English

# Item

ITEM ACTIONSEXPORT

Released

Journal Article

#### An image-processing method to detect sub-optical features based on understanding noise in intensity measurements

##### MPS-Authors
/persons/resource/persons191852

Bhatia,  Tripta
Rumiana Dimova, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

##### External Ressource
No external resources are shared
##### Fulltext (public)

Article.pdf
(Publisher version), 2MB

##### Supplementary Material (public)
There is no public supplementary material available
##### Citation

Bhatia, T. (2018). An image-processing method to detect sub-optical features based on understanding noise in intensity measurements. European Biophysics Journal, 47(5), 531-538. doi:10.1007/s00249-017-1273-z.

Cite as: http://hdl.handle.net/21.11116/0000-0000-73F8-7
##### 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 $$88 \pm 23$$ 88 ± 23 and $$6860 \pm 50$$ 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.