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@gmail.com> wrote:

On Mon, Apr 29, 2013 at 7:49 AM, Juan Nunez-Iglesias <jni.soma@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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