I’m working to develop some code that is part of an image quality test that can detect whether various rectangular inserts of varying widths in an test image phantom are visible or not. So a low contrast resolution test basically.
I’m trying to figure out what’s the best approach/algorithm for doing that.
Some of the caveats are
- the inset may not be in exactly the same position in each image (however approximately the same)
- the orientation and size will, however, always be constant
- I’m using sckit, and do not have openCV available
Each test image has a set of 5 different inserts of varying thicknesses, and I’d like an end result to say which is the thinnest visible insert in that particular image test.
- use sckit
match_templateas I know each inserts size/orientation to try to match, and a check if it’s a ‘good enough’ match
- simply use numpy to do an averaged profile along the orientation of the inserts, and check if there is a peak with minumum height discernable from the background noise
For this type of problem, what other ideas or approaches might be usable?