reference_averageNeighborDistanceMap.md

averageNeighborDistanceMap

Takes a label map, determines which labels touch and replaces every label with the average distance to their neighboring labels.

To determine the distances, the centroid of the labels is determined internally.

Categories: Graphs, Labels, Measurements

Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clij2_-2.5.0.1.jar.

Usage in ImageJ macro

Ext.CLIJ2_averageNeighborDistanceMap(Image input, Image destination);

Usage in object oriented programming languages

Java

// init CLIJ and GPU
import net.haesleinhuepf.clij2.CLIJ2;
import net.haesleinhuepf.clij.clearcl.ClearCLBuffer;
CLIJ2 clij2 = CLIJ2.getInstance();
// get input parameters
ClearCLBuffer input = clij2.push(inputImagePlus);
destination = clij2.create(input);

// Execute operation on GPU
clij2.averageNeighborDistanceMap(input, destination);

// show result
destinationImagePlus = clij2.pull(destination);
destinationImagePlus.show();

// cleanup memory on GPU
clij2.release(input);
clij2.release(destination);

Matlab

% init CLIJ and GPU
clij2 = init_clatlab();
% get input parameters
input = clij2.pushMat(input_matrix);
destination = clij2.create(input);

% Execute operation on GPU
clij2.averageNeighborDistanceMap(input, destination);

% show result
destination = clij2.pullMat(destination)

% cleanup memory on GPU
clij2.release(input);
clij2.release(destination);

Icy JavaScript

// init CLIJ and GPU
importClass(net.haesleinhuepf.clicy.CLICY);
importClass(Packages.icy.main.Icy);
clij2 = CLICY.getInstance();
// get input parameters
input_sequence = getSequence();
input = clij2.pushSequence(input_sequence);
destination = clij2.create(input);

// Execute operation on GPU
clij2.averageNeighborDistanceMap(input, destination);

// show result
destination_sequence = clij2.pullSequence(destination)
Icy.addSequence(destination_sequence);
// cleanup memory on GPU
clij2.release(input);
clij2.release(destination);

clEsperanto Python (experimental)

import pyclesperanto_prototype as cle
cle.average_neighbor_distance_map(input, destination)

Example notebooks

quantitative_neighbor_maps.ipynb

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