Advertisement

3D Gabor Wavelets for Evaluating Medical Image Registration Algorithms

  • Linlin Shen
  • Dorothee Auer
  • Li Bai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)

Abstract

A Gabor wavelets based method is proposed in this paper for evaluating and tuning the parameters of image registration algorithms. The registration quality is measured by the anatomical variability of the registered images. We propose in this paper a local anatomical structure descriptor, namely the Maximum Responded Gabor Wavelet (MRGW) for such a purpose. The effectiveness of the descriptor is demonstrated through a practical spatial normalization example – the variance of MRGW is successfully applied to tune the parameters of a nonlinear spatial normalization algorithm, which is integrated in one of the most popular software packages for medical image processing – the Statistical Parametric Mapping (SPM).

Keywords

3D Gabor Wavelets Medical Image Registration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Thompson, P., Toga, A.W.: A surface-based technique for warping three-dimensional images of the brain. IEEE Transactions On Medical Imaging 15, 402–417 (1996)CrossRefGoogle Scholar
  2. 2.
    Hajnal, J.V., Saeed, N., Soar, E.J., Oatridge, A., Young, I.R., Bydder, G.M.: A Registration And Interpolation Procedure For Subvoxel Matching Of Serially Acquired Mr-Images. Journal Of Computer Assisted Tomography 19, 289–296 (1995)CrossRefGoogle Scholar
  3. 3.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image And Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  4. 4.
    Gaens, T., Maes, F., Vandermeulen, D., Suetens, P.: Non-rigid multimodal image registration using mutual information. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1099–1106. Springer, Heidelberg (1998)Google Scholar
  5. 5.
    Ashburner, J., Friston, K.J.: Nonlinear spatial normalization using basis functions. Human Brain Mapping 7, 254–266 (1999)CrossRefGoogle Scholar
  6. 6.
    Robbins, S., Evans, A.C., Collins, D.L., Whitesides, S.: Tuning and comparing spatial normalization methods. Medical Image Analysis 8, 311–323 (2004)CrossRefGoogle Scholar
  7. 7.
    West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer, C.R., Kessler, R.M., Maciunas, R.J., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vander Meulen, D., vandenElsen, P.A., Napel, S., Sumanaweera, T.S., Harkness, B., Hemler, P.F., Hill, D.L.G., Hawkes, D.J., Studholme, C., Maintz, J.B.A., Viergever, M.A., Malandain, G., Pennec, X., Noz, M.E., Maguire, G.Q., Pollack, M., Pelizzari, C.A., Robb, R.A., Hanson, D., Woods, R.P.: Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal Of Computer Assisted Tomography 21, 554–566 (1997)CrossRefGoogle Scholar
  8. 8.
    Holden, M., Hill, D.L.G., Denton, E.R.E., Jarosz, J.M., Cox, T.C.S., Rohlfing, T., Goodey, J., Hawkes, D.J.: Voxel similarity measures for 3-D serial MR brain image registration. IEEE Transactions On Medical Imaging 19, 94–102 (2000)CrossRefGoogle Scholar
  9. 9.
    Dinov, I.D., Mega, M.S., Thompson, P.M., Woods, R.P., Sumners, D.L., Sowell, E.L., Toga, A.W.: Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage. IEEE Transactions On Information Technology In Biomedicine 6, 73–85 (2002)CrossRefGoogle Scholar
  10. 10.
    Gabor, D.: Theory of communications. Journal of Institution of Electrical Engineers 93, 429–457 (1946)Google Scholar
  11. 11.
    Granlund, G.H.: Search of a general picture processing operator. Computer Graphics and Image Processing 8, 155–173 (1978)CrossRefGoogle Scholar
  12. 12.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial- frequency, and orientation opptimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A-Optics Image Science and Vision 2, 1160–1169 (1985)CrossRefGoogle Scholar
  13. 13.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognition 24, 1167–1186 (1991)CrossRefGoogle Scholar
  14. 14.
    Weldon, T.P., Higgins, W.E., Dunn, D.F.: Efficient Gabor filter design for texture segmentation. Pattern Recognition 29, 2005–2015 (1996)CrossRefGoogle Scholar
  15. 15.
    Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)CrossRefGoogle Scholar
  16. 16.
    Shen, L., Bai, L.: A review on Gabor wavelets for face recognition. Pattern Analysis and Applications (in press, 2006)Google Scholar
  17. 17.
    Shen, L., Bai, L., Fairhurst, M.: Gabor wavelets and Generalized Discriminant Analysis for face identification and verification. Image and Vision Computing (in press, 2006)Google Scholar
  18. 18.
    Wang, Y.J., Chua, C.S.: Face recognition from 2D and 3D images using 3D Gabor filters. Image And Vision Computing 23, 1018–1028 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Linlin Shen
    • 1
  • Dorothee Auer
    • 1
  • Li Bai
    • 2
  1. 1.Academic RadiologyUniversity of Nottingham, Queen’s Medical CentreNottinghamUK
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK

Personalised recommendations