Label3d

This pull request features two things:

1. 2D labelling refactoring. This has resulted in:
   * Higher readability of the code by breaking large functions into smaller ones.
   * More exhaustive documentation of the code.
   * Better performance.

2. Addition of support for 3D arrays.

I have read the papers that were referenced in the code. I think have understood them to let's say 90%, but I have found the implementation of the 2D labelling slightly different than the algorithm described in the paper by Kensheng et al. (there is no decision tree and also only one iteration is performed).
I have therefore implemented the 3D in an analogous manner. Since it has passed my (simple) test cases, I think that it is time to come up with it and get some feedback from you guys.
The scipy connectivity parameter doesn't strike me as being particularly clear, whereas everyone knows what 4- and 8-connectivity implies.  It is a problem, though, that it does not generalize to N-dimensions.  Our algorithm wouldn't consider pixels that aren't neighbors, so perhaps we can simply specify the binary structure as a parameter, with an example on how to change it?  Then we can also include default structures for 4- and 8-connectivity.  Thoughts?