Automated Identification of Thoracolumbar Vertebrae Using Orthogonal Matching Pursuit

  • Tao Wu
  • Bing Jian
  • Xiang Sean Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


A reliable detection and definitive labeling of vertebrae can be difficult due to factors such as the limited imaging coverage and various vertebral anomalies. In this paper, we investigate the problem of identifying the last thoracic vertebra and the first lumbar vertebra in CT images, aiming to improve the accuracy of an automatic spine labeling system especially when the field of view is limited in the lower spine region. We present a dictionary-based classification method using a cascade of simultaneous orthogonal matching pursuit (SOMP) classifiers on 2D vertebral regions extracted from the maximum intensity projection (MIP) images. The performance of the proposed method in terms of accuracy and speed has been validated by experimental results on hundreds of CT images collected from various clinical sites.


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  1. 1.
    Konin, G.P., Walz, D.M.: Lumbosacral transitional vertebrae: Classification, imaging findings, and clinical relevance. American Journal of Neuroradiology 195, 465–466 (2010)Google Scholar
  2. 2.
    Smyth, P.P., Taylor, C.J., Adams, J.E.: Vertebral shape: automatic measurement with active shape models. Radiology 211, 571–578 (1999)CrossRefGoogle Scholar
  3. 3.
    Dong, X., Zheng, G.: Automated vertebra identification from X-ray images. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010 Part II. LNCS, vol. 6112, pp. 1–9. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Peng, Z., Zhong, J., Wee, W.G., Lee, J.H.: Automated vertebra segmentation and quantification algorithm of the whole spine MR images. In: International Conference of the IEEE Engineering in Medicine and Biology Society (2005)Google Scholar
  5. 5.
    Schmidt, S., Kappes, J.H., Bergtholdt, M., Pekar, V., Dries, S.P.M., Bystrov, D., Schnörr, C.: Spine detection and labeling using a parts-based graphical model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Corso, J.J., Alomari, R.S., Chaudhary, V.: Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 202–210. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Huang, S.-H., Chu, Y.-H., Lai, S.-H., Novak, C.L.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Trans. Med. Imaging 28(10), 1595–1605 (2009)CrossRefGoogle Scholar
  8. 8.
    Kelm, B., Zhou, S., Suehling, M., Zheng, Y., Wels, M., Comaniciu, D.: Detection of 3D spinal geometry using iterated marginal space learning. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 96–105. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Stern, D., Likar, B., Pernus, F., Vrtovec, T.: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine. Physics in Medicine and Biology 55(1), 247 (2010)CrossRefGoogle Scholar
  10. 10.
    Herring, J.L., Dawant, B.M.: Automatic lumbar vertebral identification using surface-based registration. Journal of Biomedical Informatics 34(2), 74–84 (2001)CrossRefGoogle Scholar
  11. 11.
    Yao, J., O’Connor, S.D., Summers, R.M.: Automated spinal column extraction and partitioning. In: ISBI, pp. 390–393 (2006)Google Scholar
  12. 12.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. Medical Image Analysis 13(3), 471–482 (2009)CrossRefGoogle Scholar
  13. 13.
    Ma, J., Lu, L., Zhan, Y., Zhou, X., Salganicoff, M., Krishnan, A.: Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010 Part I. LNCS, vol. 6361, pp. 19–27. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Tropp, J.A., Gilbert, A.C., Strauss, M.J.: Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit. Signal Processing 86(3), 572–588 (2006)CrossRefzbMATHGoogle Scholar
  15. 15.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. (1), pp. 511–518 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tao Wu
    • 1
  • Bing Jian
    • 1
  • Xiang Sean Zhou
    • 1
  1. 1.Siemens Healthcare, SYNGO R&DUSA

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