Making algorithms at least 3D, preferably nD

[Juan Nunez-Iglesias]

Hi again Ankit,

I think this is all a bit off-topic anyway. The whole idea of making the
algorithms nD is to apply them to 3D volumetric data, not 2D+t. That they *
sometimes* apply to 2D+t is merely a happy accident.

But scikit-image is part of scikits, an "index of add-on toolkits that
complement SciPy, a library of *scientific* computing routines." (emphasis
mine). One of the goals is (/should be; @stefanv can weigh in) the analysis
of scientific images, many of which are 3D volumetric. And many such
algorithms can be applied as-is whether the data is 2D or 3D. This includes
filters, edge detectors, segmentation methods, convex hulls, and more.


On Mon, Apr 29, 2013 at 1:05 PM, Ankit Agrawal <aaaagrawal at gmail.com> wrote:

>
>
> On Mon, Apr 29, 2013 at 7:49 AM, Juan Nunez-Iglesias <jni.soma at gmail.com>wrote:
>
>> In Marianne's case, there is a 3D volumetric image *in addition to* a
>> time axis.
>>
>> Furthermore, if the time resolution in t is sufficient, many nD
>> algorithms can be used, along t as well (with suitable parameters e.g.
>> sigma for gaussian gradient magnitude). For an example, see:
>>
>> Andres, B., Kroeger, T., Briggman, K. L., Denk, W., Korogod, N., Knott,
>> G., Koethe, U., and Hamprecht, F. A. (2012). Globally optimal
>> closed-surface segmentation for connectomics. ECCV, 778–791.
>>
>> where they use a 3D segmentation method to do tracking in 2D+t video.
>>
>
> @Juan, this was an interesting read. I can feel why the 3D volumetric
> algorithm fits 2D x t (video), because the task involved is segmentation
> based tracking. However, I am still not fully convinced(would like to know
> more such examples if any) and feel that most nD algorithms would work
> differently on 2D x t and 3D. Thanks.
>
> Regards,
> Ankit Agrawal,
> Communication and Signal Processing,
> IIT Bombay.
>
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