I recently started a PhD which involves a lot of electron microscopy.
Therefore a proper knowledge of SEM image analysis is necessary.
Currently I’m involved in a project that focusses on crack initiation of stress corrosion cracks (SCC) in stainless steels.
Getting to the point, I wish to analyse SEM images, at 500x M and 10 WD, that have a lot of cracks on the surface of the material.
Interesting things that I wish to gain from these pictures are e.g. crack density (#cracks vs. distance) and crack lengths.
Obviously counting these cracks manually is ill-favored since this is a too biased method which takes up too much time.
My current understanding of Fiji is still limited.
In order to handle this problem I feel that I need to segmentate these cracks from the material itself.
However, this material has a duplex structure, it contains two allotropes of iron namely austenite (which is much more favourable to cracking) and ferrite (less favourable).
The austenite has the tendency to form small oxides which increases the crack susceptibility.
If it is possible to segmentate this cracks, it should be possible to create a mask (binary image) with ROIs that are measurable.
Before the actual segmentating phase, the pictures should need some preprocessing as well but I’m not sure which could prove useful.
If counting the cracks in this manner proves to be too difficult, It could be helpful to approach the problem in a different manner as well.
One should be able to segmentate the austenite from the ferrite and determine, in percentages, howmuch of each is present in the picture.
In this way it’s nice to prove that certain areas which contain more austenite show more cracks as well.
You can find some raw image files in the annex of this topic.
Hopefully one can point me in the right direction with a to-do list in fiji.
Thanks in advance.