Abstract

We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.

© 2013 Optical Society of America

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  1. H. Rabal and R. Braga, eds., Dynamic Laser Speckle and Applications (CRS, Taylor & Francis, 2008).
  2. R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).
    [CrossRef]
  3. G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
    [CrossRef]
  4. H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
    [CrossRef]
  5. I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
    [CrossRef]
  6. A. Dai Pra, I. Passoni, and H. Rabal, “Evaluation of laser dynamic speckle signals applying granular computing,” Signal Process. 89, 266–274 (2009).
    [CrossRef]
  7. E. Blotta, V. Ballarin, and H. Rabal, “Decomposition of biospeckle signals through granulometric size distribution,” Opt. Lett. 34, 1201–1203 (2009).
    [CrossRef]
  8. I. Passoni, H. Rabal, and C. Arizmendi, “Characterizing dynamic speckle time series with the Hurst coefficient concept,” Fractals 12, 319–329 (2004).
    [CrossRef]
  9. G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
    [CrossRef]
  10. H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
    [CrossRef]
  11. G. Romero, E. Alanis, and H. Rabal, “Statistics of the dynamic speckle produced by a rotating diffuser and its application to the assessment of paint drying,” Opt. Eng. 39, 1652–1658 (2000).
    [CrossRef]
  12. M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
    [CrossRef]
  13. A. Federico and G. Kaufmann, “Evaluation of dynamic speckle activity using the empirical mode decomposition method,” Opt. Commun. 267, 287–294 (2006).
    [CrossRef]
  14. 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]
  15. G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
    [CrossRef]
  16. H. Z. Cummins and H. L. Swinney, “Light beating spectroscopy,” in Vol. 8 of Progress in Optics (North-Holland, 1970), pp. 133–200.
  17. E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
    [CrossRef]
  18. I. Rish, “An empirical study of the naive Bayes classifier,” in RC 22230 IBM Research Report. IBM Research Division (Thomas J. Watson Research Center, Yorktown Heights, NY, 2001).
  19. E. Parzen, “On estimation of a probability function and mode,” Ann. Math. Stat. 33, 1065–1076 (1962).
    [CrossRef]
  20. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell. 97, 273–324 (1997).
    [CrossRef]
  21. M. Stone, “Cross-validatory choice and assessment of statistical predictions,” J. R. Stat. Soc. 36, 111–147 (1974).
  22. S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
    [CrossRef]
  23. G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.
  24. M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
    [CrossRef]
  25. L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

2012 (1)

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

2011 (1)

E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
[CrossRef]

2010 (2)

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

2009 (3)

A. Dai Pra, I. Passoni, and H. Rabal, “Evaluation of laser dynamic speckle signals applying granular computing,” Signal Process. 89, 266–274 (2009).
[CrossRef]

E. Blotta, V. Ballarin, and H. Rabal, “Decomposition of biospeckle signals through granulometric size distribution,” Opt. Lett. 34, 1201–1203 (2009).
[CrossRef]

G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
[CrossRef]

2006 (1)

A. Federico and G. Kaufmann, “Evaluation of dynamic speckle activity using the empirical mode decomposition method,” Opt. Commun. 267, 287–294 (2006).
[CrossRef]

2005 (2)

G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
[CrossRef]

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

2004 (1)

I. Passoni, H. Rabal, and C. Arizmendi, “Characterizing dynamic speckle time series with the Hurst coefficient concept,” Fractals 12, 319–329 (2004).
[CrossRef]

2003 (1)

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

2000 (1)

G. Romero, E. Alanis, and H. Rabal, “Statistics of the dynamic speckle produced by a rotating diffuser and its application to the assessment of paint drying,” Opt. Eng. 39, 1652–1658 (2000).
[CrossRef]

1997 (1)

R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell. 97, 273–324 (1997).
[CrossRef]

1996 (2)

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]

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

1985 (1)

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

1974 (1)

M. Stone, “Cross-validatory choice and assessment of statistical predictions,” J. R. Stat. Soc. 36, 111–147 (1974).

1973 (1)

R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).
[CrossRef]

1962 (1)

E. Parzen, “On estimation of a probability function and mode,” Ann. Math. Stat. 33, 1065–1076 (1962).
[CrossRef]

Alanis, E.

