A BIOMETRIC IDENTIFICATION SYSTEM BASED ON THYROID TISSUE ECHO-MORPHOLOGY

José C. R. Seabra, Ana L. N. Fred

2009

Abstract

This paper proposes a biometric system based on features extracted from the thyroid tissue accessed through 2D ultrasound. Tissue echo-morphology, which accounts for the intensity (echogenicity), texture and structure has started to be used as a relevant parameter in a clinical setting. In this paper, features related to texture, morphology and tissue reflectivity are extracted from the ultrasound images and the most discriminant ones are selected as an input for a prototype biometric identification system. Several classifiers were tested, with the best results (90% identification rate) being achieved with the maximum a posteriori classifier. Another classifier which only takes into account the reflectivity parameter achieved a reasonable identification rate of 70%. This suggests that the acoustic impedance (reflectivity) of the tissue is a good parameter to discriminate between individuals. This paper shows the effectiveness of the proposed classification, which can be used not only as a new biometric modality but also as a diagnostic tool.

References

  1. J. Abbot and F. Thurstone. Acoustic speckle: Theory and experimental analysis. Ultrasound Imaging, 1:303- 324, 1979.
  2. C. Burckhardt. Speckle in ultrasound b-mode scans. IEEE Transations on Sonics and Ultrasonics, SU-25(1):1- 6, January 1978.
  3. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
  4. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell., 23(11):1222-1239, 2001.
  5. S. Catherine, L. Maria, A. Aristides, and V. Lambros. Quantitative image analysis in sonograms of the thyroid gland. Nuclear Instruments and Methods in Physics Research A, 569:606-609, December 2006.
  6. J. Dias, T. Silva, and J. Leitão. Adaptive restoration of speckled SAR images using a compound random markov field. In Procedings IEEE International Conference on Image Processing, Vol.II, pages 79-83, Chicago, USA, October 1998. IEEE.
  7. R. M. Haralick, Dinstein, and K. Shanmugam. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3:610-621, November 1973.
  8. V. Kolmogorov and R. Zabih. What energy functions can be minimizedvia graph cuts? IEEE Trans. Pattern Anal. Mach. Intell., 26(2):147-159, 2004.
  9. Guy Mailloux, Michel Bertrand, Robert Stampfler, and Serge Ethier. Computer analysis of echographic textures in hashimoto disease of the thyroid. Journal of Clinical Ultrasound, 14(7):521-527, 1986.
  10. O. V. Michailovich and A. Tannenbaum. Despeckling of medical ultrasound images. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 53(1):64-78, 2006.
  11. Salil Prabhakar, Josef Kittler, Davide Maltoni, Lawrence O'Gorman, and Tieniu Tan. Introduction to the special issue on biometrics: Progress and directions. IEEE Trans. Pattern Anal. Mach. Intell., 29(4):513-516, 2007.
  12. M.A. Savelonas, D.K. Iakovidis, N. Dimitropoulos, and D. Maroulis. Computational characterization of thyroid tissue in the radon domain. Computer-Based Medical Systems, 2007. CBMS 7807. Twentieth IEEE International Symposium on, pages 189-192, June 2007.
  13. Daniel Smutek, Radim Sara, Petr Sucharda, and Ludvik Tesar. Different types of image texture features in ultrasound of patients with lymphocytic thyroiditis. In ISICT 7803: Proceedings of the 1st international symposium on Information and communication technologies, pages 100-102. Trinity College Dublin, 2003.
  14. Daniel Smutek, Radim Sara, Petr Sucharda, and Ludvik Tesar. Image texture analysis of sonograms in chronic inflammations of thyroid gland. Ultrasound in Medicine and Biology, 29:1531-1543(13), November 2003.
  15. José Seabra, João Xavier, and João Sanches. Convex ultrasound image reconstruction with log-euclidean priors. In In Proc. of the Engineering in Medicine and Biology Conference, Vancouver, Canada, 2008.
  16. Tortora Gerard J Tortora, Gerard J. (Gerard Joseph). Principles of anatomy and physiology, 2000.
  17. C. M. van Bemmel, L. Spreeuwers, M.A. Viergever, and W.J. Niessen. Level-set based carotid artery segmentation for stenosis grading. In MICCAI 7802: Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II, pages 36-43, London, UK, 2002. Springer-Verlag.
  18. C. Xu and J.L. Prince. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3), March 1998.
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Paper Citation


in Harvard Style

C. R. Seabra J. and L. N. Fred A. (2009). A BIOMETRIC IDENTIFICATION SYSTEM BASED ON THYROID TISSUE ECHO-MORPHOLOGY . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 186-193. DOI: 10.5220/0001556501860193


in Bibtex Style

@conference{biosignals09,
author={José C. R. Seabra and Ana L. N. Fred},
title={A BIOMETRIC IDENTIFICATION SYSTEM BASED ON THYROID TISSUE ECHO-MORPHOLOGY},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={186-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001556501860193},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - A BIOMETRIC IDENTIFICATION SYSTEM BASED ON THYROID TISSUE ECHO-MORPHOLOGY
SN - 978-989-8111-65-4
AU - C. R. Seabra J.
AU - L. N. Fred A.
PY - 2009
SP - 186
EP - 193
DO - 10.5220/0001556501860193