CLIJ2
GPU accelerated image processing for everyone
generateFeatureStack
Generates a feature stack for Trainable Weka Segmentation.
Use this terminology to specifiy which stacks should be generated:
- “original” original slice
- “GaussianBlur=s” Gaussian blurred image with sigma s
- “LaplacianOfGaussian=s” Laplacian of Gaussian blurred image with sigma s
- “SobelOfGaussian=s” Sobel filter applied to Gaussian blurred image with sigma s
- “minimum=r” local minimum with radius r
- “maximum=r” local maximum with radius r
- “mean=r” local mean with radius r
- “entropy=r” local entropy with radius r
- “gradientX” local gradient in X direction
- “gradientY” local gradient in Y direction
Use sigma=0 to apply a filter to the original image. Feature definitions are not case sensitive.
Example: “original gaussianBlur=1 gaussianBlur=5 laplacianOfGaussian=1 laplacianOfGaussian=7 entropy=3”
Categories: Segmentation, Machine Learning
Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clijx-weka_-0.32.0.1.jar.
Usage in ImageJ macro
Ext.CLIJx_generateFeatureStack(Image input, Image feature_stack_destination, String feature_definitions);
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); feature_stack_destination = clijx.create(input);
// Execute operation on GPU clijx.generateFeatureStack(input, feature_stack_destination, feature_definitions);
// show result feature_stack_destinationImagePlus = clijx.pull(feature_stack_destination); feature_stack_destinationImagePlus.show(); // cleanup memory on GPU clijx.release(input); clijx.release(feature_stack_destination);
Matlab
% init CLIJ and GPU clijx = init_clatlabx(); % get input parameters input = clijx.pushMat(input_matrix); feature_stack_destination = clijx.create(input);
% Execute operation on GPU clijx.generateFeatureStack(input, feature_stack_destination, feature_definitions);
% show result feature_stack_destination = clijx.pullMat(feature_stack_destination) % cleanup memory on GPU clijx.release(input); clijx.release(feature_stack_destination);