but with “Power Spectrum” alone you are way off the track…
Actually, I didn’t suggest to “google ‘Power spectrum’” which actually will not help much with the problem in question!
In short, the first cited report compares three global approaches to orientation analysis (quality of the results & computational effort) when implemented digitally. Theoretically the three approaches lead to the same result which is shown mathematically in the second cited publication.
For your kind of images, the three digital implementations lead to results that are nearly indistinguishable, i.e. one may use the easiest approach which is the one based on the Power Spectrum. However, the most efficient approach, i.e. the one for which the relation of quality and relative computational effort is maximized, is the one based on the Autocorrelation function.
As mentioned before, the implementation of the three approaches is not trivial and surely not a project for a beginner. The provided ImageJ-plugin “Slice Integrals” may help but the remaining tasks, that are detailed in the report, are still quite involved. Actually, it took me some month to code, test and refine the three implementations that I cannot provide for free. However, I still offer to analyze a decent amount of images for you.
I mean that to find that which sample formed in one direction.
If you are looking for the sample having the greatest amount of aligned fibers (fibers having approximately the same orientation), then you must look for orientation results showing a single, high and slim (pronounced) maximum. In this respect, example Image-2 is better when compared to Image-1.
The FORUM told me that I’m not allowed to post any further replies, and that I’m limited to post a maximum of 5 images. What a mess!
Consequently, I propose to change to the ImageJ-list where we could continue our work.
For the list please see:
Please be so kind and post a short message there and I shall send you the results.