snow_partitioning

snow_partitioning(im, dt=None, r_max=4, sigma=0.4)[source]

Partition the void space into pore regions using a marker-based watershed algorithm, with specially filtered peaks as markers.

Parameters
  • im (array_like) – A boolean image of the domain, with True indicating the pore space and False elsewhere.

  • dt (array_like, optional) – The distance transform of the pore space. This is done automatically if not provided, but if the distance transform has already been computed then supplying it can save some time.

  • r_max (int) – The radius of the spherical structuring element to use in the Maximum filter stage that is used to find peaks. The default is 4.

  • sigma (float) – The standard deviation of the Gaussian filter used in step 1. The default is 0.4. If 0 is given then the filter is not applied, which is useful if a distance transform is supplied as the im argument that has already been processed.

Returns

  • A **named tuple* containing all of the images used during the*

  • process. They can be accessed as attriutes with the following names

    im

    The binary image of the void space

    dt

    The distance transform of the image

    peaks

    The peaks of the distance transform after applying the steps of the SNOW algorithm

    regions

    The void space partitioned into pores using a marker based watershed with the peaks found by the SNOW algorithm

Notes

The SNOW network extraction algorithm (Sub-Network of an Over-segmented Watershed) was designed to handle to perculiarities of high porosity materials, but it applies well to other materials as well.

References

[1] Gostick, J. “A versatile and efficient network extraction algorithm using marker-based watershed segmenation”. Physical Review E. (2017)