I am a new user of ImageJ, so my questions might be very basic. I use ImageJ to analyse the distribution of particles in a matrix. I have some optical microscope pictures like this one (particles are in black) :
The end goal of any analysis is to get accurate results, so you should generally go with a method that does this. Usually by checking against ground truth. If you want to dig into it, take a subsection of several images where you manually count/highlight the areas you think are positive. Test a variety of automated thresholds on those images, and compare the areas that you manually checked. The main benefit to automation is that you can’t simply choose the threshold that gives you the answer that you want.
There are many thresholding methods exactly because there is no best method for any particular project. Some will overestimate when you have very few objects, others will underestimate when you have too many positive objects… and so on.
You seem to have issues with blur around the edges (lower left and lower right) which could impact your analysis as well.
Might also consider Weka pixel classification, as it might be more robust than a threshold, depending on the consistency of your images. Making sure you are taking “fair” images is always the best place to start if you are troubleshooting.