Livewire segmentation in scikit-image

[Pavlo Dyban]

Hello! I am a great fan of scikit-learn and have used it in a number of 
various projects so far. This time, I want to contribute to the project. I 
have implemented Livewire segmentation algorithm for image segmentation and 
would like to bring it into the main package. My code is hosted on github, 
it is documented with sphinx and tested. 

Livewire segmentation technique deduces object boundaries in the image by 
converting the image to a weighted graph where edges' weights are computed 
from the gradient image. The shortest path algorithm minimises the total 
cost function, thus avoiding steep gradients (i.e. object boundaries in the 
original image). An example of how the algorithm works you will find in my 
repository: https://github.com/pdyban/livewire.

The API is straightforward:

from livewire import LiveWireSegmentationalgorithm = LiveWireSegmentation(image)path = algorithm.compute_shortest_path((0, 0), (10, 25))

Do you think this algorithm would be needed inside scikit-image? If yes, 
would that belong inside segmentation module? Could someone assist me in my 
first open source contribution? It would be great if I could contribute to 
your project! Thanks!
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