G. Romero, E. Alanis, and H. Rabal, “Statistics of the dynamic speckle produced by a rotating diffuser and its application to the assessment of paint drying,” Opt. Eng. 39, 1652–1658 (2000).
[CrossRef]

Alanís, E.

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

Arizaga, R.

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
[CrossRef]

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
[CrossRef]

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

Arizmendi, C.

I. Passoni, H. Rabal, and C. Arizmendi, “Characterizing dynamic speckle time series with the Hurst coefficient concept,” Fractals 12, 319–329 (2004).
[CrossRef]

Asakura, T.

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

Baldwin, G.

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

Ballarin, V.

E. Blotta, V. Ballarin, and H. Rabal, “Decomposition of biospeckle signals through granulometric size distribution,” Opt. Lett. 34, 1201–1203 (2009).
[CrossRef]

Ballarín, V.

E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
[CrossRef]

Blotta, E.

E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
[CrossRef]

E. Blotta, V. Ballarin, and H. Rabal, “Decomposition of biospeckle signals through granulometric size distribution,” Opt. Lett. 34, 1201–1203 (2009).
[CrossRef]

Briers, J. D.

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]

Brun, M.

E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
[CrossRef]

Cap, N.

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

Cummins, H. Z.

H. Z. Cummins and H. L. Swinney, “Light beating spectroscopy,” in Vol. 8 of Progress in Optics (North-Holland, 1970), pp. 133–200.

Dai Pra, A.

A. Dai Pra, I. Passoni, and H. Rabal, “Evaluation of laser dynamic speckle signals applying granular computing,” Signal Process. 89, 266–274 (2009).
[CrossRef]

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

Dai Pra, A. L.

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

Dinstein, I.

R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).
[CrossRef]

Federico, A.

A. Federico and G. Kaufmann, “Evaluation of dynamic speckle activity using the empirical mode decomposition method,” Opt. Commun. 267, 287–294 (2006).
[CrossRef]

Froilán Gonzalez, J.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

Fujii, H.

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

Guzman, M.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

Guzmán, M.

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

Haralick, R. M.

R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).
[CrossRef]

John, G. H.

R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell. 97, 273–324 (1997).
[CrossRef]

Kaufmann, G.

A. Federico and G. Kaufmann, “Evaluation of dynamic speckle activity using the empirical mode decomposition method,” Opt. Commun. 267, 287–294 (2006).
[CrossRef]

Kohavi, R.

R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell. 97, 273–324 (1997).
[CrossRef]

Meschino, G.

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.

Murialdo, S.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.

Nohira, K.

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

Ohura, T.

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

Pajuelo, M.

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

Parzen, E.

E. Parzen, “On estimation of a probability function and mode,” Ann. Math. Stat. 33, 1065–1076 (1962).
[CrossRef]

Passoni, I.

A. Dai Pra, I. Passoni, and H. Rabal, “Evaluation of laser dynamic speckle signals applying granular computing,” Signal Process. 89, 266–274 (2009).
[CrossRef]

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

I. Passoni, H. Rabal, and C. Arizmendi, “Characterizing dynamic speckle time series with the Hurst coefficient concept,” Fractals 12, 319–329 (2004).
[CrossRef]

Passoni, L.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.

Passoni, L. I.

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

Rabal, H.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
[CrossRef]

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

A. Dai Pra, I. Passoni, and H. Rabal, “Evaluation of laser dynamic speckle signals applying granular computing,” Signal Process. 89, 266–274 (2009).
[CrossRef]

E. Blotta, V. Ballarin, and H. Rabal, “Decomposition of biospeckle signals through granulometric size distribution,” Opt. Lett. 34, 1201–1203 (2009).
[CrossRef]

G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
[CrossRef]

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

I. Passoni, H. Rabal, and C. Arizmendi, “Characterizing dynamic speckle time series with the Hurst coefficient concept,” Fractals 12, 319–329 (2004).
[CrossRef]

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

G. Romero, E. Alanis, and H. Rabal, “Statistics of the dynamic speckle produced by a rotating diffuser and its application to the assessment of paint drying,” Opt. Eng. 39, 1652–1658 (2000).
[CrossRef]

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.

Rabal, H. J.

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
[CrossRef]

Rish, I.

I. Rish, “An empirical study of the naive Bayes classifier,” in RC 22230 IBM Research Report. IBM Research Division (Thomas J. Watson Research Center, Yorktown Heights, NY, 2001).

