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.