I’m working on an arbitrary image averaging script, where I’d like to assume input images are not guaranteed to overlap (completely, or even partially). As such, I need to construct a “common” space which contains all the input images. Finding the voxel dimensions is easy per-image, but before I go about writing something to locate all the “corners” of the data in space manually, is there a more clever solution in ITK I might be able to use?
I don’t know of anything clever, but I did write the code for computing a world space bounding box of a volume.
Here’s my code to ‘regularize’ a volume. It makes a volume with cubic voxels and an orientation of the identity matrix.
Thanks for the code, I can definitely steal some of that.
Since you’re isotropizing images, you might find this useful, Resampling to Isotropic: Signal Processing Theory
Interesting thread. Yeah, I don’t do any sort of filtering, so it can result it artifacts.
Thanks very much again @dchen, I stole your code mostly wholesale to sort out the bounding box, now I can combine arbitrary-space images into a common “mega” image covering the full bounding box, as I wanted.
You’re welcome. I’m glad you found my code useful.