Index (Trainable Segmentation 4.0.0 API)

A B C D E F G H I K L M N O P R S T U V W

A

adaptedRandIndex3D(ImagePlus, ImagePlus) - Static method in class trainableSegmentation.metrics.RandError: Calculate the Rand index between to 3D clusters, as described by William M.
adaptedRandIndexFScore3D(ImagePlus, ImagePlus) - Static method in class trainableSegmentation.metrics.RandError: Calculate the F-score of the Rand index between to 3D clusters, as described by William M.
adaptedRandIndexStats3D(ImagePlus, ImagePlus) - Static method in class trainableSegmentation.metrics.RandError: Calculate the Rand index between to 3D clusters, as described by William M.
adaptedRandIndexStats3DN2(ImagePlus, ImagePlus) - Static method in class trainableSegmentation.metrics.RandError: Calculate the Rand index stats between to 3D clusters, as described by William M.
ADD_TRACE - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to add the current trace to a class
addAnisotropicDiffusion(ImagePlus, int, int, int, float, float, float) - Method in class trainableSegmentation.FeatureStack: Add anisotropic diffusion filtering images to current stack
addBilateralFilter(ImagePlus, double, double) - Method in class trainableSegmentation.FeatureStack: Add bilateral filter image to current stack
addBinaryData(ImagePlus, FeatureStack, String) - Method in class trainableSegmentation.WekaSegmentation: Add instances to a specific class from a label (binary) image.
addBinaryData(ImagePlus, FeatureStack, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from a label (binary) image.
addBinaryData(List<Point3f>[], FeatureStackArray, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from lists of coordinates.
addBinaryData(ImagePlus, int, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add label image as binary data
addBinaryData(ImagePlus, ImagePlus, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add binary training data from input and label images.
addBinaryData(ImagePlus, ImagePlus, ImagePlus, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add binary training data from input and label images.
addCenterLinesBinaryData(ImagePlus, int, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add center lines of label image as binary data
addClass() - Method in class trainableSegmentation.WekaSegmentation: Add new segmentation class.
addDefaultFeatures() - Method in class trainableSegmentation.FeatureStack: Add the default features to the feature stack
addDerivatives(double, int, int) - Method in class trainableSegmentation.FeatureStack: Add derivatives features to current stack
addDoG(float, float) - Method in class trainableSegmentation.FeatureStack: Add difference of Gaussians to feature stack (single thread version)
addEntropy(int, int) - Method in class trainableSegmentation.FeatureStack: Add entropy filter to current stack
addErodedBinaryData(ImagePlus, int, String, String) - Method in class trainableSegmentation.WekaSegmentation: Add eroded version of label image as binary data
addExample(int, Roi, int) - Method in class trainableSegmentation.WekaSegmentation: Adds a ROI to the list of examples for a certain class and slice.
addFeaturesMT(ImagePlus) - Method in class trainableSegmentation.FeatureStack: Add features based on a list of filters in a multi-thread fashion
addGabor(ImagePlus, double, double, double, double, int) - Method in class trainableSegmentation.FeatureStack: Add Gabor features to current stack
addGaussianBlur(float) - Method in class trainableSegmentation.FeatureStack: Add Gaussian blur slice to current stack
addGradient(float) - Method in class trainableSegmentation.FeatureStack: Add Sobel filter version of the original image
addHessian(float) - Method in class trainableSegmentation.FeatureStack: Add Hessian features from original image (single thread version).
addHSB() - Method in class trainableSegmentation.FeatureStack: Add HSB features
addKuwaharaFeatures(ImagePlus, int, int, int) - Method in class trainableSegmentation.FeatureStack: Add Kuwahara filter features to the current stack
addLabeledData(ImagePlus, FeatureStack) - Method in class trainableSegmentation.WekaSegmentation: Add instances from a labeled image.
addLabeledData(ImageProcessor, FeatureStack, String[], int) - Method in class trainableSegmentation.WekaSegmentation: Add instances reading the pixel classes from a label image.
addLabeledData(ImageProcessor, FeatureStack, int[], int) - Method in class trainableSegmentation.WekaSegmentation: Add instances reading the pixel classes from a label image.
addLabeledData(ImagePlus, ImagePlus) - Method in class trainableSegmentation.WekaSegmentation: Add labeled training data from input and label images.
addLabeledData(ImagePlus, ImagePlus, String[], int) - Method in class trainableSegmentation.WekaSegmentation: Add training data from input and labeled images.
addLabeledData(ImagePlus, ImagePlus, int[], int) - Method in class trainableSegmentation.WekaSegmentation: Add training data from input and labeled images.
addLaplacian(double) - Method in class trainableSegmentation.FeatureStack: Add Laplacian features to current stack
addLipschitzFilter(ImagePlus, boolean, boolean, double) - Method in class trainableSegmentation.FeatureStack: Add Lipschitz filter image to current stack
addMax(float) - Method in class trainableSegmentation.FeatureStack: Add maximum filter to current feature stack.
addMean(float) - Method in class trainableSegmentation.FeatureStack: Add mean filter to current stack
addMedian(float) - Method in class trainableSegmentation.FeatureStack: Add median filter to the current feature stack.
addMembraneFeatures(int, int) - Method in class trainableSegmentation.FeatureStack: Add membrane features to the stack (single thread version)
addMin(float) - Method in class trainableSegmentation.FeatureStack: Add minimum filter to current feature stack.
addNeighbors(int, int) - Method in class trainableSegmentation.FeatureStack: Add 8 neighbors of the original image as features
addRandomBalancedBinaryData(ImageProcessor, FeatureStack, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from a label (binary) image in a random and balanced way.
addRandomBalancedBinaryData(ImageProcessor, ImageProcessor, FeatureStack, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from a label (binary) image in a random and balanced way (with repetition).
addRandomBalancedBinaryData(List<Point3f>[], FeatureStackArray, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from lists of coordinates in a random and balanced way.
addRandomBalancedBinaryData(List<Point3f>[], FeatureStackArray, ImagePlus, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from lists of coordinates in a random and balanced way.
addRandomBalancedBinaryData(ImageProcessor, ImageProcessor, ImageProcessor, FeatureStack, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances to two classes from a label (binary) image in a random and balanced way (with repetition).
addRandomBalancedBinaryData(ImagePlus, ImagePlus, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add binary training data from input and label images in a random and balanced way (same number of samples per class).
