The objects you’re measuring the intensity of are the secondary objects, which don’t have any explicit size measurements put on them in your pipeline; in the example image you sent me, the average area was about 115 pixels in area (with a SD of about 70), and visually there didn’t seem to be a huge range in size in the identified objects, but that may not be true for your other images. Can you confirm whether the image you sent was one that had objects with integrated intensity of >1000 in your hands? I can’t confirm on my end since I don’t have the images of the other channels to actually carry out the measurement.
All that being said, it’s pretty easy for you to validate this yourself (and worth knowing how to do!) rather than me doing it for you- you should look at the output of IdentifySecondaryObjects in test mode (open the eye to see the output) on an image that you know produces objects with integrated intensities that seem too high. Are there objects there that visually look too big? If so, you’ve got a couple options-
- Use Distance-B as your thresholding method in IdentifySecondaryObjects; this will allow you to set an upper limit on how far outside the nuclear diameter cell edges can be set, which should keep the size to within a constrained range.
- If for whatever reason Distance-B doesn’t work nicely on your images, after your IDSecondary step as it’s currently configured add a MeasureObjectSizeAndShape and a FilterObjects modules to throw out cells (and their corresponding nuclei) that are too big.
If it’s not that your objects are too big, then something is going wrong in your Rescale modules (I hadn’t realized you were using 32 bit input images before, which can sometimes behave a little quirkily)- hover over the output images on each of your RescaleIntensity modules to make sure that the rescaled output images never have intensities >1.