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Pedicle Detection in Planar Radiographs Based on Image Descriptors

  • Pedro Cunha
  • Daniel C. Moura
  • Jorge G. Barbosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

Assessing spinal deformations requires a 3D evaluation. However, due to restrictions of conventional 3D imaging techniques, 3D reconstructions are typically performed from planar radiographs. Conventional reconstruction methods require a large interaction time for the identification of anatomical structures of interest. Recently, semi-supervised methods were proposed that enable to reduce interaction time. However, these methods have shown difficulties to determine precisely the pedicles of vertebrae, which are fundamental for calculating several clinical indices. This paper proposes a new method for the detection of pedicles in planar radiographs. The method is based in the use of feature descriptors for training a binary classifier and a detection phase that is carried out by sweeping a region of interest classifying all of its pixels. The location of the pedicle corresponds to the candidate with the largest output value of the classifier. The evaluation of the method was performed by comparison with a manual identification from an expert. The classifier used was a Support Vector Machine (SVM) and several descriptors were selected in order to determine which best suits this problem. The best results were obtained using Histograms of Oriented Gradients (HOG), which was able of determining a valid detection in approximatly half of the cases.

Keywords

Feature Descriptors Classifiers Spine Radiological Images 

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References

  1. 1.
    Aubin, C., Descrimes, J., Dansereau, J., Skalli, W., Lavaste, F., Labelle, H.: Geometrical modeling of the spine and the thorax for the biomechanical analysis of scoliotic deformities using the finite element method. Annales de chirurgie 49, 749 (1995)Google Scholar
  2. 2.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Benameur, S., Mignotte, M., Labelle, H., De Guise, J.: A hierarchical statistical modeling approach for the unsupervised 3-d biplanar reconstruction of the scoliotic spine. IEEE Transactions on Biomedical Engineering 52(12), 2041–2057 (2005)CrossRefGoogle Scholar
  4. 4.
    Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  5. 5.
    Chang, C., Lin, C.: Libsvm: a library for support vector machines (2001)Google Scholar
  6. 6.
    Chen, J., Tian, J., Lee, N., Zheng, J., Smith, R., Laine, A.: A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration. IEEE Transactions on Biomedical Engineering 57(7), 1707–1718 (2010)CrossRefGoogle Scholar
  7. 7.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  9. 9.
    Doré, V., Duong, L., Cheriet, F., Cheriet, M.: Towards Segmentation of Pedicles on Posteroanterior X-Ray Views of Scoliotic Patients. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1028–1039. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Dumas, R., Mitton, D., Laporte, S., Dubousset, J., Steib, J., Lavaste, F., Skalli, W.: Explicit calibration method and specific device designed for stereoradiography. Journal of Biomechanics 36(6), 827–834 (2003)CrossRefGoogle Scholar
  11. 11.
    Duong, L., Cheriet, F., Labelle, H.: Automatic Detection of Scoliotic Curves in Posteroanterior Radiographs. IEEE Transactions on Biomedical Engineering 57(5), 1143–1151 (2009)CrossRefGoogle Scholar
  12. 12.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing. Prentice-Hall, Upper Saddle River (2004)Google Scholar
  13. 13.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)zbMATHCrossRefGoogle Scholar
  14. 14.
    Kadoury, S., Cheriet, F., Laporte, C., Labelle, H.: A versatile 3d reconstruction system of the spine and pelvis for clinical assessment of spinal deformities. Medical and Biological Engineering and Computing 45(6), 591–602 (2007)CrossRefGoogle Scholar
  15. 15.
    Kubat, M., Holte, R., Matwin, S.: Learning When Negative Examples Abound. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 146–153. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  16. 16.
    Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided selection. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179–186. Morgan Kaufmann (1997)Google Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630Google Scholar
  18. 18.
    Mitton, D., Landry, C., Veron, S., Skalli, W., Lavaste, F., De Guise, J.: 3d reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes. Medical and Biological Engineering and Computing 38(2), 133–139 (2000)CrossRefGoogle Scholar
  19. 19.
    Moura, D., Barbosa, J., Reis, A., Manuel, J., Tavares, R.: A flexible approach for the calibration of biplanar radiography of the spine on conventional radiological systems. Computer Modeling in Engineering & Sciences 60(2), 115–138 (2010)Google Scholar
  20. 20.
    Moura, D.C., Boisvert, J., Barbosa, J.G., Labelle, H., Tavares, J.M.R.S.: Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model. Medical Engineering & Physics 33(8), 924–933 (2011)CrossRefGoogle Scholar
  21. 21.
    Stokes, I.: Three-dimensional terminology of spinal deformity: a report presented to the Scoliosis Research Society by the Scoliosis Research Society Working Group on 3-D terminology of spinal deformity. Spine 19(2), 236 (1994)CrossRefGoogle Scholar
  22. 22.
    Tola, E., Lepetit, V., Fua, P.: Daisy: an Efficient Dense Descriptor Applied to Wide Baseline Stereo, vol. 32, pp. 815–830Google Scholar
  23. 23.
    Wu, G., Chang, E.: Class-boundary alignment for imbalanced dataset learning. In: Proceedings of the ICML, vol. 3. Citeseer (2003)Google Scholar
  24. 24.
    Yazici, M., Acaroglu, E., Alanay, A., Deviren, V., Cila, A., Surat, A.: Measurement of vertebral rotation in standing versus supine position in adolescent idiopathic scoliosis. Journal of Pediatric Orthopaedics 21(2), 252 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pedro Cunha
    • 1
    • 2
  • Daniel C. Moura
    • 1
    • 2
    • 3
  • Jorge G. Barbosa
    • 1
    • 2
  1. 1.Faculdade de Engenharia, Departamento de Engenharia InformáticaUniversidade do PortoPortugal
  2. 2.Laboratório de Inteligência Artificial e Ciência dos ComputadoresUniversidade do PortoPortugal
  3. 3.INEGI - Instituto de Engenharia Mecânica e Gestão Industrial, Faculdade de EngenhariaUniversidade do PortoPortugal

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