Skip to main content
SpringerLink
Log in

Segmentation of cam-type femurs from CT scans

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

We introduce a new way to accurately segment cam-type pathological femurs from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. In addition, the pathological femurs with femoral-acetabular impingements have a non-standard shape, which increases segmentation difficulty. We overcome these difficulties by (a) dividing the femur into two rounds of segmentation—one for the femur head and another for the body—(b) pre-processing the CT scan to reduce anatomical sources of error (c) two modes of segmentation—a rough estimation of a contour and another for fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam femoral–acetabular impingements. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. Femur models segmented with our method had an average volume overlap error of 2.71±0.44% and symmetric surface distance of 0.28±0.04 mm compared to ground truth models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Álvarez, L.B.-N.: A real time morphological snakes algorithm. Retrieved 6 September, 2010 from image processing on line: http://www.ipol.im/pub/algo/abmh_real_time_morphological_snakes_algorithm/ (2010)

  2. Álvarez, L., Baumela, L., Henríquez, P., Márquez-Neila, P.: Morphological snakes. In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco (2010)

    Google Scholar 

  3. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  4. Chan, T., Vese, L.A.: Active contours without edges. IEEE Trans. Images Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  5. Chen, S., Radke, R.J.: Level set segmentation with both shape and intensity priors. In: IEEE 12th International Conference on Computer Vision, pp. 763–770 (2009)

    Chapter  Google Scholar 

  6. Cohen, L.: On active contour models and balloons. CVGIP, Image Underst. 53(2), 211–218 (1991)

    Article  MATH  Google Scholar 

  7. Cohen, L.D., Cohen, I.: Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1131–1147 (1993)

    Article  Google Scholar 

  8. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Proceedings on European Conference on Computer Vision vol. 2, pp. 484–4498 (1998)

    Google Scholar 

  9. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  10. Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. Comput. Vis. 69(3), 335–351 (2006)

    Article  Google Scholar 

  11. Cremers, D., Schnörr, C., Weickert, J., Schellewald, C.: Diffusion-snakes using statistical shape knowledge. In: Notes in Computer Science, vol. 1888, pp. 164–174 (2000)

    Google Scholar 

  12. Fernandes, L.A., Oliveira, M.M.: Real-timeline detection through an improved Hough transform voting scheme. Pattern Recognit. 41(1), 299–314 (2008)

    Article  MATH  Google Scholar 

  13. Field, D., Hutchinson, J.O.: Field’s Lower Limb Anatomy, Palpation and Suface Markings, 1st edn. Churchill Livingstone, London (2008)

    Google Scholar 

  14. Gilles, B., Moccozet, L., Magnenat-Thalmann, N.: Anatomical modelling of the musculoskeletal systems. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 289–296 (2006)

    Google Scholar 

  15. He, L., Peng, Z., Everding, B., Wang, X., Han, C.Y., Weiss, K.L., et al.: A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput., 141–163 (2008)

  16. Hossain, M., Andrew, J.G.: Current management of femoro-acetabular impingement. Curr. Orthop. 22(4), 300–310 (2008)

    Article  Google Scholar 

  17. Kass, K., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1987)

    Article  Google Scholar 

  18. Kaus, M.R., Pekar, V., Lorenz, C., Truyen, R., Lobregt, S., Weese, J.: Automated 3-D PDM construction from segmented images using deformable models. IEEE Trans. Med. Imaging 22(8), 1005–1013 (2003)

    Article  Google Scholar 

  19. Magnenat-Thalmann, N., Yahia-Cherif, L., Gilles, B., Molet, T.: Hip joint reconstruction and motion visualization using MRI and optical motion capture. Biomedizinische Technik/Biomedical Engineering 48(s1), 20–23 (2003)

    Article  Google Scholar 

  20. McInerney, T., Terzopoulos, D.: T-snakes: topology adaptive snakes. Med. Image Anal. 4(2), 73–91 (2000)

    Article  Google Scholar 

  21. Rizzo, D.C.: Delmar’s Fundamentals of Anatomy & Physiology. Delmar a Division of Thompson Learning, Albany (2001)

    Google Scholar 

  22. Schmid, J., Kim, J., Magnenat-Thalman, N.: Robust statistical shape models for MRI bone segmentation in presence of small field of view. Med. Image Anal. 15, 155–168 (2011)

    Article  Google Scholar 

  23. Sherouse, G.W., Novins, K., Chaney, E.L.: Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int. J. Radiat. Oncol. Biol. Phys. 18(3), 651–658 (1990)

    Article  Google Scholar 

  24. Song, W.W., Li, G.H., Ou, Z.Y., Han, J., Zhao, D.W., Wang, W.M.: Model-based segmentation of femoral head and acetabulum from CT images. In: IEEE/ICME International Conference on Complex Medical Engineering, pp. 586–590 (2007)

    Chapter  Google Scholar 

  25. Stegmaier, S., Strengert, M., Klein, T., Ertl, T.: A simple and flexible volume rendering framework for graphics-hardware-based raycasting. In: Proceedings of Volume Graphics, New York, USA, pp. 187–195 (2005)

    Google Scholar 

  26. Tannast, M., Siebenrock, K.A., Anderson, S.E.: Femoroacetabular impingment: radiographic diagnosis—what the radiologist should know. Am. J. Roentgenol. 188, 1540–1552 (2007)

    Article  Google Scholar 

  27. Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. Int. J. Comput. Vis. 50(3), 271–293 (2002)

    Article  MATH  Google Scholar 

  28. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  29. Yokota, F., Okada, T., Takao, M., Sugano, N., Tada, Y., Sato, Y.: Automated segmentation of the femur and pelvis from 3D CT data of diseased hip uding hierachical statistical shape model of joint structure. In: Medical Image Computing and Computer-Assisted Intervention. Lecture Notes on Computer Science, vol. 5762, pp. 811–818 (2009)

    Google Scholar 

  30. Zoroofi, R.A., Sato, T., Sasama, T., Nishii, T., Nobuhiko, S., Yonenobu, K., et al.: Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans. Inf. Technol. Biomed. 7(4), 329–343 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Ottawa, 800 King Edward Ave, Ottawa, K1N 6N5, Ontario, Canada

    Gabriel Telles O’Neill & Won-Sook Lee

  2. Division of Orthopaedic Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

    Paul Beaulé

Authors
  1. Gabriel Telles O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Won-Sook Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Beaulé
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Won-Sook Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

O’Neill, G.T., Lee, WS. & Beaulé, P. Segmentation of cam-type femurs from CT scans. Vis Comput 28, 205–218 (2012). https://doi.org/10.1007/s00371-011-0636-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-011-0636-1

Keywords

Search

Navigation