addRandomBalancedBinaryData(ImagePlus, ImagePlus, String, String, int, ImagePlus) - Method in class trainableSegmentation.WekaSegmentation: Add binary training data from input and label images in a random and balanced way (same number of samples per class).
addRandomBalancedBinaryData(ImagePlus, ImagePlus, String, String, int, ImagePlus, ImagePlus) - Method in class trainableSegmentation.WekaSegmentation: Add binary training data from input and label images in a random and balanced way (same number of samples per class).
addRandomBalancedBinaryData(ImagePlus, ImagePlus, ImagePlus, String, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add binary training data from input and label images in a random and balanced way (same number of samples per class).
addRandomBalancedLabeledData(ImageProcessor, FeatureStack, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances from a labeled image in a random and balanced way.
addRandomBalancedLabeledData(ImageProcessor, int[], FeatureStack, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances from a labeled image in a random and balanced way.
addRandomBalancedLabeledData(ImageProcessor, FeatureStack, String[], int) - Method in class trainableSegmentation.WekaSegmentation: Add instances reading the pixel classes from a label image in a random and balanced way.
addRandomBalancedLabeledData(ImagePlus, ImagePlus, int) - Method in class trainableSegmentation.WekaSegmentation: Add training data from input and labeled images in a random and balanced way (same number of samples per class).
addRandomBalancedLabeledData(ImagePlus, ImagePlus, int[], int) - Method in class trainableSegmentation.WekaSegmentation: Add training data from input and labeled images in a random and balanced way (same number of samples per class).
addRandomBalancedLabeledData(ImagePlus, ImagePlus, String[], int) - Method in class trainableSegmentation.WekaSegmentation: Add training data from input and labeled images in a random and balanced way (same number of samples per class).
addRandomData(ImagePlus, FeatureStack, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add instances defined by a label (binary) image in a random way.
addRandomData(ImagePlus, ImagePlus, String, int) - Method in class trainableSegmentation.WekaSegmentation: Add training data from input and a binary label image in a random way.
addStructure(double, double) - Method in class trainableSegmentation.FeatureStack: Add structure tensor features to current stack It computes, for all pixels in the input image, the eigenvalues of the so-called structure tensor.
addTrace(String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Add the current ROI to a specific class and slice.
addVariance(float) - Method in class trainableSegmentation.FeatureStack: Add variance-filtered image to the stack (single thread version)
AdjustedRandError - Class in trainableSegmentation.metrics: This class implements the adjusted Rand error, defined as the 1 - adjusted Rand index.
AdjustedRandError(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.AdjustedRandError: Initialize adjusted Rand error metric.
adjustedRandError(ImageProcessor, ImageProcessor, double) - Static method in class trainableSegmentation.metrics.AdjustedRandError: Calculate the adjusted Rand error between some 2D original labels and the corresponding proposed labels.
adjustedRandIndex(ShortProcessor, ShortProcessor) - Static method in class trainableSegmentation.metrics.AdjustedRandError: Calculate the adjusted Rand index between to clusters, as described by Lawrence Hubert and Phipps Arabie \cite{Rand71}.
adjustSegmentationStateToData(Instances) - Method in class trainableSegmentation.WekaSegmentation: Adjust current segmentation state (attributes and classes) to loaded data
ai - package ai
allSame - Variable in class ai.SplitFunction
ANISOTROPIC_DIFFUSION - Static variable in class trainableSegmentation.FeatureStack: Anisotropic diffusion filter flag index
apply() - Method in class trainableSegmentation.utils.WatershedTransform2D: Apply fast watersheds using flooding simulations, as described by Soille, Pierre, and Luc M.
APPLY_CLASSIFIER - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to apply the current classifier to an image or stack
applyClassifier(Instances, int, int, int) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Apply current classifier to set of instances
applyClassifier(String, String, String, String, String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Apply current classifier to specific image (2D or stack)
applyClassifier(ImagePlus) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a given image.
applyClassifier(ImagePlus, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a given image.
applyClassifier(ImagePlus, ImagePlus, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a given image with precomputed features.
applyClassifier(ImagePlus, FeatureStackArray, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a given image with precomputed features.
applyClassifier(boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to current image.
applyClassifier(int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to current image.
applyClassifier(ImagePlus, int[], int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply the current classifier to an image subdividing the image in tiles for memory saving.
applyClassifier(ImagePlus, int[], int[], int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a user-defined ROI of a given image.
applyClassifier(Instances, int, int, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to set of instances
applyClassifier(FeatureStackArray, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a set of feature vectors (given in a feature stack array).
applyClassifierMT(ImagePlus, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Apply current classifier to a given image in a complete concurrent way.
applyClassifierToTestData() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Apply classifier to test data
applyClassifierToTestData() - Method in class trainableSegmentation.Weka_Segmentation: Apply classifier to test data.
applyClassifierToTestImage(ImagePlus, int) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Apply current classifier to image
applyEntropy(ImageProcessor, int, int) - Method in class trainableSegmentation.filters.Entropy_Filter: Apply entropy filter to an image
applyFilter(ImageProcessor, int, int, int) - Method in class trainableSegmentation.filters.Kuwahara: Apply filter to input image (in place)
attNumVals - Variable in class hr.irb.fastRandomForest.DataCache: Attribute description - holds a 0 for numeric attributes, and the number of available categories for nominal attributes.
AttributeClassPair - Class in ai: Inner class to order attributes while preserving class indices
attributeValue - Variable in class ai.AttributeClassPair: real value of the corresponding attribute
availableFeatures - Static variable in class trainableSegmentation.FeatureStack: names of available filters
availableFeatures - Static variable in class trainableSegmentation.FeatureStack3D: names of available filters

