Livewire segmentation in scikit-image

[Stéfan van der Walt]

Hi Pavlo

On 22 February 2016 at 13:43, Pavlo Dyban <pdyban at gmail.com> wrote:
> 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.

Thanks for your interest in contributing to scikit-image!

> 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.

I've only heard of livewire in terms of interactive work.  Is it
commonly used as a stand-alone technique?

> 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!

It may well be--I will rely on our segmentation specialists Emmanuelle
and Juan to take a look.  Do you perhaps have a citation for this
method?

We're happy to help you get pull requests off the ground.  In the mean
time, take a look at:

http://scikit-image.org/docs/stable/contribute.html

Thanks!
Stéfan