IntDen vs RawIntDen


Hi All,
I am trying to measure fluorescence intensity using ImageJ but the concept of IntDen confuses me…

So the IntDen = the product of area and mean gray value, and RawIntDen= sum of the values of the pixels. What exactly is the product of area and mean gray value, and how is that different from RawIntDen?

If I am aiming on measuring mean intensity/pixel, I assume IntDen is the correct option?

Any suggestion would be greatly appreciated! Thanks in advance.




Quoting the ImageJ Analyze Menu page

Integrated Density - Calculates and displays two values: “IntDen” (the product of Area and Mean Gray Value) and “RawIntDen” (the sum of the values of the pixels in the image or selection). “RawIntDen” is only available in ImageJ 1.44c or later. “IntDen” and “RawIntDen” values are the same for uncalibrated image. The Dot Blot Analysis example demonstrates how to use this option to analyze a dot blot assay.”

Also - perhaps this old ImageJ listserv thread on this same topic will help as well…

Hope this helps!

eta :slight_smile:


Hi @thomas,

No, mean gray value is the same as mean intensity.

I made a table a while back to summarize these terms as many people find them confusing.

As you can see from the 3rd column, a small, bright object can have the same integrated density as a large, dim object.

Note that many factors influence the final pixel value, so it is quite difficult to find the absolute concentration of a probe from the mean intensity of a micrograph! I only mean to say that these parameters are correlated, for some range of values, if the imaging system is working well.

Hope it helps.

Image J on fluorescence signals on chromosomes

Hi @tswayne,
Thanks and it helps a lot!

So does it mean the difference between mean grey value and integrated density is that integrated density takes area into account? Then under what circumstance is integrated density measurement preferred?



You’re welcome! :slight_smile:

Yes, IntDen takes area into account.

The preferred measurement depends on the underlying question you’re trying to answer.
Can you share in general terms what the experimental question is?


Hi @tswayne,
I used IHC to label my molecule of interest in brain sections, and now I want to quantify how many of them are in the sections. I did a bit research online and people seem to use IntDen measurement. That is why it confuses me:)



Hi @thomas,

Just to be sure – how many of what? How many cells containing the molecule, or how many molecules themselves?



Hi @tswayne,
Molecules themselves;)



Hi @thomas,

Do note @tswayne’s comment that you won’t get an absolute measure of the number of molecules, only an approximate concentration relative to other cells in the section, and compared to other sections if you have stained in exactly the same way (and preferably at the same time).

Also, if you follow the link and read the example about dot blots, be aware that the example is a bit old, and only shows the IntDen, as ImageJ used to give. ImageJ now gives IntDen and RawIntDen. Only RawIntDen is actually the integrated density (ie, sum of all pixels in the ROI), and IntDen is the integrated density relative to the scaling of the image. So if the image is unscaled (as you can see at the top of the image title bar it will only say n x y pixels), the two values are the same, but if it is scaled (it will say p x q microns (or whatever) then give the size in pixels in parentheses), then the IntDen is the mean intensity per scaling, eg mean per micron squared if it is in microns. This is essentially the same as taking the mean, except that is mean per pixel.

So to go back to your question about when Integrated Density is preferred, it is useful if you wish to know the absolute intensity per cell for example, but irrespective of cell size, in which case the RawIntDen value can be used. In practice, this is trickier for quantification. For example, have you imaged the entire cell volume in every measured cell?



Also note that if your image is scaled and calibrated, the IntDen measurement will account for that as well.

Here’s an illustrative example measuring the integrated density of the Blobs sample image after setting various combinations of pixel size and intensity calibration:


@thomas, in addition to the important comments by @imagejan and @Glyn_Nelson, note that for the most sensitive comparison you should subtract background from each measurement.

You can subtract and even out background using the ImageJ Process > Subtract Background… but there is usually some additional background especially in tissue like brain.

If your measurement of choice is Mean, then measure the mean of a background region and subtract that number from each mean value you get on your sections.

If your measurement of choice is IntDen or RawIntDen, then you need to subtract the background mean from each pixel. One way to do this is:

adjusted IntDen = IntDen of experimental ROI - (mean of background ROI * area of experimental ROI)


Hi all,
Thanks for joining the discussion:)

@imagejan Please correct me if I understood it wrong. So if the image is scaled, does it mean the IntDen (not the RawIntDen) = mean grey value?

@tswayne Yes I have been playing around with the subtract background. The larger the rolling ball radius is, the more “even” background I will get. But does it mean the rolling ball radius should always be the larger the better?

And if the RawIntDen gives us the absolute intensity, what do RawIntDen and mean grey value tell us in real world? What is the different between measuring the mean grey value of the molecule of interest and the RawIntDen from the tissue section? In either case I can still compare the measurement between treatments, but why should I choose one over the other?



Hi @thomas,

Subtracting background: the best value is based on trial and error. If the value is too large, it won’t fix an uneven background. If it’s too small, you will lose parts of your image features (usually cells).

Measurements: IntDen is not mean grey value.
You may want to check the user guide as @etarena suggested.

Maybe the best way to get a feeling for this would be: take one of your images, or one of the ImageJ samples (File > Open Samples), and play around with it.

Draw some ROIs in areas of obviously different brightness.

Draw large and small ROIs in areas of the same apparent brightness.

Measure each ROI and look at the values you get.
Then I think you will get more insight into what the values mean.

Once you feel comfortable with that, try measuring positive and negative controls for your experiment.
Then you will be better prepared to make the decision of which measurement to use.

Hope this helps.