B

BalancedRandomForest - Class in ai: This class implements a Balanced Random Forest classifier, it is an ensemble classifier of random trees where all classes have the same representation in the training process.
BalancedRandomForest() - Constructor for class ai.BalancedRandomForest
BalancedRandomTree - Class in ai: This class implements a random tree based on the split function specified by the template in Splitter
BalancedRandomTree(Instances, ArrayList<Integer>, Splitter) - Constructor for class ai.BalancedRandomTree: Build random tree for a balanced random forest
balanceTrainingData(Instances) - Static method in class trainableSegmentation.WekaSegmentation: Balance number of instances per class
balanceTrainingData() - Method in class trainableSegmentation.WekaSegmentation: Balance number of instances per class (in the loaded training data)
Benchmark - Class in hr.irb.fastRandomForest: Runs 10 iterations of 10-fold crossvalidation on the supplied arff file(s) using weka.classifiers.trees.RandomForest, and FastRandomForest, and prints accuracy, AUC (averaged over all classes) and computation time to stdout.
BILATERAL - Static variable in class trainableSegmentation.FeatureStack: Bilateral flag index
buildClassifier(Instances) - Method in class ai.BalancedRandomForest: Build Balanced Random Forest
buildClassifier(Instances) - Method in class hr.irb.fastRandomForest.FastRandomForest: Builds a classifier for a set of instances.
buildClusterer() - Method in class trainableSegmentation.unsupervised.PixelClustering: Build current clusterer based on the current instances
buildEvaluator(Instances) - Method in class hr.irb.fastRandomForest.FRFAttributeEval

