I agree with @Herbie and @7rebor that it will pay off to first improve your image quality by:
- improving the staining (check antibody specificity, use negative controls), and
- optimizing the acquisition (higher optical resolution by using higher magnification/higher NA objective).
This applies if the background fluorescence is due to auto-fluorescence in your tissue. If there’s unspecific binding of your antibodies in the cytoplasm, you’ll have to work on improving the staining procedure (more rigorous washing etc.)
Regarding the analysis, I’d like to add another suggestion. I quickly tried using Trainable Weka Segmentation on your png image. I was drawing the following traces to define foreground (collagen) and background (everything else) (after switching the LUT to Grays for visibility):
With the following settings:
I got this probability map (the brighter the intensity, the more “likely” it is classified as your class of interest by the trained classifier:
You might want to try improving this by training on more images (and of course, the better the images you feed in, the better the segmentation result will be).