Hey Guys,<div><br></div><div>I mentioned this briefly at SciPy, but I would like to reiterate: a lot of data is 3D images these days, and more and more data is being generated that is multi-channel, 3D+t. Therefore, it would be awesome if scikit-image started making more of an effort to support these. In the best case, the dimension of the underlying array can be abstracted away — see <a href="https://github.com/jni/ray/blob/develop/ray/morpho.py#L328" target="_blank">here</a> for example, the functions juicy_center (which extracts the centre of an array, along all dimensions), surfaces (grabs the &quot;border&quot; arrays along each dimension), hollowed (zeroes-out the centre), and more. Otherwise, writing a 3D function that gracefully degrades to 2D when one of the dimensions is 1 is also possible.</div>


<div><br></div><div>In general, the amount of additional effort to make code 3-, 4- or n- dimensional is relatively low when you write the algorithm initially, relative to refactoring a whole bunch of functions later. I&#39;ll try to fiddle with whichever code I need, but in the meantime, what do you think about adding a paragraph or a sentence about this issue in the scikit-image <a href="http://scikit-image.org/docs/dev/contribute.html" target="_blank">contribute</a> section, so that people at least have this in mind when they are thinking of writing something new?</div>


<div><br></div><div>Thanks,</div><div><br></div><div>Juan.</div>