Fuzzy Smoothed Composition of Local Mapping Transformations for Non-rigid Image Registration

  • Edoardo Ardizzone
  • Roberto Gallea
  • Orazio Gambino
  • Roberto Pirrone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


This paper presents a novel method for medical image registration. The global transformation is obtained by composing affine transformations, which are recovered locally from given landmarks.Transformations of adjacent regions are smoothed to avoid blocking artifacts, so that a unique continuous and differentiable global function is obtained. Such composition is operated using a technique derived from fuzzy C-means clustering. The method was successfully tested on several datasets; results, both qualitative and quantitative, are shown. Comparisons with other methods are reported. Final considerations on the efficiency of the technique are explained.


free form deformation image registration fuzzy clustering function interpolation 


  1. 1.
    Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(6), 567–585 (1989)CrossRefzbMATHGoogle Scholar
  2. 2.
    Arad, N., Dyn, N., Reisfeld, D., Yeshurun, Y.: Image warping by radial basis functions: Application to facial expressions. Computer Vision, Graphics, and Image Processing. Graphical Models and Image Processing 56(2), 161–172 (1994)CrossRefGoogle Scholar
  3. 3.
    Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.H.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Transactions on Medical Imaging 20(6), 526–534 (2001)CrossRefGoogle Scholar
  4. 4.
    Bartoli, A., Perriollat, M., Chambon, S.: Generalized thin-plate spline warps. IEEE International Conference on Computer Vision and Pattern Recognition, cvpr (2007)Google Scholar
  5. 5.
    Johnson, H.J., Christensen, G.E.: Consistent landmark and intensity-based image registration. IEEE Transactions on Medical Imaging 21, 450–461 (2002)CrossRefGoogle Scholar
  6. 6.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms (Advanced Applications in Pattern Recognition). Springer, Heidelberg (1981)CrossRefzbMATHGoogle Scholar
  7. 7.
    Delaunay, B.N.: Sur la sphère vide. Bulletin of Academy of Sciences of the USSR (6), 793–800 (1934)zbMATHGoogle Scholar
  8. 8.
    Ardizzone, E., Gallea, R., Gambino, O., Pirrone, R.: Fuzzy c-means inspired free form deformation technique for registration. In: WILF, International Workshop on Fuzzy Logic and Applications (2009)Google Scholar
  9. 9.
    Cocosco, C.A., Kollokian, V., Kwan, R.K.S., Pike, G.B., Evans, A.C.: Brainweb: Online interface to a 3d mri simulated brain database. NeuroImage 5, 425 (1997)Google Scholar
  10. 10.
    Kwan, R.K.S., Evans, A.C., Pike, G.B.: Mri simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging 18(11), 1085–1097 (1999)CrossRefGoogle Scholar
  11. 11.
    Kwan, R.K.-S., Evans, A.C., Pike, G.B.: An extensible mri simulator for post-processing evaluation. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 135–140. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  12. 12.
    Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17(3), 463–468 (1998)CrossRefGoogle Scholar
  13. 13.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Edoardo Ardizzone
    • 1
  • Roberto Gallea
    • 1
  • Orazio Gambino
    • 1
  • Roberto Pirrone
    • 1
  1. 1.DINFO - Dipartimento di Ingegneria InformaticaUniversità degli studi di PalermoPalermoItaly

Personalised recommendations