Automatic USCT Image Processing Segmentation for Osteoporosis Detection

  • Marwa FradiEmail author
  • Wajih Elhadj Youssef
  • Ghaith Bouallegue
  • Mohsen Machhout
  • Philippe Lasaygues
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 146)


Ultrasound Computed Tomography (USCT) can be used to cortical bone imaging but is limited by the strong variations in acoustic impedance between the medium and its environment. The aim of this work is to test an automatic image processing recognition to enhance the detection of the boundaries. Image processing algorithms are used for automatic detection of edges and defects. In first step, the application of pre-processing algorithm is done. In second step, we have applied k-Means and Ostu algorithm. As a result, we improve the edge detection to bring up the calculation bones structures length. Hence, osteopathologies detection was achieved and results outperform related works by Signal-to-Noise Ratio improvement and saving time execution.


Proposed k-means algorithm Discret Haar wavelet (DHW) Edge detection Osteoporosis detection SNR 


  1. 1.
    Lasaygues, P., Lefebvre, J.: Bone imaging by low frequency ultrasonic reflection tomography. In Hallowell, M., Wells, P.N.T. (eds.) Acoustical Imaging, vol. 25. Kluwer Academic Publishers, Boston (2002)Google Scholar
  2. 2.
    Lasaygues, P.: Assessing the cortical thickness of long bone shafts in children, using two-dimensional ultrasonic diffraction tomography. Ultrasound Med. Biol. 32(8), 1215–1227 (2006)CrossRefGoogle Scholar
  3. 3.
    Lasaygues, P.: Tomographie ultrasonore osseuse : Caractérisation de la diaphyse des os par inversion d’un champ acoustique diffracté, Intérêt pour l’imagerie pédiatrique (2006)Google Scholar
  4. 4.
    Lasaygues, P., Guillermin, R., Metwally, K., Fernandez, S., Balasse, L., Petit, P., Baron, C.: Contrast resolution enhancement of Ultrasonic Computed Tomography using a wavelet-based method – preliminary results in bone imaging. In: International Workshop on Medical Ultrasound Tomography, Nov (2017), Speyer, Germany modified, Avril (2018)Google Scholar
  5. 5.
    Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20(4), 1033–1048 (1993)CrossRefGoogle Scholar
  6. 6.
    Clarke, L.P., Velthuizen, R.P., Camacho, M.A., Heine, J.J., Vaidyanathan, M., Hall, L.O., Thatcher, R.W., Silbiger, M.L.: MRI segmentation: methods and applications. Magn. Reson. Imaging 13(3), 343–368 (1995)CrossRefGoogle Scholar
  7. 7.
    Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: a survey. Med. Image Anal. 5, 127–142 (2001)CrossRefGoogle Scholar
  8. 8.
    Fradi, M., Youssef, W.E., Lasaygues,P., Machhout, M.: Improved USCT of paired bones using wavelet-based image processing. Int. J. Image, Graph. Signal Process. (IJIGSP) 10(9), 1–9 (2018).
  9. 9.
    Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelets. IEEE Trans. Inf. Theory 38(2), 617–643 (1992)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Jose, A., Ravi, S., Sambath, M.: Brain tumor segmentation using K-means clustering and Fuzzy C-means algorithm and its area calculation. Int. J. Innov. Res. Comput. Commun. Eng. 2, 3496–3501 (2014)Google Scholar
  11. 11.
    Liu, S.: Image segmentation technology of the Ostu method for image materials based on binary PSO algorithm. In Jin, D., Lin, S. (eds.) Advances in Computer Science, Intelligent System and Environment, vol. 104, pp. 415–419. Springer Berlin Heidelberg, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Naga Gayathri Divya, B., Sowjanya, K.: Otsu’s method of image segmentation using particle swarm optimization technique. Int. J. Sci. Eng. Technol. Res. 4(10), 1805–1808 (2015)Google Scholar
  13. 13.
    Liu, S.: Image segmentation technology of the ostu method for image materials based on binary PSO algorithm. In: Advances in Computer Science, Intelligent System and Environment, pp. 415–419. Springer, Berlin, CSISE 2011, AISC 104 (2011)Google Scholar
  14. 14.
    Glaser, D.L., MD, Kaplan, F.S.: Osteoporosis: definition and Clinical Presentation. Spine J. 22, 12S–16S (1997)CrossRefGoogle Scholar
  15. 15.
    Liao, Y.-Y., Wu, J.-C., Li, C.-H., Yeh, C.-K.: Texture feature analysis for breast ultrasound image enhancement. Ultrason. Imaging 33, 264–278 (2011)CrossRefGoogle Scholar
  16. 16.
    Wiem FOURATI et Mohamed Salim BOUHLEL: Techniques de Débruitage d’Images. In: SETIT (2009)Google Scholar
  17. 17.
    Hadda, W., Hamrouni, K., Kalti, K.: Analyse des performances des filtres en traitement d’images. In: SETIT (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marwa Fradi
    • 1
    Email author
  • Wajih Elhadj Youssef
    • 1
  • Ghaith Bouallegue
    • 1
  • Mohsen Machhout
    • 1
  • Philippe Lasaygues
    • 2
  1. 1.Laboratory of Electronics and Micro-Electronics FSMMonastir UniversityMonastirTunisia
  2. 2.Laboratory of Mechanics and AcousticsMarseille UniversityMarseilleFrance

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