Fuzzy Local Binary Patterns for Ultrasound Texture Characterization

  • Dimitris K. Iakovidis
  • Eystratios G. Keramidas
  • Dimitris Maroulis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the Local Binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy Local Binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.

Keywords

Fuzzy Local Binary Patterns Ultrasound Thyroid nodules Support Vector Machines 

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References

  1. 1.
    Bushberg, J.T.: The Essential Physics of Medical Imaging. Lippincott Williams & Wilkins (2002) ISBN 0683301187Google Scholar
  2. 2.
    Mailloux, G., Bertrand, M., Stampfler, R., Ethier, S.: Local histogram information content of ultrasound B-mode echographic texture. Ultrasound in Medicine and Biology 11, 743–750 (1985)CrossRefGoogle Scholar
  3. 3.
    Mailloux, G., Bertrand, M., Stampfler, R., Ethier, S.: Computer Analysis of Echographic Textures in Hashimoto Disease of the Thyroid. Journal of Clinical Ultrasound 14, 521–527 (1986)CrossRefGoogle Scholar
  4. 4.
    Chikui, T., Okamura, K., Tokumori, K., Nakamura, S., Shimizu, M., Koga, M., Yoshiura, K.: Quantitative analyses of sonographic images of the parotid gland in patients with Sjögren’s syndrome. Ultrasound in Medicine and Biology 32, 617–622 (2006)CrossRefGoogle Scholar
  5. 5.
    Raeth, U., Schlaps, D., Limberg, B., Zuna, I., Lorenz, A., Kaick, G., Lorenz, W., Kommerell, B.: Diagnostic accuracy of computerized B-scan texture analysis and conventional ultrasonography in diffuse parenchymal and malignant liver disease. Journal of Clinical Ultrasound 13, 87–99 (1985)CrossRefGoogle Scholar
  6. 6.
    Llobet, R., Pérez-Cortés, J., Toselli, A., Juan, A.: Computer-aided detection of prostate cancer. International Journal of Medical Informatics 76, 547–556 (2006)CrossRefGoogle Scholar
  7. 7.
    Vince, D.G., Dixon, K.J., Cothren, R.M., Cornhill, J.F.: Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Computerized Medical Imaging and Graphics 24, 221–229 (2000)CrossRefGoogle Scholar
  8. 8.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distribution. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  9. 9.
    Keramidas, E.G., Iakovidis, D., Maroulis, D., Karkanis, S.A.: Efficient and Effective Ultrasound Image Analysis Scheme for Thyroid Nodule Detection. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1052–1060. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Caballero, K., Barajas, J., Pujol, O., Savatella, N., Radeva, P.: In-vivo IVUS Tissue Classification A Comparison Between Normalized Image Reconstruction and RF Signals Analysis Progress in Pattern Recognition. Image Analysis and Applications 4225, 137–146 (2006)Google Scholar
  11. 11.
    Rotger, D., Radeva, P., Rodriguez, O., Mauri, J.: Near Real-Time Plaque Segmentation of IVUS. Computers in Cardiology 30, 69–72 (2003)Google Scholar
  12. 12.
    Brunenberg, E., Pujol, O., Romeny, B.H., Radeva, P.: Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake. Medical Image Computing and Computer-Assisted Intervention 4191, 9–16 (2006)Google Scholar
  13. 13.
    Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)MATHGoogle Scholar
  14. 14.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, London (2006)MATHGoogle Scholar
  15. 15.
    Skouroliakou, C., Lyra, M., Antoniou, A., Vlahos, L.: Quantitative image analysis in sonograms of the thyroid gland. Nuclear Instruments and Methods in Physics Research 569, 606–609 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dimitris K. Iakovidis
    • 1
  • Eystratios G. Keramidas
    • 1
  • Dimitris Maroulis
    • 1
  1. 1.Dept. of Informatics and TelecommunicationsUniversity of AthensAthensGreece

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