C

calculateCriterionVariance(float[][], float[][], float, float[][], float[][]) - Method in class trainableSegmentation.filters.Kuwahara
calculateCriterionVarianceDivMean(float[][], float[][], float, float[][], float[][]) - Method in class trainableSegmentation.filters.Kuwahara
calculateCriterionVarianceDivMean2(float[][], float[][], float, float[][], float[][]) - Method in class trainableSegmentation.filters.Kuwahara
call() - Method in class ai.VotesCollector: Determine predictions for a single instance.
call() - Method in class hr.irb.fastRandomForest.VotesCollector: Determine predictions for a single instance.
call() - Method in class hr.irb.fastRandomForest.VotesCollectorDataCache: Determine predictions for a single instance (defined in "instanceIdx").
CHANGE_CLASS_COLOR - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to change the color associated to a class
CHANGE_CLASS_NAME - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to change a class name
changeClassColor(String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Change the color associated to a class.
changeClassName(String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Change a class name
classicRandIndex(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.RandError: Calculate the Rand index between to clusters, as described by William M.
ClassificationStatistics - Class in trainableSegmentation.metrics: This class stores statistics from a classification
ClassificationStatistics(double, double, double, double, double) - Constructor for class trainableSegmentation.metrics.ClassificationStatistics: Create classification statistics
classifiedMismatches - Variable in class trainableSegmentation.metrics.WarpingResults
classifyListOfImages(ArrayList<ImagePlus>, Instances, AbstractClassifier, AtomicInteger, boolean) - Method in class trainableSegmentation.WekaSegmentation: Classify a list of images in a concurrent way
classifyMismatches2d(ImagePlus, ArrayList<Point3f>, int) - Method in class trainableSegmentation.metrics.WarpingError: Classify warping mismatches as MERGE, SPLIT, HOLE_ADDITION, HOLE_DELETION, OBJECT_ADDITION, OBJECT_DELETION
classifyMismatches2d(ImagePlus, ArrayList<Point3f>, double, double, int) - Method in class trainableSegmentation.metrics.WarpingError: Classify warping mismatches as MERGE, SPLIT, HOLE_ADDITION, HOLE_DELETION, OBJECT_ADDITION, OBJECT_DELETION and count the number of false positives and false negatives
classifySlice(ImagePlus, Instances, AbstractClassifier, AtomicInteger, boolean) - Method in class trainableSegmentation.WekaSegmentation: Classify a slice in a concurrent way
classIndex - Variable in class hr.irb.fastRandomForest.DataCache: Numeric index of the class attribute.
classValue - Variable in class ai.AttributeClassPair: index of the class associated to this pair
ClusteredWarpingMismatches - Class in trainableSegmentation.metrics: This class stores the number of mismatches after applying a topology-preserving warping.
ClusteredWarpingMismatches(int, int, int, int, int, int) - Constructor for class trainableSegmentation.metrics.ClusteredWarpingMismatches
clusterMismatchesByError(ImagePlus, ArrayList<Point3f>, int[]) - Method in class trainableSegmentation.metrics.WarpingError: Cluster the result mismatches from the warping so pixels belonging to the same error are only counted once.
clusterMismatchesByType(int[]) - Method in class trainableSegmentation.metrics.WarpingError: Cluster the result mismatches from the warping by types of errors.
Color_Clustering - Class in trainableSegmentation.unsupervised: Fiji plugin to perform color clustering on a 2D image or stack based on different color space representations (RGB, HSB and Lab) and all available clusterers in Weka.
Color_Clustering() - Constructor for class trainableSegmentation.unsupervised.Color_Clustering
ColorClustering - Class in trainableSegmentation.unsupervised: This class contains all the library methods to perform color clustering based on a few color space transformations and the Weka clusterers.
ColorClustering(ImagePlus) - Constructor for class trainableSegmentation.unsupervised.ColorClustering: Constructor using only image, features and clusterer will be set as null
ColorClustering(ImagePlus, int, ArrayList<ColorClustering.Channel>) - Constructor for class trainableSegmentation.unsupervised.ColorClustering: Creates color clustering object based on image, number of samples and selected channels.
ColorClustering.Channel - Enum in trainableSegmentation.unsupervised: Names of all channels (features) that can be used.
compareTo(PixelRecord) - Method in class trainableSegmentation.utils.PixelRecord: Compare with a pixel record based on its value and timestamp
computeDerivativeImage(double, int, int, ImagePlus) - Static method in class trainableSegmentation.ImageScience
computeDifferentialImage(double, int, int, int, ImagePlus) - Static method in class trainableSegmentation.ImageScience
computeEdgesImage(double, ImagePlus) - Static method in class trainableSegmentation.ImageScience
computeEigenimages(double, double, ImagePlus) - Static method in class trainableSegmentation.ImageScience
computeHessianImages(double, boolean, ImagePlus) - Static method in class trainableSegmentation.ImageScience
computeLaplacianImage(double, ImagePlus) - Static method in class trainableSegmentation.ImageScience
confusionMatrix(ImageProcessor, ImageProcessor, ArrayList<String>, int[]) - Static method in class trainableSegmentation.utils.Utils: Calculate confusion matrix bewtween two label images.
confusionMatrix(ImageStack, ImageStack, ArrayList<String>, int[]) - Static method in class trainableSegmentation.utils.Utils: Calculate confusion matrix bewtween two label images.
confusionMatrix(ImagePlus, ImagePlus, ArrayList<String>, int[]) - Static method in class trainableSegmentation.utils.Utils: Calculate confusion matrix bewtween two label images.
confusionMatrixBinarySlice(ImageProcessor, ImageProcessor, double) - Static method in class trainableSegmentation.WekaSegmentation: Calculate the confusion matrix of a slice (2 classes)
connectedComponents(ImagePlus, int) - Static method in class trainableSegmentation.utils.Utils: Connected components based on Find Connected Regions (from Mark Longair)
connectedComponents(ImagePlus, int, int) - Static method in class trainableSegmentation.utils.Utils: Connected components based on Find Connected Regions (from Mark Longair)
containsColorFeatures(Instances) - Method in class trainableSegmentation.WekaSegmentation: Check if a set of Weka instances has color attributes (given by the presence of the attribute names "Hue", "Saturation" or "Brightness").
containsExclusive2DFeatures(Instances) - Method in class trainableSegmentation.WekaSegmentation: Check if a set of Weka instances has 2D image attributes (given by the presence of the attribute names from FeatureStack which are not present in FeatureStack3D).
containsExclusive3DFeatures(Instances) - Method in class trainableSegmentation.WekaSegmentation: Check if a set of Weka instances has 3D image attributes (given by the presence of the attribute names from FeatureStack3D which are not present in FeatureStack).
CREATE_CLASS - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to create a new class
createClusteredImage(FeatureStackArray) - Method in class trainableSegmentation.unsupervised.ColorClustering: Creates clustered image based on provided FeatureStackArray and using private clusterer, returns as ImagePlus
createClusterer(AbstractClusterer) - Method in class trainableSegmentation.unsupervised.ColorClustering: Build clusterer using the current features and the provided number of clusters.
createFeatures() - Method in class trainableSegmentation.unsupervised.ColorClustering: Generate the features based on the selected color space channels.
createFile(String, Instances) - Method in class trainableSegmentation.unsupervised.ColorClustering: Create ARFF file of features with provided name.
createFSArray(ImagePlus) - Method in class trainableSegmentation.unsupervised.ColorClustering: Create FeatureStackArray based on provided image and selected channels (private variable)
createHeaderName(boolean, boolean) - Method in class trainableSegmentation.WekaSegmentation: Create the name of the training header.
createInBagSortedIndices() - Method in class hr.irb.fastRandomForest.DataCache: Invoked only when tree is trained.
createInstance(int, int, int) - Method in class trainableSegmentation.FeatureStack: Create instance (feature vector) of a specific coordinate
createInstance(int, int) - Method in class trainableSegmentation.FeatureStack: Create instance (feature vector) of a specific coordinate without a class, to be used on unsupervised clustering.
createInstanceInPlace(int, int, int, DenseInstance) - Method in class trainableSegmentation.FeatureStack: Create instance (feature vector) of a specific coordinate in place.
createInstances(ArrayList<String>) - Method in class trainableSegmentation.FeatureStack: Create the instances for the whole stack
createInstances(ArrayList<String>, FeatureStack) - Method in class trainableSegmentation.WekaSegmentation: Create instances of a feature stack (to be submitted to an Executor Service)
createKernel(int, int) - Method in class trainableSegmentation.filters.Kuwahara: create the convolution kernel
createLUT(Color[]) - Static method in class trainableSegmentation.utils.Utils: Create look-up table (LUT) from array of colors
createNewClass(String) - Static method in class trainableSegmentation.Weka_Segmentation: Create a new class
createPrecisionRecallPlot(ArrayList<ClassificationStatistics>) - Static method in class trainableSegmentation.utils.Utils: Create plot with the precision-recall curve
createProbabilityMaps(FeatureStackArray) - Method in class trainableSegmentation.unsupervised.ColorClustering: Create probability map based on provided features and current cluster.
createProbImgFromTestData() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Apply classifier to a set of images and create a multi-channel probability distribution image.
createProbImgFromTestData(ImagePlus, int) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Create multi-channel probability distribution image from image
createTrainingInstances() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Create training instances out of the user markings
createTrainingInstances() - Method in class trainableSegmentation.WekaSegmentation: Create training instances out of the user markings
cursor - Variable in class trainableSegmentation.utils.Neighborhood2D
Cursor2D - Class in trainableSegmentation.utils
Cursor2D(int, int) - Constructor for class trainableSegmentation.utils.Cursor2D

