Greetings good people!
I am trying to segment pictures with very tiny, circular features. The problem is pretty much the same as in my older thread (this one, meaning that the pictures are noisy and I can’t afford to use any kind of gaussian or median filtering because they would “mix” very close circles together. Some circles have a diameter of a few pixels, even.
I’ll post here an example of the pictures I’m trying to work with (for reference, they are 800x800 px wide):
However, since last time I managed to make some progress on the detection side, meaning that now the problem is just ("“just”") segmenting the pictures properly.
I have been having quite some success with Ilastik and its pixel-classification mode, but I still need to fine-tune the process, as the results are still not entirely usable, albeit very good.
To be more clear, this is the result I get after training on five similar, 2D pictures on the example above:
As you may see, Ilastik struggles to separate circles that are very close one to each other.
My question is: what can I do to optimize the process? For very small and clustered circles are there some training features that I should turn off or on? Should I limit the features to smaller sigmas? Is there anything in particular that I should take care of during the training?
Thank you in advance!