http://imagej.273.s1.nabble.com/Background-subtraction-tp5009655p5009663.html
illumination. However, if you want to do quantification (measure intensity)
I would not use the rolling ball algorithm. There are more appropriate
used and with those you could also avoid your current problems. If you
>>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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