Hi Stéfan

If you had a mask for an individual superpixel, and indices into your
array, x, y, z, you can imagine finding the coordinates of all pixels
under that mask with

x[mask], y[mask], z[mask]

The mask you typically recover from a label image, so, e.g., mask =
(labels == 3).

Thanks.

Now, the trickier problem is figuring out where, relative to other
super-pixels, this one is located.  For that, it may be better to
represent the image as a graph, where each node represents a
super-pixel, and edges represent links to other super-pixels (in fact,

I assumed/hoped there is some python based graph library that was numpy friendly.

this is something we should implement in scikit-image to make handling
labels easier).

Would you be interested in collaborating on such a feature?

Sure.  I am still feeling my way, but am willing to contribute what every way I can.

Regards,

Michael.