Evaluation of Similarity Measures for Non-rigid Registration

  • Darko Škerl
  • Boštjan Likar
  • Franjo Pernuš
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)


In this paper we propose a protocol for the evaluation of similarity measures for non-rigid registration. The protocol is feasible for the evaluation of non-rigid registration in which the deformation model is based on a set of regularly or irregularly distributed corresponding point pairs. The proposed protocol is able to deduce five properties of the similarity measure for each point pair of the deformation model, so that local or global estimation of the similarity measure properties can be derived. The feasibility of the proposed protocol was demonstrated on a popular deformation model based on B-splines, on six similarity measures, and on the “gold standard” CT and MR images of three spine vertebrae and three MR T1 and T2 images of the head.


Similarity Measure Mutual Information Deformation Model Normalize Mutual Information Point Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Thompson, P.M., Toga, A.W.: Warping strategies for intersubject registration. In: Bankman, I. (ed.) Handbook of Medical Image Processing, pp. 569–601. Academic Press, London (1999)Google Scholar
  2. 2.
    Maintz, J.B., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2, 1–36 (1998)CrossRefGoogle Scholar
  3. 3.
    Woods, R.P.: Validation of Registration Accuracy. In: Bankman, I. (ed.) Handbook of Medical Image Processing, pp. 491–498. Academic Press, London (1999)Google Scholar
  4. 4.
    Hellier, P., Barillot, C., Corouge, I., Gibaud, B., Le Goualher, G., Collins, D.L., Evans, A., Malandain, G., Ayache, N., Christensen, G.E., Johnson, H.J.: Retrospective evaluation of intersubject brain registration. IEEE Trans. Med. Imaging 22, 1120–1130 (2003)CrossRefGoogle Scholar
  5. 5.
    West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer Jr., C.R., Kessler, R.M., Maciunas, R.J., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., van den Elsen, P.A., Napel, S., Sumanaweera, T.S., Harkness, B., Hemler, P.F., Hill, D.L., Hawkes, D.J., Studholme, C., Maintz, J.B., Viergever, M.A., Malandain, G., Woods, R.P., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. J. Comput. Assist. Tomogr. 21, 554–566 (1997)CrossRefGoogle Scholar
  6. 6.
    Schnabel, J.A., Tanner, C., Castellano-Smith, A.D., Degenhard, A., Leach, M.O., Hose, D.R., Hill, D.L., Hawkes, D.J.: Validation of nonrigid image registration using finite-element methods: application to breast MR images. IEEE Trans. Med. Imaging 22, 238–247 (2003)CrossRefGoogle Scholar
  7. 7.
    Škerl, D., Likar, B., Pernuš, F.: A protocol for evaluation of image registration similarity measures. IEEE Trans. Med. Imaging (in press, 2006)Google Scholar
  8. 8.
    Tomaževič, D., Likar, B., Slivnik, T., Pernuš, F.: 3-D/2-D registration of CT and MR to X-ray images. IEEE Trans. Med. Imaging 22, 1407–1416 (2003)CrossRefGoogle Scholar
  9. 9.
    Unser, M.: Splines: A Perfect Fit for Signal and Image Processing. IEEE Signal Processing Magazine 16, 22–38 (1999)CrossRefGoogle Scholar
  10. 10.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999)CrossRefGoogle Scholar
  11. 11.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16, 187–198 (1997)CrossRefGoogle Scholar
  12. 12.
    Wells 3rd, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1, 35–51 (1996)CrossRefGoogle Scholar
  13. 13.
    Studholme, C., Hill, D.L., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)CrossRefGoogle Scholar
  14. 14.
    Tomaževič, D., Likar, B., Pernuš, F.: 3-D/2-D registration by integrating 2-D information in 3-D. IEEE Trans. Med. Imaging 25, 17–27 (2006)CrossRefGoogle Scholar
  15. 15.
    Roche, A., Malandain, G., Pennec, X., Ayache, N.: The correlation ratio as a new similarity measure for multimodality image registration. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1115–1124. Springer, Heidelberg (1998)Google Scholar
  16. 16.
    Hartkens, T., Hill, D.L., Castellano-Smith, A.D., Hawkes, D.J., Maurer Jr., C.R., Martin, A.J., Hall, W.A., Liu, H., Truwit, C.L.: Measurement and analysis of brain deformation during neurosurgery. IEEE Trans. Med. Imaging 22, 82–92 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Darko Škerl
    • 1
  • Boštjan Likar
    • 1
  • Franjo Pernuš
    • 1
  1. 1.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations