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Multiscale spatial segregation analysis in digital images of biofilms()

Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Ty...

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Detalles Bibliográficos
Autores principales: Dogsa, Iztok, Mandic-Mulec, Ines
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10542597/
https://www.ncbi.nlm.nih.gov/pubmed/37790733
http://dx.doi.org/10.1016/j.bioflm.2023.100157
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author Dogsa, Iztok
Mandic-Mulec, Ines
author_facet Dogsa, Iztok
Mandic-Mulec, Ines
author_sort Dogsa, Iztok
collection PubMed
description Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Typically, this information is encoded in a digital image that represents the binary system, e.g., a microscopy image of a two species biofilm. To decode spatial segregation information, we have developed quantitative measures for evaluating the degree of the spatial scale-dependent segregation of two bacterial strains in a digital image. The constructed algorithm is based on the new segregation measures and overcomes drawbacks of existing approaches for biofilm segregation analysis. The new approach is implemented in a freely available software and was successfully applied to biofilms of two strains and bacterial suspensions for detection of the different spatial scale-dependent segregation levels.
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spelling pubmed-105425972023-10-03 Multiscale spatial segregation analysis in digital images of biofilms() Dogsa, Iztok Mandic-Mulec, Ines Biofilm Article Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Typically, this information is encoded in a digital image that represents the binary system, e.g., a microscopy image of a two species biofilm. To decode spatial segregation information, we have developed quantitative measures for evaluating the degree of the spatial scale-dependent segregation of two bacterial strains in a digital image. The constructed algorithm is based on the new segregation measures and overcomes drawbacks of existing approaches for biofilm segregation analysis. The new approach is implemented in a freely available software and was successfully applied to biofilms of two strains and bacterial suspensions for detection of the different spatial scale-dependent segregation levels. Elsevier 2023-09-20 /pmc/articles/PMC10542597/ /pubmed/37790733 http://dx.doi.org/10.1016/j.bioflm.2023.100157 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Dogsa, Iztok
Mandic-Mulec, Ines
Multiscale spatial segregation analysis in digital images of biofilms()
title Multiscale spatial segregation analysis in digital images of biofilms()
title_full Multiscale spatial segregation analysis in digital images of biofilms()
title_fullStr Multiscale spatial segregation analysis in digital images of biofilms()
title_full_unstemmed Multiscale spatial segregation analysis in digital images of biofilms()
title_short Multiscale spatial segregation analysis in digital images of biofilms()
title_sort multiscale spatial segregation analysis in digital images of biofilms()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10542597/
https://www.ncbi.nlm.nih.gov/pubmed/37790733
http://dx.doi.org/10.1016/j.bioflm.2023.100157
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