Romero, G.

G. Romero, E. Alanis, and H. Rabal, “Statistics of the dynamic speckle produced by a rotating diffuser and its application to the assessment of paint drying,” Opt. Eng. 39, 1652–1658 (2000).
[CrossRef]

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

Scandurra, A.

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

Sendra, G.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

Sendra, G. H.

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
[CrossRef]

G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
[CrossRef]

Shanmugan, K.

R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).
[CrossRef]

Shintomi, Y.

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

Stone, M.

M. Stone, “Cross-validatory choice and assessment of statistical predictions,” J. R. Stat. Soc. 36, 111–147 (1974).

Swinney, H. L.

H. Z. Cummins and H. L. Swinney, “Light beating spectroscopy,” in Vol. 8 of Progress in Optics (North-Holland, 1970), pp. 133–200.

Triv, M.

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

Trivi, M.

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
[CrossRef]

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
[CrossRef]

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.

Weber, C.

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

Webster, S.

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]

Ann. Math. Stat. (1)

E. Parzen, “On estimation of a probability function and mode,” Ann. Math. Stat. 33, 1065–1076 (1962).
[CrossRef]

Artif. Intell. (1)

R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell. 97, 273–324 (1997).
[CrossRef]

Fractals (1)

I. Passoni, H. Rabal, and C. Arizmendi, “Characterizing dynamic speckle time series with the Hurst coefficient concept,” Fractals 12, 319–329 (2004).
[CrossRef]

IEEE Trans. Syst. Man Cybern. (1)

R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973).
[CrossRef]

J. Biomed. Opt. (2)

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]

S. Murialdo, L. Passoni, M. Guzman, G. Sendra, H. Rabal, M. Trivi, and J. Froilán Gonzalez, “Discrimination of motile bacteria and filamentous fungi using dynamic speckle,” J. Biomed. Opt. 17, 056011 (2012).
[CrossRef]

J. Opt. Soc. Am. A (1)

G. H. Sendra, H. Rabal, R. Arizaga, and M. Trivi, “Vortex analysis in dynamic speckle images,” J. Opt. Soc. Am. A 26, 2634–2639 (2009).
[CrossRef]

J. R. Stat. Soc. (1)

M. Stone, “Cross-validatory choice and assessment of statistical predictions,” J. R. Stat. Soc. 36, 111–147 (1974).

Opt. Commun. (2)

I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005).
[CrossRef]

A. Federico and G. Kaufmann, “Evaluation of dynamic speckle activity using the empirical mode decomposition method,” Opt. Commun. 267, 287–294 (2006).
[CrossRef]

Opt. Eng. (2)

H. Rabal, R. Arizaga, N. Cap, M. Trivi, G. Romero, and E. Alanís, “Transient phenomena analysis using dynamic speckle patterns,” Opt. Eng. 35, 57–62 (1996).
[CrossRef]

G. Romero, E. Alanis, and H. Rabal, “Statistics of the dynamic speckle produced by a rotating diffuser and its application to the assessment of paint drying,” Opt. Eng. 39, 1652–1658 (2000).
[CrossRef]

Opt. Lasers Eng. (1)

M. Pajuelo, G. Baldwin, R. Arizaga, N. Cap, H. Rabal, and M. Trivi, “Bio-speckle assessment of bruising in fruits,” Opt. Lasers Eng. 40, 13–24 (2003).
[CrossRef]

Opt. Lett. (3)

G. H. Sendra, H. J. Rabal, R. Arizaga, and M. Trivi, “Biospeckle images decomposition in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005).
[CrossRef]

H. Fujii, T. Asakura, K. Nohira, Y. Shintomi, and T. Ohura, “Blood flow observed by time varying speckle,” Opt. Lett. 10, 104–106 (1985).
[CrossRef]

E. Blotta, V. Ballarin, and H. Rabal, “Decomposition of biospeckle signals through granulometric size distribution,” Opt. Lett. 34, 1201–1203 (2009).
[CrossRef]

Proc. SPIE (2)

G. H. Sendra, A. L. Dai Pra, L. I. Passoni, R. Arizaga, H. J. Rabal, and M. Triv, “Biospeckle descriptors: a performance comparison,” Proc. SPIE 7387, 73871K (2010).
[CrossRef]

