Hello everyone,

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.