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Micron. 2011 Dec;42(8):911-20. doi: 10.1016/j.micron.2011.06.010. Epub 2011 Jul 1.

Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images.

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  • 1Institute of Electronic Structure and Laser, Foundation for Research and Technology, P.O. Box 1527, Vassilika Vouton, 71110 Heraklion, Greece. tsibidis@iesl.forth.gr

Abstract

Interaction between bacteria and protozoa is an increasing area of interest, however there are a few systems that allow extensive observation of the interactions. A semi-automated approach is proposed to analyse a large amount of experimental data and avoid a time demanding manual object classification. We examined a surface system consisting of non nutrient agar with a uniform bacterial lawn that extended over the agar surface, and a spatially localised central population of amoebae. Location and identification of protozoa and quantification of bacteria population are performed by the employment of image analysis techniques in a series of spatial images. The quantitative tools are based on intensity thresholding, or on probabilistic models. To accelerate organism identification, correct classification errors and attain quantitative details of all objects a custom written Graphical User Interfaces has also been developed.

Copyright © 2011 Elsevier Ltd. All rights reserved.

PMID:
21775158
[PubMed - indexed for MEDLINE]
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