D

data - Variable in class ai.VotesCollector
data - Variable in class hr.irb.fastRandomForest.VotesCollector
data - Variable in class hr.irb.fastRandomForest.VotesCollectorDataCache
DataCache - Class in hr.irb.fastRandomForest: Stores a dataset that in FastRandomTrees use for training.
DataCache(Instances) - Constructor for class hr.irb.fastRandomForest.DataCache: Creates a DataCache by copying data from a weka.core.Instances object.
DataCache(DataCache) - Constructor for class hr.irb.fastRandomForest.DataCache: Makes a copy of a DataCache.
DEFAULT_FLAGS - Static variable in class trainableSegmentation.metrics.WarpingError: default flags
DELETE_TRACE - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to delete the current trace
deleteExample(int, int, int) - Method in class trainableSegmentation.WekaSegmentation: Remove an example list from a class and specific slice
deleteTrace(String, String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Delete a specific ROI from the list of a specific class and slice
DERIVATIVES - Static variable in class trainableSegmentation.FeatureStack: Derivatives filter flag index
DERIVATIVES - Static variable in class trainableSegmentation.FeatureStack3D: Derivatives filter flag index
dilate(FloatProcessor) - Static method in class trainableSegmentation.utils.Utils: Applies the morphological dilate operator.
disableMissingFeatures(GenericDialog) - Static method in class trainableSegmentation.Weka_Segmentation: Disables features which rely on missing third party libraries.
displayGraphs(Instances, AbstractClassifier) - Static method in class trainableSegmentation.Weka_Segmentation: Display the threshold curve window (for precision/recall, ROC, etc.).
distributionForInstance(Instance) - Method in class ai.BalancedRandomForest: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the class probability distribution for an instance.
doClassBalance() - Method in class trainableSegmentation.WekaSegmentation: Get the boolean flag to enable/disable the class balance
DOG - Static variable in class trainableSegmentation.FeatureStack: Difference of Gaussians filter flag index
DOG - Static variable in class trainableSegmentation.FeatureStack3D: difference of Gaussian filter flag index
doHomogenizeClasses() - Method in class trainableSegmentation.WekaSegmentation: Deprecated.
use doClassBalance

E

EDGES - Static variable in class trainableSegmentation.FeatureStack3D: edge filter flag index
ENTROPY - Static variable in class trainableSegmentation.FeatureStack: entropy filter flag index
Entropy_Filter - Class in trainableSegmentation.filters: This class implements a circular entropy filter
Entropy_Filter() - Constructor for class trainableSegmentation.filters.Entropy_Filter
entropyConditionedOnRows(double[][]) - Static method in class hr.irb.fastRandomForest.SplitCriteria: Similar to weka.core.ContingencyTables.entropyConditionedOnRows.
entropyOverColumns(double[][]) - Static method in class hr.irb.fastRandomForest.SplitCriteria: Similar to weka.core.ContingencyTables.entropyOverColumns Does not output entropy, output is modified to make routine faster: the result is not divided by "total", as the total is a constant in all operations (subtraction, comparison) performed as a part of splitting in FastRfTree.
enumerateMeasures() - Method in class ai.BalancedRandomForest: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns an enumeration of the additional measure names.
erode(FloatProcessor) - Static method in class trainableSegmentation.utils.Utils: Applies the morphological erode operator.
evaluate(Instance) - Method in class ai.BalancedRandomTree: Evaluate sample
evaluate(Instance) - Method in class ai.GiniFunction: Evaluate a single instance based on the current state of the split function
evaluate(Instance) - Method in class ai.InformationGainFunction: Evaluate a single instance based on the current state of the split function
evaluate(Instance) - Method in class ai.SplitFunction
evaluateAttribute(int) - Method in class hr.irb.fastRandomForest.FRFAttributeEval
extractPixelValues(ImageProcessor, double, double) - Method in class trainableSegmentation.utils.WatershedTransform2D: Extract pixel values from input image such that they have value h, hMin <= h <= hMax.

