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

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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]
Copyright of Micron is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images
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  Data: <searchLink fieldCode="AR" term="%22Tsibidis%2C+George+D%2E%22">Tsibidis, George D.</searchLink><relatesTo>1</relatesTo><i> tsibidis@iesl.forth.gr</i><br /><searchLink fieldCode="AR" term="%22Burroughs%2C+Nigel+J%2E%22">Burroughs, Nigel J.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Gaze%2C+William%22">Gaze, William</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wellington%2C+Elizabeth+M%2EH%2E%22">Wellington, Elizabeth M.H.</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Micron%22">Micron</searchLink>. Dec2011, Vol. 42 Issue 8, p911-920. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Salmonella+typhimurium%22">Salmonella typhimurium</searchLink><br /><searchLink fieldCode="DE" term="%22Acanthamoeba%22">Acanthamoeba</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+classification%22">Biological classification</searchLink><br /><searchLink fieldCode="DE" term="%22Bacterial+typing%22">Bacterial typing</searchLink><br /><searchLink fieldCode="DE" term="%22Protozoa%22">Protozoa</searchLink><br /><searchLink fieldCode="DE" term="%22Microorganism+populations%22">Microorganism populations</searchLink>
– Name: Abstract
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  Data: 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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  Data: <i>Copyright of Micron is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.micron.2011.06.010
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        Text: English
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        Type: general
      – SubjectFull: Acanthamoeba
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      – SubjectFull: Image analysis
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      – SubjectFull: Biological classification
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      – SubjectFull: Bacterial typing
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      – SubjectFull: Protozoa
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      – SubjectFull: Microorganism populations
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      – TitleFull: Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images
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              Text: Dec2011
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