M. Guzmán, G. Meschino, A. L. Dai Pra, L. Passoni, H. Rabal, and M. Trivi, “Dynamic laser speckle: decision models with computational intelligence techniques,” Proc. SPIE 7387, 738717 (2010).
[CrossRef]

Signal Process. (2)

E. Blotta, V. Ballarín, M. Brun, and H. Rabal, “Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings,” Signal Process. 91, 2395–2403 (2011).
[CrossRef]

A. Dai Pra, I. Passoni, and H. Rabal, “Evaluation of laser dynamic speckle signals applying granular computing,” Signal Process. 89, 266–274 (2009).
[CrossRef]

Other (5)

I. Rish, “An empirical study of the naive Bayes classifier,” in RC 22230 IBM Research Report. IBM Research Division (Thomas J. Watson Research Center, Yorktown Heights, NY, 2001).

H. Z. Cummins and H. L. Swinney, “Light beating spectroscopy,” in Vol. 8 of Progress in Optics (North-Holland, 1970), pp. 133–200.

H. Rabal and R. Braga, eds., Dynamic Laser Speckle and Applications (CRS, Taylor & Francis, 2008).

L. I. Passoni, A. L. Dai Pra, A. Scandurra, G. Meschino, C. Weber, M. Guzmán, H. Rabal, and M. Trivi, “Improvements in the visualization of segmented areas of patterns of dynamic laser speckle in advances in self-organizing maps,” in Advances in Intelligent Systems and Computing, P. A. Estévez, J. C. Príncipe, and P. Zegers, eds. (Springer, 2012), pp. 163–171.

G. Meschino, S. Murialdo, L. Passoni, H. Rabal, and M. Trivi, “Biospeckle image stack process based on artificial neural networks,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2010), pp. 4056–4059.

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Figures (7)

Fig. 1.
Fig. 1.

Pixel intensity over time.

Fig. 2.
Fig. 2.

Experimental set up for the recording of biospeckle images.

Fig. 3.
Fig. 3.

Estimated probability density functions of the three chosen descriptors for the bruised region of the training sample.

Fig. 4.
Fig. 4.

Descriptor images of a bruised apple sample used as the training set: (a) DWT Shannon Entropy descriptor; (b) Range descriptor; (c) Fujii descriptor. The color bar shows the image intensity scale. In this sample the bruised region is located at the middle bottom and the inert part is in the right upper corner.

Fig. 5.
Fig. 5.

(a) RGB image created with the results of computing the Bayesian model of the sample in Figure 4. Blue plane is associated with the inert region, the green plane with the healthy region and the red plane the bruised region. (b), (c) and (d) composition of four expanded pixels.

Fig. 6.
Fig. 6.

Naïve Bayes classifier applied to the sample shown in Figure 5 (training set): (a) results of the Naïve Bayes classifier and (b) blurred version of figure (a).

Fig. 7.
Fig. 7.

Results of a testing case. The Naïve Bayes classifier discovers a bruised area in another apple sample not used in the training phase (assumed unknown). (a) RGB image, (b) Naïve Bayes classifier image, and (c) blurred image of the Naïve Bayes classifier.

Tables (1)

Tables Icon

Table 1. Confusion Matrix Showing How Many Pixels of Each Class are Correctly Identified (as a Percentage) in the Principal Diagonala

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

P(ck/d)=P(ck)×P(d/ck)P(d),
P(d)=i=1nP(ci)P(d/ci).
P(ck/d)=P(c)×P(d/ck)i=1nP(c)P(d/ci)P(ck/d)=P(d/ck)i=1nP(d/ci).
P(d/ck)=j=1mP(dj/ck).
P(ck/d)=j=1mP(dj/ck)i=1nj=1mP(dj/ci).
classify(d1,...,d3)=argmaxcj=1nP(dj/ck)i=13j=1nP(dj/ci),
Rx,y=max(Ix,y,t)min(Ix,y,t),
Fx,y=n=1N|Ix,y(n)Ix,y(n1)||Ix,y(n)+Ix,y(n1)|,
Ej(i)=1Njk|Cj(k)|2,
Etotal(i)=jEj(i),
pj(i)=Ej(i)Etotal(i).
SWx,y(i)=j<0pj(i)ln[pj(i)].

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