NDLI: Biospeckle image stack process based on artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Meschino, G. Murialdo, S. Passoni, L. Rabal, H. Trivi, M.
Copyright Year	2010
Description	Author affiliation: CIOP, Argentina (Rabal, H.; Trivi, M.) \|\| Univ. Nacional de Mar del Plata, Argentina (Meschino, G.; Murialdo, S.; Passoni, L.)
Abstract	This paper proposes the identification of regions of interest in biospeckle patterns using unsupervised neural networks of the type Self-Organizing Maps. Segmented images are obtained from the acquisition and processing of laser speckle sequences. The dynamic speckle is a phenomenon that occurs when a beam of coherent light illuminates a sample in which there is some type of activity, not visible, which results in a variable pattern over time. In this particular case the method is applied to the evaluation of bacterial chemotaxis. Image stacks provided by a set of experiments are processed to extract features of the intensity dynamics. A Self-Organizing Map is trained and its cells are colored according to a criterion of similarity. During the recall stage the features of patterns belonging to a new biospeckle sample impact on the map, generating a new image using the color of the map cells impacted by the sample patterns. It is considered that this method has shown better performance to identify regions of interest than those that use a single descriptor. To test the method a chemotaxis assay experiment was performed, where regions were differentiated according to the bacterial motility within the sample.
Starting Page	4056
Ending Page	4059
File Size	1308049
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627620
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Microorganisms Speckle Pixel Training Adaptive optics Chemical lasers Optical imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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