Advertisement

Automatic Segmentation of Femur Bones in Anterior-Posterior Pelvis X-Ray Images

  • Feng Ding
  • Wee Kheng Leow
  • Tet Sen Howe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Segmentation of femurs in Anterior-Posterior x-ray images is very important for fracture detection, computer-aided surgery and surgical planning. Existing methods do not perform well in segmenting bones in x-ray images due to the presence of large amount of spurious edges. This paper presents an atlas-based approach for automatic segmentation of femurs in x-ray images. A robust global alignment method based on consistent sets of edge segments registers the whole atlas to the image under joint constraints. After global alignment, the femur models undergo local refinement to extract detailed contours of the femurs. Test results show that the proposed algorithm is robust and accurate in segmenting the femur contours of different patients.

Keywords

Automatic Segmentation Edge Point Global Alignment Rigid Transformation Active 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Manos, G.K., Cairns, A.Y., Rickets, I.W., Sinclair, D.: Segmenting radiographs of the hand and wrist. Computer Methods and Programs in Biomedicine 43(3-4), 227–237 (1993)CrossRefGoogle Scholar
  2. 2.
    Chen, H., Jain, A.K.: Tooth contour extraction for matching dental radiographs. In: Proc. Int. Conf. on Pattern Recognition, pp. 522–525 (2004)Google Scholar
  3. 3.
    El-Feghi, I., Huang, S.S.A., M.A., Ahmadi, M.: X-ray image segmentation using auto adaptive fuzzy index measure. In: Proc. Midwest Symposium on Circuits and Systems, vol. 3, pp. 499–502 (2004)Google Scholar
  4. 4.
    Yap, D.W.H., Chen, Y., Leow, W.K., En Howe, T.: Detecting femur fractures by texture analysis of trabeculae. In: Proc. Int. Conf. on Pattern Recognition, vol. 3, pp. 730–733 (2004)Google Scholar
  5. 5.
    Chen, Y., Ee, X., Leow, W.K., Howe, T.S.: Automatic extraction of femur contours from hip x-ray images. In: Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, pp. 200–209 (2005)Google Scholar
  6. 6.
    Dammann, F., Bode, A., Schwaderer, E., Schaich, M., Heuschmid, M., Maassen, M.M.: Computer-aided surgical planning for implantation of hearing aids based on ct data in a vr environment. Radiographics 21, 183–190 (2001)Google Scholar
  7. 7.
    Boukala, N., Favier, E., Laget, B., Radeva, P.: Active shape model based segmentation of bone structures in hip radiographs. In: Proc. of IEEE Int. Conf. on Industrial Technology, pp. 1682–1687 (2004)Google Scholar
  8. 8.
    Duay, V., Houhou, N., Thiran, J.P.: Atlas-based segmentation of medical images locally constrained by level sets. Technical report, Signal Processing Institute (ITS), Ecole Polytechnique Fédérale de Lausanne (EPFL) (2005)Google Scholar
  9. 9.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision. Technical report, Imaging Science and Biomedical Engineering, Manchester M13 9PT, UK (2004)Google Scholar
  10. 10.
    Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Technical report, Department of Electrical and Computer Engineering, The Johns Hopkins University (1998)Google Scholar
  11. 11.
    Ding, F., Leow, W.K., Wang, S.C.: Segmentation of 3D CT volume images using a single 2D atlas. In: Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, pp. 459–468 (2005)Google Scholar
  12. 12.
    Vinhais, C., Campilho, A.: Genetic model-based segmentation of chest x-ray images using free form deformations. In: Proc. Int. Conf. Image Analysis and Recognition (2005)Google Scholar
  13. 13.
    Belongie, S., Malik, J., Puzicha, J.: Shape context: A new descriptor for shape matching and object recognition. In: NIPS, pp. 831–837 (2000)Google Scholar
  14. 14.
    Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill, New York (1995)Google Scholar
  15. 15.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Cheng, J., Foo, S.W.: Dynamic directional gradient vector flow for snakes. IEEE Trans.on Image Processing 15(6), 1563–1571 (2006)CrossRefGoogle Scholar
  17. 17.
    Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. CVPR. vol. 1, pp. 316–323 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Feng Ding
    • 1
  • Wee Kheng Leow
    • 1
  • Tet Sen Howe
    • 2
  1. 1.Dept. of Computer Science, National University of Singapore, 3 Science Drive 2, 117543Singapore
  2. 2.Dept. of Orthopaedics, Singapore General Hospital, Outram Road, 169608Singapore

Personalised recommendations