snow_partitioning_n#

snow_partitioning_n(im, r_max=4, sigma=0.4, peaks=None)[source]#

This function partitions an imaging oontain an arbitrary number of phases into regions using a marker-based watershed segmentation.

Parameters:

im (ndarray) – Image of porous material where each phase is represented by unique integer starting from 1 (0’s are ignored).
r_max (scalar) – 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 (scalar) – The standard deviation of the Gaussian filter used. The default is 0.4. If 0 is given then the filter is not applied.
peaks (ndarray, optional) – Optionally, it is possible to supply an array containing peaks, which are used as markers in the watershed segmentation. Must be a boolean array with True indicating peaks; scipy.ndimage.label with cubic connectivity is used to label them. If this argument is provided then r_max and sigma are ignored, since these are specfically used in the peak finding process.

Returns:

results – A custom object with the following data as attributes:

’im’
The original image of the porous material
’dt’
The combined distance transform in alll phases of the image
’phase_max_label’
The list of max label of each phase in order to distinguish between each other
’regions’
The partitioned regions of n phases using a marker based watershed with the peaks found by the SNOW algorithm

Return type:

Results object

References

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

[2] Khan, ZA et al. “Dual network extraction algorithm to investigate multiple transport processes in porous materials: Image-based modeling of pore and grain-scale processes”. Computers in Chemical Engineering. (2019)