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Global Localization and Orientation of the Cervical Spine in X-ray Images

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Computational Methods and Clinical Applications for Spine Imaging (CSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10182))

Abstract

Injuries in cervical spine X-ray images are often missed by emergency physicians. Many of these missing injuries cause further complications. Automated analysis of the images has the potential to reduce the chance of missing injuries. Towards this goal, this paper proposes an automatic localization of the spinal column in cervical spine X-ray images. The framework employs a random classification forest algorithm with a kernel density estimation-based voting accumulation method to localize the spinal column and to detect the orientation. The algorithm has been evaluated with 90 emergency room X-ray images and has achieved an average detection accuracy of 91% and an orientation error of 3.6\(^{\circ }\). The framework can be used to narrow the search area for other advanced injury detection systems.

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References

  1. Platzer, P., Hauswirth, N., Jaindl, M., Chatwani, S., Vecsei, V., Gaebler, C.: Delayed or missed diagnosis of cervical spine injuries. J. Trauma Acute Care Surg. 61(1), 150–155 (2006)

    Article  Google Scholar 

  2. Morris, C., McCoy, E.: Clearing the cervical spine in unconscious polytrauma victims, balancing risks and effective screening. Anaesthesia 59(5), 464–482 (2004)

    Article  Google Scholar 

  3. Tezmol, A., Sari-Sarraf, H., Mitra, S., Long, R., Gururajan, A.: Customized hough transform for robust segmentation of cervical vertebrae from x-ray images. In: Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, Proceedings, pp. 224–228. IEEE (2002)

    Google Scholar 

  4. Larhmam, M.A., Mahmoudi, S., Benjelloun, M.: Semi-automatic detection of cervical vertebrae in X-ray images using generalized hough transform. In: 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 396–401. IEEE (2012)

    Google Scholar 

  5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  6. Bromiley, P.A., Adams, J.E., Cootes, T.: Localisation of vertebrae on DXA images using constrained local models with random forest regression voting. In: Yao, J., Glocker, B., Kinder, T., Li, S. (eds.) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. LNCVB, pp. 159–171. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  7. Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic location of vertebrae on DXA images using random forest regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 361–368. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_45

    Chapter  Google Scholar 

  8. Al-Arif, S.M.M.R., Asad, M., Knapp, K., Gundry, M., Slabaugh, G.: Hough forest-based corner detection for cervical spine radiographs. In: Proceedings of the 19th Conference on Medical Image Understanding and Analysis (MIUA), pp. 183–188 (2015)

    Google Scholar 

  9. Al-Arif, S.M.M.R., Asad, M., Knapp, K., Gundry, M., Slabaugh, G.: Cervical vertebral corner detection using Haar-like features and modified Hough forest. In: 2015 5th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE (2015)

    Google Scholar 

  10. 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: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_73

    Chapter  Google Scholar 

  11. Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40763-5_33

    Chapter  Google Scholar 

  12. Benjelloun, M., Mahmoudi, S., Lecron, F.: A framework of vertebra segmentation using the active shape model-based approach. J. Biomed. Imaging 2011, 9 (2011)

    Google Scholar 

  13. Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013)

    Google Scholar 

  14. Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. In: PAMI (2015)

    Google Scholar 

  15. Botev, Z.I., Grotowski, J.F., Kroese, D.P., et al.: Kernel density estimation via diffusion. Ann. Stat. 38(5), 2916–2957 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Schwarz, C., Teich, J., Welzl, E., Evans, B.: On finding a minimal enclosing parallelogram. Citeseer (1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, City, University of London, London, UK

    S. M. Masudur Rahman Al Arif & Greg Slabaugh

  2. University of Exeter Medical School, Exeter, UK

    Michael Gundry & Karen Knapp

Authors
  1. S. M. Masudur Rahman Al Arif
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  2. Michael Gundry
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  3. Karen Knapp
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  4. Greg Slabaugh
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Corresponding author

Correspondence to S. M. Masudur Rahman Al Arif .

Editor information

Editors and Affiliations

  1. National Institutes of Health , Bethesda, Massachusetts, USA

    Jianhua Yao

  2. University of Ljubljana , Ljubljana, Slovenia

    Tomaž Vrtovec

  3. University of Bern , Bern, Switzerland

    Guoyan Zheng

  4. University of Sheffield , Sheffield, United Kingdom

    Alejandro Frangi

  5. Imperial College London , London, United Kingdom

    Ben Glocker

  6. University of Western Ontario, London, Ontario, Canada

    Shuo Li

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© 2016 Springer International Publishing AG

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Al Arif, S.M.M.R., Gundry, M., Knapp, K., Slabaugh, G. (2016). Global Localization and Orientation of the Cervical Spine in X-ray Images. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science(), vol 10182. Springer, Cham. https://doi.org/10.1007/978-3-319-55050-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-55050-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55049-7

  • Online ISBN: 978-3-319-55050-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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