Making algorithms at least 3D, preferably nD

[Johannes Schönberger]

Josh, thanks for the link. I'm not in this field but it looks quite interesting.

Johannes Schönberger

Am 30.04.2013 um 03:47 schrieb "Josh Warner" <silvertrumpet999 at gmail.com<mailto:silvertrumpet999 at gmail.com>>:

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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