Re: HELP!!!-segmentation and quantification of DIC images
Posted by
Till Bretschneider-2 on
URL: http://imagej.273.s1.nabble.com/HELP-segmentation-and-quantification-of-DIC-images-tp5022977p5022997.html
Hi Joel,
QuimP is available through the Fiji updater which is the easiest way to install it.
Help->Update
Click "Manage Update Sites" and tick "QuimP" in the list.
Apply changes and restart Fiji.
Other ways to install it are described on the homepage:
https://warwick.ac.uk/fac/sci/dcs/people/till_bretschneider/quimpIn Fiji you find the DIC filter installed under Plugins->QuimP-19.08.01->DIC
The method used is described in Zvi Kam. Microscopic differential interference contrast image processing by line integration (LID) and deconvolution. Bioimaging, 6(4):166–176, 1998.
Some rudimentary help is here:
https://pilip.lnx.warwick.ac.uk/docs/master/QuimP_Guide.html#x1-250001In terms of segmentation there are a number of options:
For images like the vesicle example some simple form of thresholding might do, or you can produce a mask based on thresholding which feeds into a random-walker method for segmentation (Plugins->QuimP-19.08.01->RandomWalk). If clusters of vesicles need to be resolved you would need to manually provide seeds for each particle in the cluster, or use a watershed method afterwards. The Boa plugin gives you full control over segmentation with the possibility to manually correct. QuimP contains a number of tools for detailed time-series analyses of shape changes.
We have tested quite a few different approaches to processing DIC images. Although, there are more sophisticated ones than the one by Kam, we found that in practice the latter is very reliable and essentially has only one free parameter that needs tuning.
Most people will record DIC images alongside fluorescence because usually they come for free. However, very few take care and have the skills to actually optimise their DIC setup. Therefore, ,ost DIC images I got to see are of very poor quality. If possible, detailed shape analyses should be based on fluorescence and 3D optical sectioning.
Best wishes,
Till
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