Automatic Segmentation of Vertebrae in Ultrasound Images

  • Florian BertonEmail author
  • Wassim Azzabi
  • Farida Cheriet
  • Catherine Laporte
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)


This paper presents an automatic method for the segmentation of vertebrae in ultrasound images. Its goal is to determine whether each pixel belongs to the bone surface, its acoustic shadow or other tissues. The method is based on the extraction of several image features described in the literature and which we adapted to our problem, and on a random forest classifier. Morphological operations and vertebra-specific constraints are then used in a regularisation step in order to obtain homogeneous regions of both the surface and the acoustic shadow of the vertebra. Experiments on a test database of 9 images show promising results, with average recognition rates for the bone surface and acoustic shadow of 81.87 %, and 91.01 %, respectively.


Segmentation Vertebrae Ultrasound Acoustic shadow Random forests 


  1. 1.
    Daanen, V., Tonetti, J., Troccaz, J.: A fully automated method for the delineation of osseous interface in ultrasound images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 549–557. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  2. 2.
    Hacihaliloglu, I., Abugharbieh, R., Hodgson, A., Rohling, R.: Bone surface localization in ultrasound using image phase-based features. Ultrasound Med. Biol. 35(9), 1475–1487 (2009)CrossRefGoogle Scholar
  3. 3.
    Foroughi, P., Boctor, E., Swartz, M., Taylor, R., Fichtinger, G.: Ultrasound bone segmentation using dynamic programming. In: Proceedings of the Ultrasonics Symposium, pp. 2523–2526 (2007)Google Scholar
  4. 4.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Hellier, P., Coup, P., Morandi, X., Collins, D.L.: An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images. Med. Image Anal. 14, 195–204 (2010)CrossRefGoogle Scholar
  7. 7.
    Karamalis, A., Wein, W., Klein, T., Navab, N.: Ultrasound confidence maps using random walks. Med. Image Anal. 16, 1101–1112 (2012)CrossRefGoogle Scholar
  8. 8.
    Kerby, B., Rohling, R., Nair, V., Abolmaesumi, P.: Automatic identification of lumbar level with ultrasound. In: Proceedings of the IEEE EMBC, pp. 2980–2983 (2008)Google Scholar
  9. 9.
    Tran, D., Rohling, R.N.: Automatic detection of lumbar anatomy in ultrasound images of human subjects. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57(9), 2248–2256 (2010)Google Scholar
  10. 10.
    Al-Deen Ashab, H., Lessoway, V.A., Khallaghi, S., Cheng, A., Rohling, R., Abolmaesumi, P.: An augmented reality system for epidural anesthesia (AREA) prepuncture identification of vertebrae. IEEE Trans. Biomed. Eng. 60(9), 2636–2644 (2013)CrossRefGoogle Scholar
  11. 11.
    Chung-Wai, J.C., Guang-Quan, Z. Siu-Yin, L., Tak-Man, M., Ka-Lee, L., Yong-Ping, Z.: Ultrasound volume projection imaging for assessment of scoliosis. IEEE Transactions on Medical Imaging (2015) (in press)Google Scholar
  12. 12.
    Wei, C., Lou, E.H.M., Le, L.H.: Ultrasound imaging of spinal vertebrae to study scoliosis. Open J. Acoust. 2(3), 95–103 (2012)CrossRefGoogle Scholar
  13. 13.
    Cheung, C., Siu-Yin, L., Yong-Ping, Z. : Development of 3-D ultrasound system for assessment of adolescent idiopathic scoliosis (AIS): And system validation. In: Proceedings of the IEEE EMBC, pp. 6474–6477 (2013)Google Scholar
  14. 14.
    Ungi, T., King, F., Kempston, M., Keri, Z., Lasso, A., Mousavi, P., Rudan, J., Borschneck, D.P., Fichtinger, G.: Spinal curvature measurement by tracked ultrasound snapshots. Ultrasound Med. Biol. 40(2), 447–454 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Florian Berton
    • 1
    Email author
  • Wassim Azzabi
    • 2
  • Farida Cheriet
    • 1
  • Catherine Laporte
    • 2
  1. 1.École Polytechnique de MontréalMontrealCanada
  2. 2.École de Technologie SupérieureMontrealCanada

Personalised recommendations