voronoiOtsuLabeling

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 clijx_-0.30.1.22.jar.

Usage in ImageJ macro

Ext.CLIJx_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.clijx.CLIJx;
import net.haesleinhuepf.clij.clearcl.ClearCLBuffer;
CLIJx clijx = CLIJx.getInstance();

// get input parameters
ClearCLBuffer input = clijx.push(inputImagePlus);
destination = clijx.create(input);
float spot_sigma = 1.0;
float outline_sigma = 2.0;

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

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

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

Matlab

% init CLIJ and GPU
clijx = init_clatlabx();

% get input parameters
input = clijx.pushMat(input_matrix);
destination = clijx.create(input);
spot_sigma = 1.0;
outline_sigma = 2.0;

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

% show result
destination = clijx.pullMat(destination)

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

clEsperanto Python (experimental)

import pyclesperanto_prototype as cle

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

Example notebooks

Segmentation_3D.ipynb
voronoi_otsu_labeling.ipynb

Example scripts

napari_performance_demo.py

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