voronoiOtsuLabeling

Labeles objects directly from grey-value images.

The two sigma parameters allow tuning the segmentation result. The first sigma controls how close detected cells can be (spot_sigma) and the second controls how precise segmented objects are outlined (outline_sigma).Under the hood, this filter applies two Gaussian blurs, spot detection, Otsu-thresholding and Voronoi-labeling. The thresholded binary image is flooded using the Voronoi approach starting from the found local maxima. Noise-removal sigma for spot detection and thresholding can be configured separately.

Category: Labels

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_voronoiOtsuLabeling(Image input, Image destination, Number spot_sigma, Number outline_sigma);

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);
float spot_sigma = 1.0;
float outline_sigma = 2.0;

// Execute operation on GPU
clij2.voronoiOtsuLabeling(input, destination, spot_sigma, outline_sigma);

// 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);
spot_sigma = 1.0;
outline_sigma = 2.0;

% Execute operation on GPU
clij2.voronoiOtsuLabeling(input, destination, spot_sigma, outline_sigma);

% 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);
spot_sigma = 1.0;
outline_sigma = 2.0;

// Execute operation on GPU
clij2.voronoiOtsuLabeling(input, destination, spot_sigma, outline_sigma);

// 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.voronoi_otsu_labeling(input, destination, spot_sigma, outline_sigma)

Example notebooks

voronoi_otsu_labeling
Segmentation_3D.ipynb
voronoi_otsu_labeling.ipynb

Example scripts

voronoi_otsu_labeling.ijm
napari_performance_demo.py

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