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.
I'm uncomfortable leaving this as is. I've mentioned before that we should probably standardise to numpy/scipy's "connectivity" parameter (ranging from 1 to ndim), but even before that (it's an API change and I'm happy to tackle it myself after this is merged), neighbors in 3D should be the appropriate values for that dimension: 6, 18, or 26. (18 is rarely used so happy to leave unimplemented if it would add complexity.)