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dc.contributor.authorParker, Albert E.
dc.contributor.authorChristen, J. A.
dc.contributor.authorLorenz, Lindsey A.
dc.contributor.authorSmith, Heidi J.
dc.date.accessioned2021-09-20T20:23:36Z
dc.date.available2021-09-20T20:23:36Z
dc.date.issued2020-05
dc.identifier.citationParker, A.E., Christen, J.A., Lorenz, L., & Smith, H. (2020). Optimal surface estimation and thresholding of confocal microscope images of biofilms using Beer's Law. Journal of Microbiological Methods, 174, 105943. https://doi.org/10.1016/j.mimet.2020.105943en_US
dc.identifier.issn0167-7012
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/16457
dc.description.abstractBeer's Law explains how light attenuates into thick specimens, including thick biofilms. We use a Bayesian optimality criterion, the maximum of the posterior probability distribution, and computationally efficiently fit Beer's Law to the 3D intensity data collected from thick living biofilms by a confocal scanning laser microscope. Using this approach the top surface of the biofilm and an optimal image threshold can be estimated. Biofilm characteristics, such as bio-volumes, can be calculated from this surface. Results from the Bayesian approach are compared to other approaches including the method of maximum likelihood or simply counting bright pixels. Uncertainty quantification (i.e., error bars) can be provided for the parameters of interest. This approach is applied to confocal images of stained biofilms of a common lab strain of Pseudomonas aeruginosa, stained biofilms of Janthinobacterium isolated from the Antarctic, and biofilms of Staphylococcus aureus that have been genetically modified to fluoresce green.en_US
dc.language.isoen_USen_US
dc.rights© This manuscript version is made available under the CC-BY-NC-ND 4.0 licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleOptimal surface estimation and thresholding of confocal microscope images of biofilms using Beer's Lawen_US
dc.typeArticleen_US
mus.citation.extentfirstpage105943en_US
mus.citation.journaltitleJournal of Microbiological Methodsen_US
mus.citation.volume174en_US
mus.identifier.doi10.1016/j.mimet.2020.105943en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentCenter for Biofilm Engineering.en_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.departmentMicrobiology & Immunology.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.relation.researchgroupCenter for Biofilm Engineering.en_US
mus.data.thumbpage4en_US


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© This manuscript version is made available under the CC-BY-NC-ND 4.0 license
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