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A Robust Segmentation Framework for Spine Trauma Diagnosis

  • Poay Hoon Lim
  • Ulas Bagci
  • Li Bai
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)

Abstract

Accurate three-dimensional (3D) image segmentation techniques have become increasingly important for medical image analysis in general, and for spinal vertebrae image analysis in particular. The complexity of vertebrae shapes, gaps in the cortical bone and internal boundaries pose significant challenge for image analysis. In this paper, we describe a level set image segmentation framework that integrates prior shape knowledge and local geometrical features to segment both normal and fractured spinal vertebrae. The prior shape knowledge is computed via kernel density estimation whereas the local geometrical features is captured through an edge-mounted Willmore energy. While the shape prior energy draws the level set function towards possible shape boundaries, the Willmore energy helps to capture the detail shape and curvature information of the vertebrae. Experiment on CT images of normal and fractured spinal vertebrae demonstrate promising results in 3D segmentation.

References

  1. 1.
    Adalsteinsson, D., Sethian, J.A.: A fast level set method for propagating interfaces. J. Comput. Phys. 118, 269–277 (1995)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Droske, M., Rumpf, M.: A level set formulation for willmore flow. Interfaces Free Boundaries 6(3), 361–378 (2004)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Ghebreab, S., Smeulders, A.: Combining strings and necklaces for interactive three-dimensional segmentation of spinal images using an integral deformable spine model. IEEE Trans. Biomed. Eng. 51(10), 1821–1829 (2004)CrossRefGoogle Scholar
  5. 5.
    Ghosh, S., Raja’s, A., Chaudhary, V, Dhillon, G.: Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis. In: SPIE Medical, Imaging (2011)Google Scholar
  6. 6.
    Kadoury, S., Labelle, H., Pargios, N.: Automatic inference of articulated spine models in CT images using higher-order markov random fields. Medical Image Analysis 15, 426–437 (2011)CrossRefGoogle Scholar
  7. 7.
    Kang, Y., Engelke, K., Kalender, W.A.: A new accurate and precise 3d segmentation method for skeletal structures in volumetric ct data. IEEE Trans. Med. Imag. 22(5), 586–598 (2003)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Lim, P., 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
  10. 10.
    Looby, S., Flanders, A.: Spine trauma. Radiol. Clin. N. Am. 49(1), 129–163 (2011)CrossRefGoogle Scholar
  11. 11.
    Lorenz, C., Krahnstoever, N.: 3D statistical shape models for medical image segmentation. In: 3D Digital Imaging and Modeling, pp. 4–8 (1999)Google Scholar
  12. 12.
    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: MICCAI, pp. 19–27 (2010)Google Scholar
  13. 13.
    Mastmeyer, A., Engelke, K., Fuchs, C., Kalender, W.A.: A hierarchical 3-d segmentation method and the definition of vertebral body coordinate systems for qct of the lumbar spine. Med. Image Anal. 10, 560–577 (2006)CrossRefGoogle Scholar
  14. 14.
    Mayer, M., Zenner, J., Auffarth, A., Blocher, M., Figl, M., Resch, H., Koller, H.: Hidden discoligamentous instability in cervical spine injuries: can quantitative motion analysis improve detection? Eur. Spine J. 22(10), 2219–2227 (2013)CrossRefGoogle Scholar
  15. 15.
    Naegel, B.: Using mathematical morphology for the anatomical labeling of vertebrae from 3-d ct-scan images. Comput. Med. Imag. Grap. 31(3), 141–156 (2007)CrossRefGoogle Scholar
  16. 16.
    Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Sussman, M., Smereka, P., Osher, S.: A level set approach for computing solutions to incompressible 2-phase flow. J. Comput. Phys. 114(1), 146–159 (1994)CrossRefMATHGoogle Scholar
  18. 18.
    Willmore, T.J.: Note on embedded surfaces. Analele Ştiinţifice ale Universităţii Al. I. Cuza din Iaşi. Serie Nouă Ia 11B, 493–496 (1965)Google Scholar
  19. 19.
    Yao, J., Burns, J.E., Munoz, H., Summers, R.M.: Detection of vertebral body fractures based on cortical shell unwrapping. In: MICCAI Part III, LNCS 7512 (2012)Google Scholar
  20. 20.
    Yao, J., Burns, J.E., Wiese, T., Summers, R.M.: Quantitative vertebral compression fracture evaluation using a height compass. In: SPIE Medical Imaging (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.Radiology and Imaging SciencesNational Institutes of HealthBethesdaUSA

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