I am trying to find the best way to do a thresholding for my image analysis. I am going to use this site [https://imagej.net/Auto_Local_Threshold] as a reference.
I have a computed tomography image with a bit of noise. The global thresholding is more or less good, but I think it is possible to do it better with the local thresholding.
I have carefully read the explanations on every method, but not everyhing is clear. Even when it’s clear, I always have to do the old “try and error”.
- My instint says Otsu is the answer, but I don’t understand at all how this one works. Ive seen this method is good for bipolar and noisy images. But when I use it, it’s just a mess. How does it work?
- The mean/median method takes the mean/median of the local greyscale distribution and can be adjusted with a constant C1. Niblack is similar to the mean method; it has a correction value (with the standard deviation). Sauvola is based on Niblack and has a parameter “r” empirically set. Midgrey is similar, taking instead of the mean/median the (max + min)/2 of the local values. The contrast method compares the pixel with the max or min and sets it to the one closest.
All of this methods give me poor results. I have used them succesfully to correct an image of a poorly iluminated image (for practice). The noise is probably messing with the results. Is there a way to surpass it?
- I have not understand the Bernsen method. Can you help me? I understand the words, but not the process.
Phansalkar is a modification of Sauvola that I don’t really understand, but my instinct tells me this is not the one. But what does my instinct know…
Thank you all so much.