F

falseNegatives - Variable in class trainableSegmentation.metrics.ClassificationStatistics: number of false negatives
falsePositives - Variable in class trainableSegmentation.metrics.ClassificationStatistics: number of false positives
fastLog2(float) - Static method in class hr.irb.fastRandomForest.SplitCriteria: A fast approximation of log base 2, in single precision.
FastRandomForest - Class in hr.irb.fastRandomForest: Based on the "weka.classifiers.trees.RandomForest" class, revision 1.12, by Richard Kirkby, with minor modifications: uses FastRfBagger with FastRandomTree, instead of Bagger with RandomTree. stores dataset header (instead of every Tree storing its own header) checks if only ZeroR model is possible (instead of each Tree checking) added "-threads" option
FastRandomForest() - Constructor for class hr.irb.fastRandomForest.FastRandomForest
FastRfUtils - Class in hr.irb.fastRandomForest: Utility functions for sorting float (single-precision) arrays, and for normalizing double arrays.
FeatureStack - Class in trainableSegmentation: This class stores the stacks of features that will be used during the trainable/weka segmentations.
FeatureStack(ImagePlus) - Constructor for class trainableSegmentation.FeatureStack: Construct object to store stack of image features.
FeatureStack(int, int, boolean) - Constructor for class trainableSegmentation.FeatureStack: Initialize empty feature stack.
FeatureStack(ImageProcessor) - Constructor for class trainableSegmentation.FeatureStack: Construct object to store stack of image features.
FeatureStack3D - Class in trainableSegmentation: This class defines 3D images features to be used for classification/segmentation.
FeatureStack3D(ImagePlus) - Constructor for class trainableSegmentation.FeatureStack3D: Construct object to store stack of image features
FeatureStackArray - Class in trainableSegmentation: This class stores the feature stacks of a set of input slices.
FeatureStackArray(int) - Constructor for class trainableSegmentation.FeatureStackArray: Initialize a feature stack list of a specific size (with default values for the rest of parameters).
FeatureStackArray(int, float, float, boolean, int, int, boolean[]) - Constructor for class trainableSegmentation.FeatureStackArray: Initialize a feature stack list of a specific size
FeatureStackArray(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.FeatureStackArray: Create a feature stack array based on specific filters
fill(ImageProcessor, int, int) - Static method in class trainableSegmentation.utils.Utils: Binary fill
filter(ImageProcessor, ImageStack) - Method in class trainableSegmentation.filters.Kuwahara: Apply Kuwahara filter to input image
filterFeatureStackByList() - Method in class trainableSegmentation.WekaSegmentation: Filter feature stack based on the list of feature names to use
filterFeatureStackByList(ArrayList<String>, FeatureStack) - Static method in class trainableSegmentation.WekaSegmentation: Filter feature stack based on the list of feature names to use
filterSmallObjectsAndHoles(FloatProcessor, double, int) - Static method in class trainableSegmentation.utils.Utils: Filter small objects and holes at a specific threshold value
foregroundRestrictedFscore(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate the F-score of the variation of information between two clusters using the foreground restriction, i.e.
foregroundRestrictedFScoreN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate F-score of foreground-restricted variation of information with N^2 normalization
foregroundRestrictedRandError(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand error between some 2D original labels and the corresponding proposed labels.
foregroundRestrictedRandIndex(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.RandError: Calculate the Rand index between to clusters, as described by William M.
foregroundRestrictedScoreAfterThinningN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate foreground-restricted information theoretic score with N^2 normalization after border thinning
foregroundRestrictedStats(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get the foreground-restricted variation of information statistics (entropy values, F-score, etc)
foregroundRestrictedStats(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate the variation of information statistics (entropy values, F-score, etc) between two clusters using the foreground restriction, i.e.
foregroundRestrictedVI(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate the variation of information between two clusters using the foreground restriction, i.e.
foregroundRestrictedVIN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate foreground-restricted variation of information with N^2 normalization
FRFAttributeEval - Class in hr.irb.fastRandomForest: Evaluate the merit of each attribute using a random forest.
FRFAttributeEval() - Constructor for class hr.irb.fastRandomForest.FRFAttributeEval: Constructor
FRFAttributeEval(FastRandomForest) - Constructor for class hr.irb.fastRandomForest.FRFAttributeEval: Constructor.
fromLabel(String) - Static method in enum trainableSegmentation.unsupervised.ColorClustering.Channel: Get channel from label (label is a String).
fScore - Variable in class trainableSegmentation.metrics.ClassificationStatistics: F-score, harmonic mean of precision and recall
fScore - Variable in class trainableSegmentation.metrics.InformationStatistics: F-score, harmonic mean of precision and recall
fscore(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate the F-score of the variation of information between two clusters A and B ( harmonic mean of C(A|B) and C(B|A) )
fScoreN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate F-score of variation of information with N^2 normalization

