Making algorithms at least 3D, preferably nD

[Josh Warner]

Volumetric 3D is rare to some, I'm sure ;) but there's a large audience 
available for medical image processing that needs 3D. Nearly everything I 
do involves 3D volumetric arrays. Confocal microscopy can generate true 3D 
multispectral data in the biological sciences.

The field of neurology may be poised to have a huge need for true 3D 
(multispectral) processing in the near future as well! If you haven't seen 
this development out of Stanford, it's worth checking 
out: http://med.stanford.edu/ism/2013/april/clarity.html

So, wherever possible, I'll be looking to add 3D implementations. I feel 
this is a "build it and they will come" sort of situation.

On Monday, April 29, 2013 12:16:29 PM UTC-5, Johannes Schönberger wrote:
>
> > 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.

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