Fully Automatic Determination of Morphological Parameters of Proximal Femur from Calibrated Fluoroscopic Images Through Particle Filtering

  • Xiao Dong
  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


A computational framework based on particle filter is proposed for fully automatic determination of morphological parameters of proximal femur from calibrated fluoroscopic images. In this framework, the proximal femur is decomposed into three components: (1) femoral head, (2) femoral neck, and (3) femoral shaft, among which structural constraints are defined according to the anatomical structure of the proximal femur. Each component is represented by a set of parameters describing its three-dimensional (3D) spatial position as well as its 3D geometrical shape. The constraints between different components are modeled by a rational network. Particle filter based inference is then used to estimate those parameters from the acquired fluoroscopic images. We report the quantitative and qualitative evaluation results on 10 dry cadaveric femurs, which indicate the validity of the present approach.


Femoral Neck Femoral Head Bayesian Network Proximal Femur Particle Filter 
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.
    Burr, D.B., Cook, L.T., Martin, N.L., Asher, M.: Measurement accuracy of proximal femoral geometry using biplanar radiography. J. Pediatr. Orthop. 1(2), 171–179 (1981)Google Scholar
  2. 2.
    Le Bras, A., Laporte, S., Bousson, V., et al.: 3D reconstruction of the proximal femur with low-dose digital stereoradiography. Comp. Aid. Surg. 9(3), 51–57 (2004)CrossRefGoogle Scholar
  3. 3.
    Ron, O., Joskowicz, L., Milgrom, C., Simkin, A.: Computer-based periaxial rotation measurement for aligning fractured femur fragments from CT: a feasibility study. Comp. Aid. Surg. 7, 332–341 (2002)CrossRefGoogle Scholar
  4. 4.
    Burkhardt, S., Roth, M., Burgkart, R., Schweikard, A.: A new system for completely MR-based computer assisted orthopaedic surgery. International Congress Series, vol. 1256, p. 1342. Elsevier, Amsterdam (2003)Google Scholar
  5. 5.
    Gottschling, H., Roth, M., Schweikard, A., Burgkart, R.: Intraoperative, fluoroscopy-based planning for complex osteotomies of the proximal femur. The International Journal of Medical Robotics & Computer Assisted Surgery 1(3), 67–73 (2005)CrossRefGoogle Scholar
  6. 6.
    Hofstetter, R., Slomczykowski, M., Sati, M., Nolte, L.-P.: Fluoroscopy as an image means for computer-assisted surgical navigation. Comp. Aid. Surg. 4(2), 65–76 (1999)CrossRefGoogle Scholar
  7. 7.
    Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 343–356. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  8. 8.
    Lee, M.W., Cohen, I.: Human upper body pose estimation in static images. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 126–138. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Wu, Y., Hua, G., Yu, T.: Tracking articulated body by dynamic Markov network. In: ICCV 2003, Nice, France, pp. 1094–1101 (2003)Google Scholar
  10. 10.
    Sudderth, E.B., Mandel, M.I., Freeman, W.T., Willsky, A.S.: Visual hand tracing using nonparametric belief propagation. In: IEEE CVPR workshop on Generative Model based Vision (2004)Google Scholar
  11. 11.
    Kanazawa, K., Killer, D., Russell, S.: Stochastic simulation algorithms for dynamic probabilistic networks. In: Proc. Uncertainty in Artificial Intelligence, pp. 346–351 (1995)Google Scholar
  12. 12.
    Mahaisavariya, B., Sitthiseripratip, K., Tongdee, T., et al.: Morphological study of the proximal femur: a new method of geometrical assessment using 3-dimensional reverse engineering. Med. Eng. Phys. 24, 617–622 (2002)CrossRefGoogle Scholar
  13. 13.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision. Technical Report, Imaging Science and Biomedical Engineering, University of Manchester, UK (March 2004)Google Scholar
  14. 14.
    Andrieu, C., Doucet, A.: Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC. IEEE T. Image Process 47(10), 456–463 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

Personalised recommendations