On Mon, Apr 29, 2013 at 11:06 AM, Ankit Agrawal <aaaagrawal@gmail.com>wrote:

@Josh and Juan, Thanks for your explanation.

I may be wrong but I feel that there would be a limited number of algorithms that are nD aware but will scale down nicely if provided with a 2D image. For instance, if we have 3D data of the type (m x n x p), many functions and algorithms involving spatial components for eg: gradient based edge detectors won't be applicable since our 3rd dimension represents a series of images, we can't have something like a gradient in that dimension.

@Ankit, actually, edge detectors generalise quite nicely to nD, e.g.: http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.filters.ga... Many other examples of nD algorithms: http://docs.scipy.org/doc/scipy/reference/ndimage.html Instead, if our data is 3D volumetric image, a great percentage of

Computer Vision algorithms won't be of any use since they rely on making sense 3D world from 2D data. I would love to hear any comments on this point. Thanks.

Photographs are rarely 3D, but various kinds of microscopy produce truly 3D images, not a sequence of unrelated images. If you give specific algorithms, we might be better able to point out how to generalise to 3D, but the gist is that most algorithms *do* generalise. It is the implementations that are 2D, not the algorithms.