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thresholdOtsu

By Robert Haase based on work by G. Landini and W. Rasband

The automatic thresholder utilizes the Otsu threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

Categories: Segmentation, Binary

Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clij2_-2.2.0.19.jar.

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Usage in ImageJ macro

Ext.CLIJ2_thresholdOtsu(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.thresholdOtsu(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.thresholdOtsu(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.thresholdOtsu(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.threshold_otsu(input, destination)

Example notebooks

basic_image_processing
binary_processing
count_overlap_between_channels
custom_clij_macro_functions
image_types
labelmap_voronoi
mean_of_touching_neighbors
measure_statistics
tables
voronoi
working_with_rois
count_blobs.ipynb
quantitative_neighbor_maps.ipynb
segmentation_2d_membranes.ipynb
voronoi_otsu_labeling.ipynb
parametric_maps.ipynb
threshold_otsu.ipynb

Example scripts

autoThreshold.ijm
basic_image_processing.ijm
benchmarkVoronoi.ijm
binary_processing.ijm
count_overlap_between_channels.ijm
create_object_outlines.ijm
custom_clij_macro_functions.ijm
distanceMap.ijm
distance_map.ijm
division_visualisation.ijm
image_types.ijm
jaccard_matrix.ijm
labelmap_voronoi.ijm
mean_of_touching_neighbors.ijm
measure_statistics.ijm
tables.ijm
voronoi.ijm
working_with_rois.ijm
simplePipeline.m
automaticThreshold.js
automaticThreshold.groovy
automaticThreshold.bsh
count_blobs.py
napari_.py
napari_magicgui.py

License terms

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

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