standardDeviationOfProximalNeighborsMap

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the standard deviation value of neighboring labels.

The distance range of the centroids of the neighborhood can be configured. Note: Values of all pixels in a label each must be identical.

Parameters

parametric_map : Image label_map : Image parametric_map_destination : Image min_distance : float, optional default : 0 max_distance : float, optional default: maximum float value

Categories: Measurements, Filter, Graphs

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_standardDeviationOfProximalNeighborsMap(Image parametric_map, Image label_map, Image parametric_map_destination, Number min_distance, Number max_distance);

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 parametric_map = clij2.push(parametric_mapImagePlus);
ClearCLBuffer label_map = clij2.push(label_mapImagePlus);
parametric_map_destination = clij2.create(parametric_map);
float min_distance = 1.0;
float max_distance = 2.0;

// Execute operation on GPU
clij2.standardDeviationOfProximalNeighborsMap(parametric_map, label_map, parametric_map_destination, min_distance, max_distance);

// show result
parametric_map_destinationImagePlus = clij2.pull(parametric_map_destination);
parametric_map_destinationImagePlus.show();

// cleanup memory on GPU
clij2.release(parametric_map);
clij2.release(label_map);
clij2.release(parametric_map_destination);

Matlab

% init CLIJ and GPU
clij2 = init_clatlab();

% get input parameters
parametric_map = clij2.pushMat(parametric_map_matrix);
label_map = clij2.pushMat(label_map_matrix);
parametric_map_destination = clij2.create(parametric_map);
min_distance = 1.0;
max_distance = 2.0;

% Execute operation on GPU
clij2.standardDeviationOfProximalNeighborsMap(parametric_map, label_map, parametric_map_destination, min_distance, max_distance);

% show result
parametric_map_destination = clij2.pullMat(parametric_map_destination)

% cleanup memory on GPU
clij2.release(parametric_map);
clij2.release(label_map);
clij2.release(parametric_map_destination);

Icy JavaScript

// init CLIJ and GPU
importClass(net.haesleinhuepf.clicy.CLICY);
importClass(Packages.icy.main.Icy);

clij2 = CLICY.getInstance();

// get input parameters
parametric_map_sequence = getSequence();
parametric_map = clij2.pushSequence(parametric_map_sequence);
label_map_sequence = getSequence();
label_map = clij2.pushSequence(label_map_sequence);
parametric_map_destination = clij2.create(parametric_map);
min_distance = 1.0;
max_distance = 2.0;

// Execute operation on GPU
clij2.standardDeviationOfProximalNeighborsMap(parametric_map, label_map, parametric_map_destination, min_distance, max_distance);

// show result
parametric_map_destination_sequence = clij2.pullSequence(parametric_map_destination)
Icy.addSequence(parametric_map_destination_sequence);
// cleanup memory on GPU
clij2.release(parametric_map);
clij2.release(label_map);
clij2.release(parametric_map_destination);

clEsperanto Python (experimental)

import pyclesperanto_prototype as cle

cle.standard_deviation_of_proximal_neighbors_map(parametric_map, label_map, parametric_map_destination, min_distance, max_distance)

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