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Interpolation-Based Detection of Lumbar Vertebrae in CT Spine Images

  • Bulat IbragimovEmail author
  • Robert Korez
  • Boštjan Likar
  • Franjo Pernuš
  • Tomaž Vrtovec
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)

Abstract

Detection of an object of interest can be represented as an optimization problem that can be solved by brute force or heuristic algorithms. However, the globally optimal solution may not represent the optimal detection result, which can be especially observed in the case of vertebra detection, where neighboring vertebrae are of similar appearance and shape. An adequate optimizer has to therefore consider not only the global optimum but also local optima that represent candidate locations for each vertebra. In this paper, we describe a novel framework for automated spine and vertebra detection in three-dimensional (3D) images of the lumbar spine, where we apply a novel optimization technique based on interpolation theory to detect the location of the whole spine in the 3D image and to detect the location of individual vertebrae within the spinal column. The performance of the proposed framework was evaluated on \(10\) computed tomography (CT) images of the lumbar spine. The resulting mean symmetric absolute surface distance of \(1.25\,{\pm }\,0.41\) mm and Dice coefficient of \(83.67\,{\pm }\,4.44\)%, computed from the final vertebra detection results against corresponding reference vertebra segmentations, indicate that the proposed framework can successfully detected vertebrae in CT images of the lumbar spine.

Keywords

Lumbar Spine Shape Model Interpolation Theory Covariance Matrix Adaptation Evolution Strategy March Cube Algorithm 
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.

Notes

Acknowledgments

This work was supported by the Slovenian Research Agency (ARRS) under grants P2–0232 and L2–4072.

References

  1. 1.
    Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J.A. et al. (eds.) Towards a New Evolutionary Computation, pp. 75–102. Springer, (2006)Google Scholar
  3. 3.
    Cheney, E.W., Light, W.A.: A course in approximation theory. Am. Math. Soc. (2009)Google Scholar
  4. 4.
    Bungartz, H.-J.: Finite elements of higher order on sparse grids. Shaker Verlag (1998)Google Scholar
  5. 5.
    Gerstner, T., Griebel, M.: Dimension adaptive tensor product quadrature. Computing 71(1), 65–87 (2003)Google Scholar
  6. 6.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques—SIGGRAPH’87, 163–169 (1987)Google Scholar
  7. 7.
    Botsch, M., Kobbelt, L.: A remeshing approach to multiresolution modeling. In: Proceedings of the 2004 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing—SGP’04, 185–192 (2004)Google Scholar
  8. 8.
    Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)CrossRefGoogle Scholar
  9. 9.
    Gower, J.C.: Generalized procrustes analysis. Psychometrika 40(1), 33–51 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  11. 11.
    Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T.: Shape representation for efficient landmark-based segmentation in 3D. IEEE Trans. Med. Imaging 33(4), 861–874 (2014)CrossRefGoogle Scholar
  12. 12.
    Weiss, K.L., Storrs, J.M., Banto, R.B.: Automated spine survey iterative scan technique. Radiology 239(1), 255–262 (2006)Google Scholar
  13. 13.
    Otake, Y., Schafer, S., Stayman, J.W., Zbijewski, W., Kleinszig, G., Graumann, R., Khanna, A.J., Siewerdsen, J.H.: Automatic localization of vertebral levels in X-ray fluoroscopy using 3D–2D registration: a tool to reduce wrong-site surgery. Phys. Med. Biol. 57(17), 5485–5508 (2012)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Proceedings of the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012, 590–598 (2012)Google Scholar
  16. 16.
    Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013, 262–270 (2013)Google Scholar
  17. 17.
    Long, L.R., Thoma, G.R.: Use of shape models to search digitized spine X-rays. In: Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems—CBMS 2000, 255–260 (2000)Google Scholar
  18. 18.
    Howe, B., Gururajan, A., Sari-Sarraf, H., Long, L.: Hierarchical segmentation of cervical and lumbar vertebrae using a customized generalized Hough transform and extensions to active appearance models. In: Proceedings of the 6th IEEE Southwest Symposium on Image Analysis and Interpretation—SSIAI 2004, 182–186 (2004)Google Scholar
  19. 19.
    Koompairojn, S., Hua, K., Bhadrakom, C.: Automatic classification system for lumbar spine X-ray images. In: Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems—CBMS 2006, 213–218 (2006)Google Scholar
  20. 20.
    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
  21. 21.
    Larhmam, M.A., Benjelloun, M., Mahmoudi, S.: Vertebra identification using template matching modeling and K-means clustering. Int. J. Comput. Assist. Radiol. Surg. 9(2), 177–187 (2014)CrossRefGoogle Scholar
  22. 22.
    Štern, D., Likar, B., Pernuš, F., Vrtovec, T.: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine. Phys. Med. Biol. 55(1), 247–264 (2010)CrossRefGoogle Scholar
  23. 23.
    Korez, R., Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T.: An improved shape-constrained deformable model for segmentation of vertebrae from CT lumbar spine images. In: Proceedings of the 2nd MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging—MICCAI CSI 2014, 76–85 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bulat Ibragimov
    • 1
    Email author
  • Robert Korez
    • 1
  • Boštjan Likar
    • 1
  • Franjo Pernuš
    • 1
  • Tomaž Vrtovec
    • 1
  1. 1.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

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