You've summarized pretty succinctly some of the major difficulties with quantitating images from fluorescence microscopy. Problems can arise at multiple steps of the process, not just with the interpretation of the images once they are acquired. One place to start would be with published papers in your field of interest that addressed the same problems that you face.

A few specific suggestions:

Chromosome Research (2008) 16:523–562 provides a general introduction to the problems and ways (ideally) to avoid them during sample preparation and image capture or (failing that) to try to correct for them thereafter. With respect to normalization among images in the presence of outliers, they note deletion of a fraction of the highest- or lowest-valued pixels, or normalization around median values to have consistent inter-quartile ranges of values. Those approaches, however, do have drawbacks as discussed there. What will work best for you depends too much on the details of the types of images to say much more.

Multi-color fluorescence images, if you have them, pose additional problems, discussed for example in BioData Mining (2016) 9:11. An earlier paper on multicolor imaging, Cytometry Part A (2005) Volume 64A: 101-109 might provide further help.

Once you understand the basic principles, you will be better able to use web searches for things like fluorescence microscopy data normalization to find what's most helpful for your study.

You've summarized pretty succinctly some of the major difficulties with quantitating images from fluorescence microscopy. Problems can arise at multiple steps of the process, not just with the interpretation of the images once they are acquired. One place to start would be with published papers in your field of interest that addressed the same problems that you face.

A few specific suggestions:

Chromosome Research (2008) 16:523–562 provides a general introduction to the problems and ways (ideally) to avoid them during sample preparation and image capture or (failing that) to try to correct for them thereafter. With respect to normalization among images in the presence of outliers, they note deletion of a fraction of the highest- or lowest-valued pixels, or normalization around median values to have consistent inter-quartile ranges of values. Those approaches, however, do have drawbacks as discussed there. What will work best for you depends too much on the details of the types of images to say much more.

Multi-color fluorescence images, if you have them, pose additional problems, discussed for example in BioData Mining (2016) 9:11. An earlier paper on multicolor imaging, Cytometry Part A (2005) Volume 64A: 101-109 might provide further help on registration of images and normalization.

Once you understand the basic principles, you will be better able to use web searches for things like fluorescence microscopy data normalization to find what's most helpful for your study.