Efficient and Effective Ultrasound Image Analysis Scheme for Thyroid Nodule Detection

  • Eystratios G. Keramidas
  • Dimitris K. Iakovidis
  • Dimitris Maroulis
  • Stavros Karkanis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)


Ultrasound imaging of thyroid gland provides the ability to acquire valuable information for medical diagnosis. This study presents a novel scheme for the analysis of longitudinal ultrasound images aiming at efficient and effective computer-aided detection of thyroid nodules. The proposed scheme involves two phases: a) application of a novel algorithm for the detection of the boundaries of the thyroid gland and b) detection of thyroid nodules via classification of Local Binary Pattern feature vectors extracted only from the area between the thyroid boundaries. Extensive experiments were performed on a set of B-mode thyroid ultrasound images. The results show that the proposed scheme is a faster and more accurate alternative for thyroid ultrasound image analysis than the conventional, exhaustive feature extraction and classification scheme.


Ultrasound Thyroid Nodules Thyroid Boundary Detection Local Binary Patterns 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Welker, M.J., Orlov, D.: Thyroid Nodules. American Family Physician 67(3) (2003)Google Scholar
  2. 2.
    Mailloux, G.E., Bertrand, M., Stampfler, R.: Local Histogram Information Content Of Ultrasound B-Mode Echographic Texture. Ultrasound in Medicine and Biology 11(5), 743–750 (1985)CrossRefGoogle Scholar
  3. 3.
    Wagner, R.F., Insana, M.F., Brown, D.G.: Unified approach to the detection and classification of speckle texture in diagnostic ultrasound. Opt. Eng. 25, 738–742 (1986)Google Scholar
  4. 4.
    Fellingham, L.L., Sommer, F.G.: Ultrasonic characterization of tissue structure in the in vivo human liver and spleen. IEEE Transactions Sonics and Ultrasonics 31(4), 418–428 (1984)Google Scholar
  5. 5.
    Smutek, D., Sara, R., Sucharda, P., Tjahjadi, T., Svec, M.: Image texture analysis of sonograms in chronic inflammations of thyroid gland. Ultrasound in Medicine and Biology 29(11), 1531–1543 (2003)CrossRefGoogle Scholar
  6. 6.
    Skouroliakou, C., Lyra, M., Antoniou, A., Vlahos, L.: Quantitative image analysis in sonograms of the thyroid gland. Nuclear Instruments and Methods in Physics Research A 569, 606–609 (2006)CrossRefGoogle Scholar
  7. 7.
    Iakovidis, D.K., Savelonas, M.A., Karkanis, S.A., Maroulis, D.E.: Segmentation of Medical Images with Regional Inhomogeneities. In: Proc. International Conference on Pattern Recognition (ICPR), vol. 2, pp. 279–282. IAPR, Hong Kong (2006)Google Scholar
  8. 8.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  9. 9.
    9. Rumack, C.M., Wilson, S.R., Charboneau, J.W., Johnson, J.A.: Diagnostic Ultrasound. Mosby (2004), ISBN 0323020232Google Scholar
  10. 10.
    Pujol, O., Radeva, P.: Supervised texture classification for intravascular tissue characterization. In: Suri, J.S., Wilson, D., Laximinarayan, S. (eds.) Handbook of Biomedical Image Analysis, Segmentation Models Part B, vol. 2, Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (1999)Google Scholar
  12. 12.
    Swain, M.J., Ballard, D.H.: Color indexing. IJCV 7(I), 11–32 (1991)CrossRefGoogle Scholar
  13. 13.
    Tomimori, E.K., Camargo, R.Y.A., Bisi, H., Medeiros-Neto, G.: Combined ultrasonografhic and cytological studies in the diagnosis of thyroid nodules. Biochimie 81, 447–452 (1999)CrossRefGoogle Scholar
  14. 14.
    Kaus, M.R., Warfield, S.K., Jolesz, F.A., Kikinis, R.: Segmentation of Meningiomas and Low Grade Gliomas in MRI. In: Taylor, C., Colchester, A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 1999. LNCS, vol. 1679, pp. 1–10. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  15. 15.
    Weiss, G.M., Provost, F.: The effect of class distribution on classifier learning. Technical Report ML-TR-43. Dept. of Computer Science, Rudgers University (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Eystratios G. Keramidas
    • 1
  • Dimitris K. Iakovidis
    • 1
  • Dimitris Maroulis
    • 1
  • Stavros Karkanis
    • 1
  1. 1.Dept. of Informatics and Telecommunications, University of Athens, Panepistimioupolis, 15784, AthensGreece

Personalised recommendations