sub-pixel image fusion based on mutual information,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '96), vol. 1, pp. 265–268, Lausanne, Switzerland, September 1996. View at Publisher · View at Google Scholar
  23. E. H. W. Meijering, W. J. Niessen, and M. A. Viergever, “Quantitative evaluation of convolution-based methods for medical image interpolation,” Medical Image Analysis, vol. 5, no. 2, pp. 111–126, 2001. View at Publisher · View at Google Scholar
  24. R. He and P. A. Narayana, “Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images,” Computerized Medical Imaging and Graphics, vol. 26, no. 4, pp. 277–292, 2002. View at Publisher · View at Google Scholar
  25. T. Yokoi, T. Soma, H. Shinohara, and H. Matsuda, “Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images,” Annals of Nuclear Medicine, vol. 18, no. 8, pp. 659–667, 2004. View at Google Scholar
  26. J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: a survey,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 986–1004, 2003. View at Publisher · View at Google Scholar
  27. M. Jenkinson and S. Smith, “A global optimisation method for robust affine registration of brain images,” Medical Image Analysis, vol. 5, no. 2, pp. 143–156, 2001. View at Publisher · View at Google Scholar
  28. C. Grova, A. Biraben, J. M. Scarabin et al., “A methodology to validate MRI/SPECT registration methods using realistic simulated spect data,” in Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '01), vol. 2208 of Lecture Notes in Computer Science, pp. 275–282, Utrecht, The Netherlands, October 2001.
  29. J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual information matching in multiresolution contexts,” Image and Vision Computing, vol. 19, no. 1-2, pp. 45–52, 2001. View at Publisher · View at Google Scholar