Hello all,

I'm replying here because the subject is more relevant and the thread is more recent, but I'll quote Emmanuelle from the older thread "Segmentation algorithms".

On Friday, June 15, 2012 4:28:07 PM UTC-4, Emmanuelle Gouillart wrote:
I could, however, write a tutorial on how to deal with the processing of 
3-D images with numpy/skimage/mayavi...

I would be very interested in such a tutorial. :)

Since I'm using skimage for processing mostly 3-D images (from X-ray 
tomography), I'm strongly biased towards 3-D support. But in fact, it's 
only worth implementing an algorithm for 3-D images if it's fast enough 
and does not have too high a memory load, since the data are much 
larger (nobody wants to wait one day for the segmentation of a 
1000*1000*1000 image... whereas one minute for a 1000x1000 image might 
still be decent enough in 2-D). 

Do you have thoughts on 3D image cross-correlation?
My data are images of shape (512, 1536, 21) so the 2D (x, y) part clearly dominates.
Still, there is a this z-component of length 21.  I'm not making use of it so far and I wonder if I should, if it's worth the trouble of finding an implementation and the additional computational cost.
So far, I'm tracking the motion of features on the 2D (512, 1536) images using normalized cross-correlation (function feature.match_template()).
The motion looks mainly planar but, well, I'm not sure how refined I should go about this.

Thank you,