Re: Segmentation of granular "stuff"

Posted by Michael Schmid on Jul 09, 2007; 1:29pm
URL: http://imagej.273.s1.nabble.com/Segmentation-of-granular-stuff-tp3698915p3698917.html

Hi Jolien,

first concerning the uploaded images - the blanks in the filenames
should be written "%20" in URLs. So, e.g., it is

www.waisman.wisc.edu/cmn/mutant%20high%20load.tif

Concerning faint images: There are plugins that may solve the
problem, e.g. the Stack NOrmalizer
   http://rsb.info.nih.gov/ij/plugins/normalizer.html
and the Background Subtraction and Image Normalization plugin
   http://rsb.info.nih.gov/ij/plugins/normalize.html

Another way to do it might be saturating a small percentage
of the pixels (Brightness&Contrast/Auto) and applying this as
new grayscale (8-bit images only). The Macro Recorder will show
you how this translates into macro code.

If some images don't have the bright granular "stuff" at all,
you may have to analyze at the histogram, e.g. determine the
"mode" (grayvalue of the highest peak, usually some kind of
background) and draw your conclusions from that (maybe divide
by some value proportional to the "mode")

Michael
________________________________________________________________

On 6 Jul 2007, at 20:42, Jolien Connor wrote:

> Hi,
>
> I am trying to do something similar to what many try
> to do with thresholding -- find a way for the software
> to pick out my pixels of interest away from the
> background.  In my case, it is granular "stuff" that
> are intracellular inclusions in the brain.  We have
> many image montages taken of mouse brain sections and
> we are trying to quantitate the percentage of specific
> brain regions occupied by the inclusions (percent
> volume).  Some of the monochrome images are here:
>
> www.waisman.wisc.edu/cmn/mutant high load.tif
> www.waisman.wisc.edu/cmn/mutant high load and
> faint.tif
> www.waisman.wisc.edu/cmn/mutant low load and faint.tif
> www.waisman.wisc.edu/cmn/control.tif
>
> They are very large since they are montages; contact
> me at [hidden email] if you want them and
> can't get them.
>
> If you look at the pictures of the mutants compared
> with the control, you can see what they are -- the
> bright granular stuff all over the place.  Cells that
> contain the inclusions sometimes have processes that
> show up, and we only want to pick out the brightest,
> most granular stuff, which are the inclusions.  We
> want a method that can tell the difference between
> mutants and controls, as well as between high load
> mutants and low load mutants (lots of them versus just
> a few).
>
> The biggest problem right now seems to be that some of
> our pictures are faint, possibly because the stain
> weakened, our mercury bulb Atto Arc controller is
> dying, or some other reason.  As humans, we can still
> pick them out, but we don't have a thresholding
> algorithm that can handle this problems.
>
> We have been using the Subtract Background function,
> then thresholding the image, and can't get consistent
> results.  We use a brightfield image that lines up
> with each monochrome image to trace our regions, then
> have ImageJ calculate the % Area occupied by pixels
> above threshold.  There's no threshold we could pick
> that would correctly differentiate between controls,
> low load mutants and high load mutants.  Any ideas?
>
> Thanks,
>
> Jolien Connor
> University of Wisconsin