Segmentation of Lumbar Vertebrae Slices from CT Images

  • Hugo HuttEmail author
  • Richard Everson
  • Judith Meakin
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)


We describe a fully automated approach to vertebrae segmentation from CT images which operates on superpixels. The method is based on a conditional random field model incorporating constraints learned from labelled superpixel features. The method is shown to provide consistently accurate segmentations of different vertebrae from a variety of subjects.


Support Vector Machine Ground Truth Conditional Random Field Statistical Shape Model Accurate Segmentation 
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.



H. Hutt was funded by the EPSRC. We are grateful to the SpineWeb initiative for making the data available and to the organisers of the CSI2014 competition.


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to State-of-the-Art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)Google Scholar
  2. 2.
    Blake, A., Kohli, P., Rother, C. (eds.): Markov Random Fields for Vision and Image Processing. The MIT Press, Cambridge (2011)zbMATHGoogle Scholar
  3. 3.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  5. 5.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Tech. 2:27:1–27:27 (2011). Software available at
  6. 6.
    Gerig, G., Jomier, M., Chakos, M.: Valmet: a new validation Tool for assessing and improving 3D object segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 2208, pp. 516–523. Springer (2001)Google Scholar
  7. 7.
    Ghosh, S., Alomari, R., Chaudhary, V., Dhillon, G.: Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis. In: SPIE Conference Series, vol. 7963 (2011)Google Scholar
  8. 8.
    Huang, J., Jian, F., Wu, H., Li, H.: An improved level set method for vertebra CT image segmentation. BioMed. Eng. OnLine 12(48) (2013)Google Scholar
  9. 9.
    Hutt, H. W., Everson, R. M., and Meakin, J. R.: Automatic segmentation of vertebrae from MR images. Technical Report, 2014, School of Physics, University of Exeter (2014)Google Scholar
  10. 10.
    Kim, Y., Kim, D.: A fully automatic vertebra segmentation method using 3D deformable fences. Comput. Med. Imaging Graph. 33, 343–352 (2009)CrossRefGoogle Scholar
  11. 11.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. Med. Image Anal. 13(3), 471–482 (2009)CrossRefGoogle Scholar
  12. 12.
    Knutsson, H.: Representing Local Structure Using Tensors. In: The 6th Scandinavian Conference on Image Analysis, Oulu, pp. 244–251 (1989)Google Scholar
  13. 13.
    Lim, P.H., Bagci, U., Bai, L.: Introducing willmore flow into level set segmentation of spinal vertebrae. IEEE Trans. Biomed. Eng. 60(1), 115–122 (2013)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Torr, P.H.S., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78, 138–156 (2000)CrossRefGoogle Scholar
  16. 16.
    Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008).
  17. 17.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)zbMATHGoogle Scholar
  18. 18.
    Wu, T.-F., Lin, C.-J., Weng, R.C.: Probability estimates for multi-class classification by pair wise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)zbMATHMathSciNetGoogle Scholar
  19. 19.
    Yao, J., Burns, J. E., Munoz, H., Summers, R.M.: Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 7512, pp. 509–516. Springer (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of ExeterExeterUK

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