A Novel 3D/2D Correspondence Building Method for Anatomy-Based Registration

  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)


The application of fluoroscopic images in operation is pervasive, especially for orthopaedic surgery. Anatomy-based 3D/2D registration, rigid or non-rigid, has been proven to improve the accuracy and precision of various image-guided therapies. One of the key steps for a successful anatomy-based registration is to establish 3D/2D correspondence between the 3D model and the 2D images. This paper presents a novel 3D/2D correspondence building method based on a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate spline based deformation to find a fraction of best matched 2D point pairs between features detected from the X-ray images and those extracted from the 3D model. The estimated point pairs are further ranked by their shape context matching cost and those with high cost are eliminated. The remaining point pairs are then used to set up a set of 3D point pairs such that we turn a 3D/2D registration problem to a 3D/3D one, whose solutions are well studied. Rigid and non-rigid registration algorithms incorporating the novel 3D/2D correspondence building method are presented. Quantitative and qualitative evaluation results are given, which demonstrate the validity of our method.


Iterative Close Point Iterative Close Point Target Registration Error Shape Context Statistical Shape Model 
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.
    Joskowicz, L., Milgrom, C., Simkin, A., et al.: FRACAS: a system for computer-aided image-guided long bone fracture surgery. Comp. Aid. Surg. 3(6), 271–288 (1998)CrossRefGoogle Scholar
  2. 2.
    Hofstetter, R., Slomczykowski, M., Sati, M., Nolte, L.-P.: Fluoroscopy as an image means for computer-assisted surgical navigation. Comput. Aid. Surg. 4(2), 65–76 (1999)CrossRefGoogle Scholar
  3. 3.
    Guéziec, A., Kazanzides, P., Williamson, B., Taylor, R.H.: Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot. IEEE T Med. Imaging 17(5), 715–728 (1998)CrossRefGoogle Scholar
  4. 4.
    Stindel, E., Birard, J.L., Merloz, P., et al.: Bone morphing: 3D morphological data for total knee arthroplasty. Comput. Aid. Surg. 7(3), 156–168 (2002)CrossRefGoogle Scholar
  5. 5.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput Vision and Image Under 89(2-3), 114–141 (2003)MATHCrossRefGoogle Scholar
  6. 6.
    Zheng, Y., Doermann, D.: Robust point matching for two-dimensional nonrigid shapes. In: Proceedings of ICCV 2005, vol. 2, pp. 1561–1566 (2005)Google Scholar
  7. 7.
    Fleute, M., Lavallée, S.: Nonrigid 3D/2D registration of images using a statistical model. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 138–147. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    le Bras, A., Laporte, S., Bousson, V., et al.: 3D reconstruction of the proximal femur with low-dose digital stereoradiography. Comput. Aid. Surg. 9(3), 51–57 (2004)CrossRefGoogle Scholar
  9. 9.
    Laporte, S., Skalli, W., de Guise, J.A., et al.: A biplanar reconstruction method based on 2D and 3d contours: application to the distal femur. Comput. Methods Biomech. Biomed. Engin. 6(1), 1–6 (2003)CrossRefGoogle Scholar
  10. 10.
    Hertzmann, A., Zorin, D.: Illustrating smooth surfaces. In: SIGGRAPH, pp. 517–526 (2000)Google Scholar
  11. 11.
    Canny, J.: A computational approach to edge detection. IEEE T Pattern Anal. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  12. 12.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE T Pattern Anal. 24(24), 509–521 (2002)CrossRefGoogle Scholar
  13. 13.
    Besl, P.J., McKay, N.D.: A method for registration of 3D shapes. IEEE T Pattern Anal. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  14. 14.
    Penney, G.P., Edwards, P.J., King, A.P., et al.: A stochastic iterative closest point algorithm (stochastICP). In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 762–769. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Benameur, S., Mignotte, M., Parent, S., et al.: 3D/2D registration and segmentation of scoliotic vertebra using statistical models. Comput. Med. Imag. Grap. 27, 321–337 (2003)CrossRefGoogle Scholar
  16. 16.
    Zheng, G., Rajamani, K.T., Nolte, L.-P.: Use of a dense surface point distribution model in a three-stage anatomical shape reconstruction from sparse information for computer-assisted orthopaedic surgery: a preliminary study. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 52–60. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guoyan Zheng
    • 1
  1. 1.MEM Research CenterUniversity of BernBernSwitzerland

Personalised recommendations