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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 19 — Jul. 1, 2010
  • pp: 3753–3761

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

Pablo Etchepareborda, Alejandro Federico, and Guillermo H. Kaufmann  »View Author Affiliations


Applied Optics, Vol. 49, Issue 19, pp. 3753-3761 (2010)
http://dx.doi.org/10.1364/AO.49.003753


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Abstract

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi–Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

© 2010 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(120.3940) Instrumentation, measurement, and metrology : Metrology
(120.6150) Instrumentation, measurement, and metrology : Speckle imaging

ToC Category:
Instrumentation, Measurement, and Metrology

History
Original Manuscript: February 10, 2010
Manuscript Accepted: June 4, 2010
Published: June 25, 2010

Citation
Pablo Etchepareborda, Alejandro Federico, and Guillermo H. Kaufmann, "Sensitivity evaluation of dynamic speckle activity measurements using clustering methods," Appl. Opt. 49, 3753-3761 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-19-3753


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References

  1. J. W. Goodman, “Statistical properties of laser speckle patterns,” in Laser Speckle and Related Phenomena, J.C.Dainty, ed. (Springer-Verlag, 1975), pp. 9–75. [CrossRef]
  2. H. J. Rabal and R. A. Braga, Dynamic Laser Speckle and Applications (CRC, 2009).
  3. R. A. Braga, W. S. Silva, T. Sáfadi, and C. M. B. Nobre, “Time history speckle pattern under statistical view,” Opt. Commun. 281, 2443–2448 (2008). [CrossRef]
  4. P. A. Faccia, O. R. Pardini, J. I. Amalvy, N. Cap, E. E. Grumel, R. Arizaga, and M. Trivi, “Differentiation of the drying time of paints by dynamic speckle interferometry,” Prog. Org. Coat. 64, 350–355 (2008). [CrossRef]
  5. M. Pajuelo, G. Baldwin, H. J. Rabal, N. Cap, R. Arizaga, and M. Trivi, “Biospeckle assessment of bruising in fruits,” Opt. Laser Eng. 40, 13–24 (2003). [CrossRef]
  6. R. A. Braga, G. W. Horgan, A. M. Enes, D. Miron, G. F. Rabelo, and J. B. B. Filho, “Biological feature isolation by wavelets in biospeckle laser images,” Comput. Electron. Agric. 58, 132–132 (2007). [CrossRef]
  7. J. D. Briers and S. Webster, “Laser speckle contrast analysis (LASCA): a nonscanning, full-field technique for monitoring capillary blood flow,” J. Biomed. Opt. 1, 174–179 (1996). [CrossRef]
  8. I. Passoni, A. D. Pra, H. J. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005). [CrossRef]
  9. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).
  10. R. Xu and D. W. II, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005). [CrossRef] [PubMed]
  11. J. Bilmes, “A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models,” Tech. Rep. (International Computer Science Institute, 1998).
  12. B. Kosko, “Stochastic competitive learning,” IEEE Trans. Neural Netw. 2, 522–529 (1991). [CrossRef] [PubMed]
  13. T. Kohonen, “The self-organizing map,” Proc. IEEE , 78, 1464–1480 (1990). [CrossRef]
  14. S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, 1998).
  15. V. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory, and Methods (Wiley, 1998).
  16. L. Zunino, D. G. Pérez, M. Garavaglia, and O. A. Rosso, “Wavelet entropy of stochastic processes,” Physica A (Amsterdam) 379, 503–512 (2007). [CrossRef]
  17. D. D. Duncan and S. J. Kirkpatrick, “The copula: a tool for simulating speckle dynamics,” J. Opt. Soc. Am. A 25, 231–237 (2008). [CrossRef]
  18. Q. Huang and B. Dom, “Quantitative methods of evaluating image segmentation,” in Proceedings of the 1995 International Conference on Image Processing (IEEE, 1995), Vol. 3, pp. 3053–3056.
  19. R. R. Roldán, J. F. G. Lopera, C. A. Allah, J. M. Aroza, and P. L. L. Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Patt. Recog. 34, 969–980 (2001). [CrossRef]
  20. Z. Wang and A. C. Bovik, “A universal image quality index,” Signal Process. Lett. 9, 81–84 (2002). [CrossRef]
  21. B. Zhang, M. Hsu, and G. Forman, “Accurate recasting of parameter estimation algorithms using sufficient statistics for efficient parallel speed-up: demonstrated for center-based data clustering algorithms,” in Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2000) (2000), Vol. 1910, pp. 243–254. [PubMed]
  22. P. Ozdzynski, A. Lin, M. Liljeholm, and J. Beatty, “A parallel general implementation of Kohonen’s self-organizing map algorithm: performance and scalability,” Neurocomput. Var. Star Bull. 44, 567–571 (2002). [CrossRef]

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