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.

Thanks again. I somehow thought that the nD algorithms that are going to be implemented in scikit should also generalize for 2D x t and hence my

Hi Juan, I think this is all a bit off-topic anyway. The whole idea of making the previous concern. Dealing just with volumetric data makes the whole picture clear.

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.

Yes, all the functions in scipy.ndimage can be genralized for volumetric 3D.