GPU accelerated image processing for everyone
By Robert Haase based on work by G. Landini and W. Rasband
The automatic thresholder utilizes the threshold methods from ImageJ on a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.
Enter one of these methods in the method text field: [Default, Huang, Intermodes, IsoData, IJ_IsoData, Li, MaxEntropy, Mean, MinError, Minimum, Moments, Otsu, Percentile, RenyiEntropy, Shanbhag, Triangle, Yen]
Categories: Segmentation, Binary
Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clij2_-2.5.0.1.jar.
Ext.CLIJ2_automaticThreshold(Image input, Image destination, String method);
// 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.automaticThreshold(input, destination, method);
// show result destinationImagePlus = clij2.pull(destination); destinationImagePlus.show(); // cleanup memory on GPU clij2.release(input); clij2.release(destination);
% init CLIJ and GPU clij2 = init_clatlab(); % get input parameters input = clij2.pushMat(input_matrix); destination = clij2.create(input);
% Execute operation on GPU clij2.automaticThreshold(input, destination, method);
% show result destination = clij2.pullMat(destination) % cleanup memory on GPU clij2.release(input); clij2.release(destination);
// 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.automaticThreshold(input, destination, method);
// show result destination_sequence = clij2.pullSequence(destination) Icy.addSequence(destination_sequence); // cleanup memory on GPU clij2.release(input); clij2.release(destination);
compare_workflows
labeling
measure_overlap
boundingBoxes.ijm
center_of_mass.ijm
compare_workflows.ijm
excludeLabelsOnEdges.ijm
excludeLabelsWithinRange.ijm
intensity_per_label.ijm
labeling.ijm
measure_overlap.ijm
outline.ijm
particle_analysis.ijm
statistics.ijm
workflow.ijm
automaticThreshold.py
segmentation.py
statistics.py
The code for the automatic thresholding methods originates from https://github.com/imagej/imagej1/blob/master/ij/process/AutoThresholder.java
Detailed documentation on the implemented methods can be found online: https://imagej.net/Auto_Threshold