CT-MR Image Registration in 3D K-Space Based on Fourier Moment Matching

  • Hong-Ren Su
  • Shang-Hong Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)


CT-MRI registration is a common processing procedure for clinical diagnosis and therapy. We propose a novel K-space affine image registration algorithm via Fourier moment matching. The proposed algorithm is based on estimating the affine matrix from the moment relationship between the corresponding Fourier spectrums. This estimation strategy is very robust because the energy of the Fourier spectrum is mostly concentrated in the low-frequency band, thus the moments of the Fourier spectrum are robust against noises and outliers. Our experiments on the real CT and MRI datasets show that the proposed Fourier-based registration algorithm provides higher registration accuracy than the existing mutual information registration technique.


Multi-modal image registration Fourier moments CT MRI 


  1. 1.
    Hawkes, D.J.: Algorithms for radiological image registration and their clinical application. Journal of Anatomy 193(3), 347–361 (1998)CrossRefGoogle Scholar
  2. 2.
    XiaoShen, W., LongGen, L., ChaoSu, H., JianJian, Q., ZhiYong, X., Yan, F.: A comparative study of three CT and MRI registration algorithms in nasopharyngeal carcinoma. Journal of Applied Clinical Medical Physics 10(2) (2009)Google Scholar
  3. 3.
    Jean-François, D., Mérence, S., Anne, B., Guy, C., Max, L., Vincent, G.: Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency. Radiotherapy & Oncology 69(3), 237–245 (2003)CrossRefGoogle Scholar
  4. 4.
    Antoine Maintz, J.B., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  5. 5.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vision Computing 21(11), 977–1000 (2003)CrossRefGoogle Scholar
  6. 6.
    West, J., et al.: Comparison and evaluation of retrospective inter-modality brain image registration techniques. Journal of Computer Assisted Tomography 21(4), 554–566 (1997)CrossRefGoogle Scholar
  7. 7.
    Josien, P.W., Pluim, J.B., Antoine, M., Max, A.V.: Mutual information based registration of medical images: a survey. IEEE Trans. Med. Imaging 22(8), 986–1004 (2003)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Twieg, D.: The k-trajectory formulation of the NMR imaging process with applications in analysis and synthesis of imaging methods. Medical Physics 10(5), 610–621 (1983)CrossRefGoogle Scholar
  10. 10.
    De Castro, E., Morandi, C.: Registration of translated and rotated images using finite Fourier transforms. IEEE Trans. Pattern Analysis Mach. Intell. 3, 700–703 (1987)CrossRefGoogle Scholar
  11. 11.
    Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. Pattern Analysis Mach. Intell. 5(8), 1266–1270 (1996)Google Scholar
  12. 12.
    Pan, W., Qin, K., Chen, Y.: An adaptable-multilayer fractional Fourier transform approach for image registration. IEEE Trans. Pattern Analysis Mach. Intell. 31(3), 400–413 (2009)CrossRefGoogle Scholar
  13. 13.
    Bracewell, R.N., Chang, K.Y., Jha, A.K., Wang, Y.H.: Affine theorem for two-dimensional Fourier transform. Electronics Letters 29(3), 304 (1993)CrossRefGoogle Scholar
  14. 14.
    Foroosh, H., Zerubia, J.B., Berthod, M.: Extension of phase correlation to subpixel registration. IEEE Trans. Image Processing 11(3), 188–200 (2002)CrossRefGoogle Scholar
  15. 15.
    Zokai, S., Wolberg, G.: Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations. IEEE Trans. Image Processing 14(10), 1422–1434 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ho, J., Peter, A., Ranganrajan, A., Yang, M.-H.: An algebraic approach to affine registration of point sets. In: Proc. Int. Conf. on Computer Vision 2009 (2009)Google Scholar
  17. 17.
  18. 18.
  19. 19.
    D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P.: Non-rigid atlas-to-image registration by minimization of class-conditional image entropy. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004, Part I. LNCS, vol. 3216, pp. 745–753. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hong-Ren Su
    • 1
  • Shang-Hong Lai
    • 1
    • 2
  1. 1.Institute of Information Systems and ApplicationsNational Tsing Hua UniversityHsinchuTaiwan
  2. 2.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

Personalised recommendations