Find Feature Clustering

Group (Subgroup)

Statistics (Morphological)

Description

This Filter determines the radial distribution function (RDF), as a histogram, of a given set of Features. Currently, the Features need to be of the same Ensemble (specified by the user), and the resulting RDF is stored as Ensemble data. This Filter also returns the clustering list (the list of all the inter-Feature distances) and the minimum and maximum separation distances. The algorithm proceeds as follows:

  1. Find the Euclidean distance from the current Feature centroid to all other Feature centroids of the same specified phase
  2. Put all caclulated distances in a clustering list
  3. Repeat 1-2 for all Features
  4. Sort the data into the specified number of bins, all equally sized in distance from the minimum distance to the maximum distance between Features. For example, if the user chooses 10 bins, and the minimum distance between Features is 10 units and the maximum distance is 80 units, each bin will be 8 units
  5. Normalize the RDF by the probability of finding the Features if distributed randomly in the given box

Note: Because the algorithm iterates over all the Features, each distance will be double counted. For example, the distance from Feature 1 to Feature 2 will be counted along with the distance from Feature 2 to Feature 1, which will be identical.

Parameters

Name Type Description
Number of Bins for RDF int32_t Number of bins to split the RDF
Phase Index int32_t Ensemble number for which to calculate the RDF and clustering list

Required Geometry

Image

Required Objects

Kind Default Name Type Component Dimensions Description
Feature Attribute Array EquivalentDiameters float (1) Diameter of a sphere with the same volume as the Feature
Feature Attribute Array Phases int32_t (1) Specifies to which Ensemble each Feature belongs
Feature Attribute Array Centroids float (3) X, Y, Z coordinates of Feature center of mass

Created Objects

Kind Default Name Type Component Dimensions Description
Feature Attribute Array ClusteringList float (1) Distance of each Features's centroid to ever other Features's centroid
Ensemble Attribute Array RDF float (Number of Bins) A histogram of the normalized frequency at each bin
Ensemble Attribute Array RDFMaxMinDistances float (2) The max and min distance found between Features

Example Pipelines

  • (09) Image Segmentation.json

Please see the description file distributed with this Plugin

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