G

GABOR - Static variable in class trainableSegmentation.FeatureStack: Gabor filter flag index
GAUSSIAN - Static variable in class trainableSegmentation.FeatureStack: Gaussian filter flag index
GAUSSIAN - Static variable in class trainableSegmentation.FeatureStack3D: Gaussian filter flag index
get(int) - Method in class trainableSegmentation.FeatureStackArray: Get n-th stack in the array (remember n>=0)
GET_PROBABILITY - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to get the probability maps
GET_RESULT - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to get the binary result
getAdjustedRandErrorConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.AdjustedRandError: Get adjusted Rand error between two images in a concurrent way (to be submitted to an Executor Service).
getAllLabels() - Static method in enum trainableSegmentation.unsupervised.ColorClustering.Channel: Get all labels in a String[] structure.
getAnisotropicDiffusion(ImagePlus, int, int, int, float, float, float) - Method in class trainableSegmentation.FeatureStack: Apply anisotropic diffusion in a concurrent way (to be submitted in an ExecutorService)
getBilateralFilter(ImagePlus, double, double) - Method in class trainableSegmentation.FeatureStack: Apply bilateral filter in a concurrent way (to be submitted in an ExecutorService)
getCapabilities() - Method in class ai.BalancedRandomForest: Returns default capabilities of the classifier.
getCapabilities() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns default capabilities of the classifier.
getCapabilities() - Method in class hr.irb.fastRandomForest.FRFAttributeEval
getClassCoordinates(ImagePlus, ImagePlus) - Static method in class trainableSegmentation.utils.Utils: Get the binary class coordinates from a label image (2D image or stack)
getClassifiedImage() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Get current classification result
getClassifiedImage() - Method in class trainableSegmentation.WekaSegmentation: Get current classification result
getClassifier() - Method in class trainableSegmentation.WekaSegmentation: Returns the current classifier.
getClassifierDistribution(Instances, int, int, int) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Apply current classifier to set of instances to get a probability distribution.
getClassLabel(int) - Method in class trainableSegmentation.WekaSegmentation: Get the label name of a class.
getClassLabels() - Method in class trainableSegmentation.WekaSegmentation: Get the current class labels
getClusteredWarpingMismatches(ImagePlus, ImagePlus, ImagePlus, double, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Get all the mismatches of warping a source image into a target image and clustering them when they belong to the same error.
getClusteredWarpingMismatchesConcurrent(ImageProcessor, ImageProcessor, ImageProcessor, double, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the simple point warping in a concurrent way (to be submitted to an Executor Service)
getComputeImportances() - Method in class hr.irb.fastRandomForest.FastRandomForest
getConfusionMatrix(ImagePlus, ImagePlus, int, int) - Method in class trainableSegmentation.WekaSegmentation: Get confusion matrix (binary images)
getConfusionMatrix(ImagePlus, ImagePlus, double) - Static method in class trainableSegmentation.WekaSegmentation: Get confusion matrix (2 classes)
getConnectivity() - Method in class trainableSegmentation.utils.WatershedTransform2D: Get the pixel connectivity (4 or 8)
getCursor() - Method in class trainableSegmentation.utils.Neighborhood2D
getCursor() - Method in class trainableSegmentation.utils.PixelRecord
getDefaultEnabledFeatures() - Static method in class trainableSegmentation.FeatureStack3D: Get the list of flags for the default features
getDerivatives(ImagePlus, double, int, int) - Method in class trainableSegmentation.FeatureStack: Get derivatives features (to be submitted in an ExecutorService)
getDerivatives(ImagePlus, double, int, int, int) - Method in class trainableSegmentation.FeatureStack3D: Get derivatives features (to be submitted in an ExecutorService)
getDoG(ImagePlus, float, float) - Method in class trainableSegmentation.FeatureStack: Get difference of Gaussians (to be submitted to an ExecutorService)
getDoG(ImagePlus, double, double) - Method in class trainableSegmentation.FeatureStack3D: Get difference of Gaussian features (to be submitted in an ExecutorService).
getDualPixelErrorMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.WarpingError: Get the best F-score of the pixel error between proposed and original labels (and all the way around) over a set of thresholds
getDualPrecisionRecallStats(double, double, double) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the precision-recall values based on pixel error between some warped 2D original labels and the corresponding proposed labels in both directions (from original labels to proposal and reversely).
getEdges(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Edges features (to be submitted in an ExecutorService)
getEnabledFeatures() - Method in class trainableSegmentation.FeatureStack: Get the list of enabled features flags
getEnabledFeatures() - Method in class trainableSegmentation.FeatureStack3D: Get the boolean flags of enabled features
getEnabledFeatures() - Method in class trainableSegmentation.FeatureStackArray: Get the features enabled for the reference stack
getEnabledFeatures() - Method in class trainableSegmentation.WekaSegmentation: Get the current enabled features
getEntropy(ImagePlus, int, int) - Method in class trainableSegmentation.FeatureStack: Calculate entropy filter filter concurrently
getEntropy(ImageProcessor, int, int) - Method in class trainableSegmentation.filters.Entropy_Filter: Get the entropy filter version of an image
getExamples(int, int) - Method in class trainableSegmentation.WekaSegmentation: Return the list of examples for a certain class.
getFeatureImportances() - Method in class hr.irb.fastRandomForest.FastRandomForest
getFeaturesInstances() - Method in class trainableSegmentation.unsupervised.ColorClustering: Get features instances that are being used
getFeaturesInstances() - Method in class trainableSegmentation.unsupervised.PixelClustering: Get features instances that are being used
getFeatureStack(int) - Method in class trainableSegmentation.WekaSegmentation: Get current feature stack
getFeatureStackArray() - Method in class trainableSegmentation.FeatureStack3D: Convert FeatureStack3D into a feature stack array (for 2D stacks).
getFeatureStackArray() - Method in class trainableSegmentation.unsupervised.ColorClustering: Get FeatureStackArray that is being used
getFeatureStackArray() - Method in class trainableSegmentation.WekaSegmentation: Get the current feature stack array
getFilter(ImagePlus, ImageProcessor, String) - Method in class trainableSegmentation.FeatureStack: Apply a filter to the original image (to be submitted to an ExecutorService)
getForegroundRestrictedFscore(double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get F-score of the foreground-restricted variation of information for a given threshold of the proposal labels.
getforegroundRestrictedFscoreConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get F-score of the foreground-restricted variation of information in a concurrent way.
getForegroundRestrictedFscores(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted F-score of the variation of information over a set of thresholds
getForegroundRestrictedGroundTruthDisagreements(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.RandError: Get foreground-restricted disagreements between prediction and ground truth labels with N^2 normalization (mergers).
getForegroundRestrictedGroundTruthDisagreements(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted disagreements between prediction and ground truth labels with N^2 normalization (mergers).
getForegroundRestrictedMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get the best F-score of the foreground-restricted variation of information over a set of thresholds
getForegroundRestrictedMetricValue(double) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand error in 2D between some original labels and the corresponding proposed labels.
getForegroundRestrictedMetricValue(double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted variation of information between original and proposed labels for a given threshold
getForegroundRestrictedPredictionDisagreements(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.RandError: Get foreground-restricted disagreements between ground truth and prediction labels with N^2 normalization (splits).
getForegroundRestrictedPredictionDisagreements(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted disagreements between ground truth and prediction labels with N^2 normalization (splits).
getForegroundRestrictedRandAfterThinningStats(double, boolean) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand score (N^2 normalization) and its derived statistics in 2D between some original labels and the corresponding proposed labels after border thinning.
