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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/7118
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dc.contributor.authorBhatia, Tripta-
dc.date.accessioned2019-01-15T15:13:52Z-
dc.date.available2019-01-15T15:13:52Z-
dc.date.issued2018-07-
dc.identifier.citationEuropean Biophysics Journal with Biophysics Letters, 2018 Vol. 47, p531–538en_US
dc.identifier.issn0175-7571-
dc.identifier.issn1432-1017-
dc.identifier.urihttp://hdl.handle.net/2289/7118-
dc.descriptionOpen Accessen_US
dc.description.abstractAccurate quantitative analysis of image data requires that we distinguish between fluorescence intensity (true signal) and the noise inherent to its measurements to the extent possible. We image multilamellar membrane tubes and beads that grow from defects in the fluid lamellar phase of the lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine dissolved in water and water-glycerol mixtures by using fluorescence confocal polarizing microscope. We quantify image noise and determine the noise statistics. Understanding the nature of image noise also helps in optimizing image processing to detect sub-optical features, which would otherwise remain hidden. We use an image-processing technique "optimum smoothening" to improve the signal-to-noise ratio of features of interest without smearing their structural details. A high SNR renders desired positional accuracy with which it is possible to resolve features of interest with width below optical resolution. Using optimum smoothening, the smallest and the largest core diameter detected is of width [Formula: see text] and [Formula: see text] nm, respectively, discussed in this paper. The image-processing and analysis techniques and the noise modeling discussed in this paper can be used for detailed morphological analysis of features down to sub-optical length scales that are obtained by any kind of fluorescence intensity imaging in the raster mode.en_US
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.relation.urihttps://doi.org/10.1007/s00249-017-1273-z)en_US
dc.rights© The Author(s) 2018en_US
dc.subjectFluid lamellar phaseen_US
dc.subjectImage noiseen_US
dc.subjectOptimum smootheningen_US
dc.subjectSignal-to-noise ratioen_US
dc.subjectTubulesen_US
dc.titleAn image‑processing method to detect sub‑optical features based on understanding noise in intensity measurementsen_US
dc.typeArticleen_US
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