Posted by
Rubiu, Elena (ext) on
Sep 17, 2014; 12:16pm
URL: http://imagej.273.s1.nabble.com/Background-subtraction-tp5009655p5009667.html
Hi Gabor,
thank you for your answer.
I had a look for those methods but it seems that it would take me more time than what I should actually dedicate to this.
Unfortunately I started using Imagej very little ago, and I'm not very confident with the right terminology. I'm not really able to tell if my images are "noisy" or they just present an uneven illumination.
What I would like to obtain in the end is that using the same threshold for all images, I get only my "interesting area" highlighted and not also some parts of the background.
I read that maybe filtering the image and then subtracting it from the original one could help with the final thresholding?
Thank you for your help!
-----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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http://imagej.nih.gov/ij/list.html>>
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