Posted by
Rubiu, Elena (ext) on
Sep 18, 2014; 12:42pm
URL: http://imagej.273.s1.nabble.com/Background-subtraction-tp5009655p5009692.html
PS: thank you again!!
-----Ursprüngliche Nachricht-----
Von: ImageJ Interest Group [mailto:
[hidden email]] Im Auftrag von Rubiu, Elena (ext)
Gesendet: Donnerstag, 18. September 2014 14:45
An:
[hidden email]
Betreff: AW: Background subtraction
Hi Karsten, Gabor and everybody,
I kept reading in various guides and I ended up with this method.
I have 3 pics:
The first one has usually a background with pixels with a gray value of 0 or 1.
The other two have a background with higher level pixels, around 10.
So if I apply the same threshold to all the pics, in the first one only the fluorescence is highlighted, while in the other 2 also some background is highlighted.
Therefore I shouldn't process all three pics cause, while in the last two it alters mainly the background, in the first one it alters my actual data.
So as a maybe valid process, I found this:
Duplicate my pic; in one copy apply a median filter.
Then, I subtract the filtered image from my original one, thus obtaining very similar fluorescence data from the original, but with a background that ranges from 1 to 3, I would say.
Now, if I apply the same threshold to all the three pics, only the fluorescence is highlighted.
What do you think? Here is an example (page 72, figure 9.5)
http://blogs.qub.ac.uk/ccbg/files/2013/06/Analyzing_fluorescence_microscopy_images.pdfI hope this time it makes some sense and that you'll be still willing to help me.
Kind regards,
Elena
-----Ursprüngliche Nachricht-----
Von: ImageJ Interest Group [mailto:
[hidden email]] Im Auftrag von Csúcs Gábor
Gesendet: Mittwoch, 17. September 2014 12:20
An:
[hidden email]
Betreff: Re: Background subtraction
Dear Elena,
I fully agree with the previous comments of Karsten: you should use background substraction not to reduce the noise but correct for uneven illumination. However, if you want to do quantification (measure intensity) I would not use the rolling ball algorithm. There are more appropriate methods (like recording a fluorescent calibration image) that could be used and with those you could also avoid your current problems. If you look for papers you may search for "flat-field correction", "vignetting correction" or "shading correction".
Greetings Gabor
>>On 9/17/14 10:01 AM, "Rubiu, Elena (ext)"
>><
[hidden email]>
>>wrote:
>>
>>>Hi everybody,
>>>
>>>I am trying to analyze the fluorescence intensity of some pictures.
>>>I usually have three different pictures of the same object, that not
>>>only differ in fluorescence intensity but also in background noise
>>>(the less the intensity, the more the noise). To deal with the noise
>>>I'm using the "subtract background" function (rolling ball
>>>algorithm), then I'm selecting the area of interest with the
>>>threshold.
>>>
>>>My idea was to use the same parameters for all three pics (same
>>>rolling ball radius, same threshold limit) but the problem is that if
>>>I subtract the background of a relatively non-noisy pic then the
>>>threshold selection is working in a different way than with a noisy
>>>picture where I subtracted the background.
>>>
>>>Is there a way to "quantify" noise in a picture, so that I can decide
>>>if I should subtract the background or not?
>>>Or should I just treat every picture in the same way, not caring if
>>>the non-noisy pic's data is completely affected by the subtraction?
>>>
>>>I hope I could explain myself clearly and that you will be willing to
>>>help me.
>>>
>>>Thank you for your time!
>>>
>>>Kind regards,
>>>Elena Rubiu
>>>
>>>
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>>>ImageJ mailing list:
http://imagej.nih.gov/ij/list.html>>
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