Decomposition of biospeckle images in temporary spectral bands

Abstract

We present a method o f analysis of im ages of dynamic speckle based on the filtering in frequency bands of the temporary history of each pixel. Butterworth filters are applied to the temporary evolution, and different im ages are constructed showing the energy in each frequency band. Different degrees of activity of the sample in study, presum ably attributed to different origins, are found. The method is exemplified w ith im ages of bruising damage in fruits and of biological activity in germ inating com seeds. It is found that the activity in the bruised region of an apple differs from the activity of healthy regions in a certain character istic frequency range. The activity of the embryo can also be distinguished from that o f the endosperm in com seeds during germination. © 2005 Optical Society of America OCXS codes: 100.0100, 120.6150, 000.1430.Dynamic speckle or biospeckle is observed in biologi cal samples illuminated by laser light. The properties and applications of this phenomenon have been treated in the literature.1 3 There are several methods for display of speckle pattern activity. Laser speckle contrast analysis,4 proposed by Briers and Webster, uses the local con trast analysis of the integrated time varying speckle in a nonscanning full-field technique for monitoring capillary blood flow. In the method of Fujii et al. ,5 an image display based on the accumulation of differences between images divided by its average shows a microcirculation map of human skin surface. Konishi and Fujii6 used the signal-to-noise ratio in a real time application to visualize a blood flow map of the human retina. The method of Arizaga et al? is based on accumulating all possible differences between con secutive and nonconsecutive frames for each pixel of images in a seed analysis application. Recently other approaches were presented to characterization of atherosclerotic plaque by spatial and temporal speckle pattern analysis8 and to applying time dependent speckle in holographic optical coherence imaging to the study of tumor tissue. 9In the aforementioned methods, the result is a single image that shows a certain measure of the to tal activity of the sample. In this Letter we propose a method with which to analyze biospeckle activity based on decomposition of images in temporary spec tral bands that permits us to obtain several mea sures with a better defined meaning, thus adding to a better detailed characterization of the behavior of the samples.Most algorithms already developed for activity im ages discard time information and pool all the information concerning activity measurement in a single measure. Biological phenomena do not exhibit welldefined frequencies, but, in some cases, they differ in the frequency ranges where energy is concentrated. By using the filters in the frequency domain, one can discriminate phenomena in the sample that occur on different time scales. We present some examples to il lustrate the methodology, but this technique can be extended to other applications of dynamic speckle in biol...

Dynamic laser speckle and fuzzy mathematical morphology applied to studies of chemotaxis towards hydrocarbons

“…A CCD camera connected to a frame grabber registered a sequence of 8 bits images and 768 × 572 squared pixels, and stored it into the computer. A constant 4-Hz sampling frequency was used, and the camera integration exposure time was set to 40 ms. A pseudo-coloured image was obtained after a processing stage, where the higher energy regions correspond to higher bacterial intensity movement (Sendra et al, 2005).…”

Discrimination of motile bacteria from filamentous fungi using dynamic speckle

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“…The losses specification in each of this filters are the following: maximum tolerated at the pass band 1 dB and the minimum losses required at the reject band is the 40 dB. Instead of using Butterworth approximation like in previous work, 15 elliptic (Cauer) filters were used because, in spite of their higher implementation complexity, they exhibit a more selective frequency response than the Butterworth solution. Then the Average Energy E for each filtered signal (lowpass, bandpass and highpass) was computed as characteristic indices at each x, y image location: where p filtertype ðx; y; nÞ is the filtered intensity of the pixel at x, y location in the n th image, and N is the quantity of images in the sequence.…”

Speckle activity images based on the spatial variance of the phase

“…(3 ) using a 5 X 5 window. The window sivp is cnvpn b v a tr ia l a n d prrnr n n tim iz a tin n cn m (9 ) Therefore the sequence of computer simulated speckle patterns generated by Eq. (6) is produced using a phase distribution given by down along the direction normal to the surface of the sample, the phase < { >(ra, n, k) is evaluated from the phase was estimated using Eq.…”