Registration of 2D Histological Images of Bone Implants with 3D SRμCT Volumes

  • Hamid Sarve
  • Joakim Lindblad
  • Carina B. Johansson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)

Abstract

To provide better insight in bone modeling and remodeling around implants, information is extracted using different imaging techniques. Two types of data used in this project are 2D histological images and 3D SRμCT (synchrotron radiation-based computed microtomography) volumes. To enable a direct comparison between the two modalities and to bypass the time consuming and difficult task of manual annotation of the volumes, registration of these data types is desired.

In this paper, we present two 2D–3D intermodal rigid-body registration methods for the mentioned purpose. One approach is based on Simulated Annealing (SA) while the other uses Chamfer Matching (CM). Both methods use Normalized Mutual Information for measuring the correspondence between an extracted 2D-slice from the volume and the 2D histological image whereas the latter approach also takes the edge distance into account for matching the implant boundary. To speed up the process, part of the computations are done on the Graphic Processing Unit.

The results show that the CM-approach provides a more reliable registration than the SA-approach. The registered slices with the CM-approach correspond visually well to the histological sections, except for cases where the implant has been damaged.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hajnal, J.V., et al.: Medical Image Registration. CRC Press, Boca Raton (2000)Google Scholar
  2. 2.
    Milan, S., Michael, F.: Handbook of Medical Imaging. SPIE Press (2000)Google Scholar
  3. 3.
    Zöllei, L., Grimson, E., Norbash, A., Wells, W.: 2D-3D rigid registration of X-Ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators. CVPR 2, 696 (2001)Google Scholar
  4. 4.
    Russakoff, D.B., Rohlfing, T., Calvin, R., Maurer, J.: Fast intensity-based 2D-3D image registration of clinical data using light fields. ICCV 01, 416 (2003)Google Scholar
  5. 5.
    Kubias, A., et al.: Extended global optimization strategy for rigid 2D/3D image registration. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 759–767. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Ino, F., et al.: A GPGPU approach for accelerating 2-D/3-D rigid registration of medical images. In: Guo, M., Yang, L.T., Di Martino, B., Zima, H.P., Dongarra, J., Tang, F. (eds.) ISPA 2006. LNCS, vol. 4330, pp. 939–950. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Knaan, D., Joskowicz, L.: Effective intensity-based 2D/3D rigid registration between fluoroscopic X-ray and CT. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 351–358. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)CrossRefGoogle Scholar
  9. 9.
    Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. IEEE Trans. on Medical Imaging 22, 986–1004 (2003)CrossRefGoogle Scholar
  10. 10.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Sciene 220, 671–681 (1983)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Goldberg, D.: Genetic Algorithms in Optimization, Search and Machine Learning. Addison-Wesley, Reading (1989)Google Scholar
  12. 12.
    Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Computer Journal 7, 152–162 (1977)Google Scholar
  13. 13.
    Lundqvist, R.: Atlas-Based Fusion of Medical Brain Images. PhD thesis, Uppsala University, Uppsala (2001)Google Scholar
  14. 14.
    Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: Proc. 5th Int. Joint Conf. Artificial Intelligence, pp. 659–663 (1977)Google Scholar
  15. 15.
    Cai, J., et al.: CT and PET lung image registration and fusion in radiotherapy treatment planning using the chamfer-matching method. International journal of radiation oncology 43, 871–883 (1999)Google Scholar
  16. 16.
    Lejdfors, C.: High-level GPU Programming. PhD thesis, Lund University (2008)Google Scholar
  17. 17.
    Hong, H., Kim, K., Park, S.: Fast 2D-3D point-based registration using GPU-based preprocessing for image-guided surgery. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 218–226. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Köhn, A., et al.: GPU accelerated image registration in two and three dimensions. In: Bildverarbeitung fur die Medizin 2006, pp. 261–265 (2006)Google Scholar
  19. 19.
    Johansson, C., Morberg, P.: Cutting directions of bone with biomaterials in situ does influence the outcome of histomorphometrical quantification. biomaterials 16, 1037–1039 (1995)CrossRefGoogle Scholar
  20. 20.
    Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. In: PAMI, vol. 10(6), pp. 849–865 (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hamid Sarve
    • 1
  • Joakim Lindblad
    • 1
  • Carina B. Johansson
    • 2
  1. 1.Centre for Image AnalysisSwedish University of Agricultural SciencesUppsalaSweden
  2. 2.Department of Clinical MedicineÖrebro UniversityÖrebroSweden

Personalised recommendations