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

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Bibliographic Details
Title: Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images
Authors: Tsibidis, George D.1 tsibidis@iesl.forth.gr, Burroughs, Nigel J.2, Gaze, William3, Wellington, Elizabeth M.H.3
Source: Micron. Dec2011, Vol. 42 Issue 8, p911-920. 10p.
Subjects: Salmonella typhimurium, Acanthamoeba, Image analysis, Biological classification, Bacterial typing, Protozoa, Microorganism populations
Abstract: 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 &y& Elsevier]
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Database: Engineering Source
Description
Abstract: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 &y& Elsevier]
ISSN:09684328
DOI:10.1016/j.micron.2011.06.010