Re: Making algorithms at least 3D, preferably nD

Volumetric image processing is definitely within scope of the scikit. The reason that most of the implementations up to this point were 2-D is simply a lack of time and hands. Luckily, that seems to be changing, so we may very well have the luxury of tackling this problem head-on.

I also think that the majority of use cases is based on 2-D data (plus channel data) and volumentric data is a specific and rare use case. I'm also dealing with lots of nD data (3-D, 4-D,…), nevertheless they are mostly still 2-D data. E.g. - SAR stacks (NxMxD) - Covariance matrix images (NxMxDxD) - Hyperspectral remote sensing images (NxMxD) - Tomographic SAR (NxMxJxD) - etc. where D is often > 200.