wekaRegionalLabelClassifier

Applies a pre-trained CLIJx-Weka model to an image and a corresponding label map to classify labeled objects.

Given radii allow to configure if values of proximal neighbors, other labels with centroids closer than given radius, should be taken into account, e.g. for determining the regional maximum.

Make sure that the handed over feature list and radii are the same used while training the model.

Categories: Labels, Segmentation

Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clijx_-0.32.0.1.jar.

Usage in ImageJ macro

Ext.CLIJx_wekaRegionalLabelClassifier(Image input, Image label_map, Image destination, String features, String modelfilename, Number radius_of_maximum, Number radius_of_minimum, Number radius_of_mean, Number radius_of_standard_deviation);

Usage in object oriented programming languages

Java

// init CLIJ and GPU
import net.haesleinhuepf.clijx.CLIJx;
import net.haesleinhuepf.clij.clearcl.ClearCLBuffer;
CLIJx clijx = CLIJx.getInstance();

// get input parameters
ClearCLBuffer input = clijx.push(inputImagePlus);
ClearCLBuffer label_map = clijx.push(label_mapImagePlus);
destination = clijx.create(input);
int radius_of_maximum = 10;
int radius_of_minimum = 20;
int radius_of_mean = 30;
int radius_of_standard_deviation = 40;

// Execute operation on GPU
clijx.wekaRegionalLabelClassifier(input, label_map, destination, features, modelfilename, radius_of_maximum, radius_of_minimum, radius_of_mean, radius_of_standard_deviation);

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

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

Matlab

% init CLIJ and GPU
clijx = init_clatlabx();

% get input parameters
input = clijx.pushMat(input_matrix);
label_map = clijx.pushMat(label_map_matrix);
destination = clijx.create(input);
radius_of_maximum = 10;
radius_of_minimum = 20;
radius_of_mean = 30;
radius_of_standard_deviation = 40;

% Execute operation on GPU
clijx.wekaRegionalLabelClassifier(input, label_map, destination, features, modelfilename, radius_of_maximum, radius_of_minimum, radius_of_mean, radius_of_standard_deviation);

% show result
destination = clijx.pullMat(destination)

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

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