Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/6956
Title: An image‑processing method to detect sub‑optical features based on understanding noise in intensity measurements
Authors: Bhatia, Tripta
Keywords: Image noise
Signal-to-noise ratio
Optimum smoothening
Tubules Fluid lamellar phase
Issue Date: 4-Jul-2018
Publisher: Springer
Citation: European Biophysics Journal, 2018, Vol.47 p531-538
Abstract: Accurate 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 88±23 and 6860±50 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.
Description: Open Access
URI: http://hdl.handle.net/2289/6956
ISSN: 0175-7571
1432-1017 (Online)
Alternative Location: https://doi.org/10.1007/s00249-017-1273-z
Copyright: 2018 The Author
Appears in Collections:Research Papers (SCM)

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