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Automatic Extraction of Proximal Femur Contours from Calibrated X-Ray Images Using 3D Statistical Models

  • Xiao Dong
  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5128)

Abstract

Automatic identification and extraction of bone contours from x-ray images is the first essential task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The initialization is solved by an Estimation of Bayesian Network Algorithm to fit a multiple component geometrical model to the x-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the x-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Our experimental results demonstrate its performance and efficacy even when part of the images are occluded.

Keywords

statistical models segmentation fluoroscopy Bayesian network 

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References

  1. 1.
    Chen, Y., et al.: Automatic Extraction of Femur Contours from Hip X-ray Images. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 200–209. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    de Luis-Garcia, R., et al.: A Fully Automatic Algorithm for Contour Detection of Bones in Hand Radiographs Using Active Contours. In: ICIP 2003, Part. III, pp. 421–424 (2003)Google Scholar
  3. 3.
    Roberts, M.G., et al.: Automatic segmentation of lumbar vertebrae on digitised radiographs using linked active appearance models. In: MIUA 2006, Part. I, pp. 120–124 (2006)Google Scholar
  4. 4.
    Gottschling, H., et al.: Intraoperative, Fluoroscopy-based Planning for Complex Osteotomies of the Proximal Femur. The International Journal of Medical Robotics & Computer Assisted Srugery 1(3), 67–73 (2005)CrossRefGoogle Scholar
  5. 5.
    Bartolini, F., et al.: Model-based Extraction of Femoral Medulla Ducts from Radiographic Images. Image and Vision Computing 22, 173–182 (2004)CrossRefGoogle Scholar
  6. 6.
    Tian, T.P., et al.: Computing Neck-Shaft Angle of Femur for X-ray Fracture Detection. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 82–89. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Benameur, S., et al.: 3D/2D Registration and Segmentation of Scoliotic Vertebrae Using Statistical Models. Computerized Medical Imaging and Graphics 27(5), 321–337 (2003)CrossRefGoogle Scholar
  8. 8.
    Zheng, G., Nolte, L.-P.: Surface Reconstruction of Bone from X-ray Images and Point Distribution Model Incorporating a Novel Method for 2D-3D Correspondence. In: CVPR 2006, Part II, pp. 2237–2244 (2006)Google Scholar
  9. 9.
    Lamecker, H., et al.: Atlas-based 3D-Shape Reconstruction from X-ray Images. In: ICPR 2006, Part I, pp. 371–374 (2006)Google Scholar
  10. 10.
    Tang, T.S., Ellis, R.E.: 2D/3D Deformable Registration Using a Hybrid Atlas. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005, Part II. LNCS, vol. 3750, pp. 223–230. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Behiels, G., et al.: Active Shape Model-based Segmentation of Digital X-ray Images. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 128–137. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Howe, B., et al.: Hierarchical Segmentation of Cervical and Lumbar Vertebrae Using a Customised Generalized Hough Transform and Extensions to Active Appearance Models. In: Proc. IEEE 6th SSIAI, March 2004, pp. 182–186 (2004)Google Scholar
  13. 13.
    Langs, G., et al.: Determining Position and Fine Shape Detail in Radiological Anatomy. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 532–539. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Seise, M., et al.: Probabilistic Segmentation of the Knee Joint from X-ray Images. In: MIUA 2006, pp. 110–114 (2006)Google Scholar
  15. 15.
    de Bruijne, M., Nielsen, M.: Image Segmentation by Shape Particle Filtering. In: ICPR 2004, Part. III (2004)Google Scholar
  16. 16.
    Zheng, G., et al.: 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, Part II. LNCS, vol. 3852, pp. 52–60. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Ma, B., Ellis, R.E.: Surface-based Registration with a Particle Filter. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004, Part I. LNCS, vol. 3216, pp. 566–573. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Lee, M.W., Cohen, I.: Human Upper Body Pose Estimation in Static Images. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004, Part II. LNCS, vol. 3022, pp. 126–138. Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Sigal, L., et al.: Tracking loose-limbed people. In: CVPR 2004, Part. 1, pp. 421–428 (2004)Google Scholar
  20. 20.
    Wu, Y., et al.: Tracking articulated body by dynamic Markov network. In: ICCV 2003, vol. 1, pp. 1094–1101 (2003)Google Scholar
  21. 21.
    Coughlan, J., Ferreira, S.: Finding deformable shapes using loopy belief propagation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 453–468. Springer, Heidelberg (2002)Google Scholar
  22. 22.
    Rangarajan, A., et al.: A Bayesian Network Framework for Relational Shape Matching. In: ICCV 2003, pp. 671–678 (2003)Google Scholar
  23. 23.
    Dong, X., Zheng, G.: Fully Automatic Determination of Morphological Parameters of Proximal Femur from Calibrated Fluoroscopic Images Through Particle Filtering. In: Campilho, A., Kamel, M. (eds.) ICIAR 2006. LNCS, vol. 4142, pp. 535–546. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

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

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