10.28. Compute Feature Clustering
Group (Subgroup)
Statistics (Morphological)
Description
This Filter computes the radial distribution function (RDF) of a set of Features. A Feature is a contiguous region of like-segmented cells (for example a grain or particle). An RDF is a histogram that describes how the Features are spatially distributed relative to each other: for each distance bin, it counts how many other Feature centroids lie at that separation distance, then normalizes that count against the count expected if the same number of Features were scattered randomly in the same volume. Values above 1.0 at a given distance indicate the Features are more likely to be found at that separation than random (clustering or ordering); values below 1.0 indicate they are less likely (exclusion). Use this Filter to detect and quantify clustering, ordering, or repulsion in a population of Features.
An Ensemble is a group of Features that share a common phase (a distinct material or crystal structure). This Filter operates on a single Ensemble at a time, specified by the Phase Index parameter, and the resulting RDF is stored as Ensemble data.
All distances in this Filter are physical lengths expressed in the same length units as the Image Geometry spacing (for example microns). The algorithm proceeds as follows:
Find the straight-line (Euclidean) distance from the current Feature centroid to every other Feature centroid of the selected phase.
Put all calculated distances in a clustering list.
Repeat steps 1-2 for all Features.
Sort the distances into the specified number of bins, all equally sized in distance from the minimum separation to the maximum separation. For example, if the user chooses 10 bins, the minimum separation is 10 microns, and the maximum separation is 80 microns, each bin spans 7 microns.
Normalize the RDF by the probability of finding the Features at that distance if they were distributed randomly in the bounding box.
The Filter also outputs the clustering list (every inter-Feature distance) and the minimum and maximum separation distances (both in physical length units).
Note: Because the algorithm iterates over all Features, each distance is double-counted. For example, the distance from Feature 1 to Feature 2 is counted along with the identical distance from Feature 2 to Feature 1.
Required Input Sources
Centroids – the X, Y, Z coordinates of each Feature’s center of mass, produced by Compute Feature Centroids.
Phases – the phase (Ensemble) that each Feature belongs to, produced by Compute Feature Phases.
Input Parameter(s)
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Selected Image Geometry |
Geometry Selection |
Image |
The target geometry |
Number of Bins for RDF |
Scalar Value |
Int32 |
Number of bins to split the RDF |
Phase Index |
Scalar Value |
Int32 |
Ensemble number for which to calculate the RDF and clustering list |
Remove Biased Features |
Bool |
Remove the biased features |
Random Number Seed Parameters
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Set Random Seed |
Bool |
When checked, allows the user to set the seed value used to randomly generate the points in the RDF |
|
Seed Value |
Scalar Value |
UInt64 |
The seed value used to randomly generate the points in the RDF |
Stored Seed Value Array Name |
DataObjectName |
Name of array holding the seed value |
Input Feature Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Phases |
Array Selection |
Allowed Types: int32 Comp. Shape: 1 |
Specifies to which Ensemble each Feature belongs |
Centroids |
Array Selection |
Allowed Types: float32 Comp. Shape: 3 |
X, Y, Z coordinates of Feature center of mass |
Biased Features |
Array Selection |
Allowed Types: uint8, boolean Comp. Shape: 1 |
Specifies which features are biased and therefore should be removed if the Remove Biased Features option is on; True values removed |
Input Ensemble Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Cell Ensemble Attribute Matrix |
AttributeMatrixSelection |
The path to the cell ensemble attribute matrix where the RDF and RDF min and max distance arrays will be stored |
Output Feature Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Clustering List |
DataObjectName |
Distance of each Feature’s centroid to every other Feature’s centroid |
Output Ensemble Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Radial Distribution Function |
DataObjectName |
A histogram of the normalized frequency at each bin |
|
Max and Min Separation Distances |
DataObjectName |
The max and min distance found between Features |
Example Pipelines
PorosityAnalysis
License & Copyright
Please see the description file distributed with this Plugin
DREAM3D-NX Help
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