Biospeckle laser spectral analysis under Inertia Moment, Entropy and Cross-Spectrum methods

Authors

Cassia M. B. Nobre, Roberto A. B. Jr, Antônio G. Costa, R. R. Cardoso, Washington S. da Silva, Thelma Sáfadi.

Published

1 June 2009

Publication details

Optics Communications, 282:11, 2236-2242

Links

DOI

 

Abstract

Biospeckle or dynamic speckle can be used as a method for analysing activity, biologic or not, from materials illuminated with laser beam. The Spatial Temporal Speckle (STS) contains data of time information of dynamic speckle and it is used as input for many techniques allowing the analysis of the activity which is being monitored. One question that rises from the manipulation of the STS is related with the information inside it, in particular, whether it is possible to access different frequency behaviors in the time series presented in the STS pattern. This study presents the Inertia Moment, the Wavelets based Entropy and the Cross-Spectrum analysis as approaches that can be used for evaluating the STS spectral content. In a simulation, STS lines have been created based on many frequencies of the fundamental harmonic. This was done for verifying as each method acts when analysing different frequencies, varying harmonics offset and amplitude. These techniques were applied to real database, to validate their action mechanism in real samples. The results present that all techniques were able to verify the spectral content of different harmonics. Inertia Moment was more efficient on analysing high frequencies, because it is a second order moment, being able to obtain more information from high variations on activity. Entropy and Cross-Spectrum, in turn, were better on differing lower frequencies. This was attributed to the convolution proccess, which is present in both methods, filtering high frequencies. Although, any of them returned informations on both high and low frequencies at the same time, they can be used simultaneously, since Entropy and Cross-Spectrum were complementary to Inertia Moment.