getForegroundRestrictedRandAfterThinningStats(double, double, double, boolean) - Method in class trainableSegmentation.metrics.RandError: Calculate the precision-recall values based on the foreground-restricted Rand score between some 2D original labels and the corresponding proposed labels (after border thinning).
getForegroundRestrictedRandAfterThinningStatsConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Get foreground-restricted Rand score value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
getForegroundRestrictedRandAfterThinningStatsN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand precision and recall between some 2D original labels and the corresponding proposed labels with N^2 normalization.
getForegroundRestrictedRandErrorConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Get foreground-restricted Rand error between two images in a concurrent way (to be submitted to an Executor Service).
getForegroundRestrictedRandIndexMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.RandError: Get the best F-score of the foreground-restricted Rand index over a set of thresholds.
getForegroundRestrictedRandIndexStats(double) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand error (N^2 normalization) and its derived statistics in 2D between some original labels and the corresponding proposed labels.
getForegroundRestrictedRandIndexStats(double, double, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the precision-recall values based on the foreground-restricted Rand index between some 2D original labels and the corresponding proposed labels.
getForegroundRestrictedRandIndexStatsConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Get foreground-restricted Rand index value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
getForegroundRestrictedRandIndexStatsN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand precision and recall between some 2D original labels and the corresponding proposed labels with N^2 normalization.
getForegroundRestrictedRandIndexStatsN2(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.RandError: Get foreground-restricted Rand statistics (precision and recall) with N^2 normalization.
getForegroundRestrictedRandIndexStatsPerSlice(double) - Method in class trainableSegmentation.metrics.RandError: Calculate the foreground-restricted Rand index (N^2 normalization) and its derived statistics in 2D between some original labels and the corresponding proposed labels.
getForegroundRestrictedScoreAfterThinning(double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted information theoretic score after border thinning for a given threshold of the proposal labels.
getforegroundRestrictedScoreAfterThinningConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted information theoretic score after border thinning in a concurrent way.
getForegroundRestrictedScoresAfterThinning(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get foreground-restricted information theoretic score after thinning over a set of thresholds
getForegroundRestrictedStatsPerSlice(double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get the foreground-restricted variation of information statistics (entropy values, F-score, etc) per slice and for a given threshold value.
getForegroundRestrictedVIConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate foreground-restricted variation of information between two images in a concurrent way (to be submitted to an Executor Service).
getFrfProto() - Method in class hr.irb.fastRandomForest.FRFAttributeEval
getFscore(double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get F-score of the variation of information for a given threshold of the proposal labels.
getFscoreConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get F-score of the variation of information in a concurrent way.
getFscores(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get F-score of the variation of information over a set of thresholds
getGabor(ImagePlus, double, double, double, double, int, ExecutorService) - Method in class trainableSegmentation.FeatureStack: Get Gabor features (to be submitted in an ExecutorService)
getGaussian(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Gaussian features (to be submitted to an ExecutorService).
getGaussianBlur(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Calculate Gaussian filter concurrently
getGoldenAngleLUT() - Static method in class trainableSegmentation.utils.Utils: Create golden angle LUT (starting in red)
getGradient(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Get Sobel filter version of the original image (to be called from an ExecutorService)
getHeight() - Method in class trainableSegmentation.FeatureStack: Get stack height
getHeight() - Method in class trainableSegmentation.FeatureStackArray: Get the height of the feature stacks.
getHessian(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Get Hessian features from original image (to be submitted in an ExecutorService).
getHessian(ImagePlus, double, boolean) - Method in class trainableSegmentation.FeatureStack3D: Get Hessian features (to be submitted in an ExecutorService)
getHSB(ImagePlus) - Method in class trainableSegmentation.FeatureStack: Calculate HSB out of the RGB channels (to be submitted to an ExecutorService)
getImage() - Method in class trainableSegmentation.unsupervised.ColorClustering: Get image that is being used
getKappa(ClassificationStatistics) - Static method in class trainableSegmentation.utils.Utils: Get Kappa statistic
getKuwaharaFeatures(ImagePlus, int, int, int) - Method in class trainableSegmentation.FeatureStack: Get Kuwahara filter features (to be submitted in an ExecutorService)
getLabel(int) - Method in class trainableSegmentation.FeatureStackArray: Get a specific label of the reference stack
getLabelImage() - Static method in class trainableSegmentation.Weka_Segmentation: Create label image out of the current user traces.
getLaplacian(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack: Get Laplacian features (to be submitted in an ExecutorService)
getLaplacian(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Laplacian features (to be submitted in an ExecutorService)
getLipschitzFilter(ImagePlus, boolean, boolean, double) - Method in class trainableSegmentation.FeatureStack: Apply Lipschitz filter in a concurrent way (to be submitted in an ExecutorService)
getLoadedTrainingData() - Method in class trainableSegmentation.WekaSegmentation: Returns a the loaded training data or null, if no training data was loaded.
getMatchingFiles(String, String) - Static method in class hr.irb.fastRandomForest.Benchmark: When supplied with a directory name, returns an ArrayList with all the files inside that directory that have the specified extension.
getMax(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Get original image after maximum filtering (to be called from an ExecutorService)
getMaxDepth() - Method in class hr.irb.fastRandomForest.FastRandomForest: Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class trainableSegmentation.WekaSegmentation: Get maximum depth of the random forest
getMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get the best F-score of the variation of information over a set of thresholds
getMaximalVInfoAfterThinning(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get the best V_Info after thinning over a set of thresholds.
getMaximalVRandAfterThinning(double, double, double, boolean) - Method in class trainableSegmentation.metrics.RandError: Get the best V_Rand after thinning over a set of thresholds.
getMaximum(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Maximum features (to be submitted to an ExecutorService).
getMaximumSigma() - Method in class trainableSegmentation.WekaSegmentation: Get the maximum sigma/radius to use in the features
getMean(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Get original image after mean filtering (to be called from an ExecutorService)
getMean(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Mean features (to be submitted to an ExecutorService).
getMeasure(String) - Method in class ai.BalancedRandomForest: Returns the value of the named measure.
getMeasure(String) - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the value of the named measure.
getMedian(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Get original image after median filtering (to be called from an ExecutorService)
getMedian(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Median features (to be submitted to an ExecutorService).
getMembraneFeatures(ImagePlus, int, int) - Method in class trainableSegmentation.FeatureStack: Get membrane features (to be submitted in an ExecutorService)
getMembranePatchSize() - Method in class trainableSegmentation.WekaSegmentation: Get the membrane patch size
getMembraneSize() - Method in class trainableSegmentation.FeatureStack: Get the current membrane thickness
getMembraneThickness() - Method in class trainableSegmentation.WekaSegmentation: Get current expected membrane thickness
getMetricValue(double) - Method in class trainableSegmentation.metrics.AdjustedRandError: Calculate the Rand error in 2D between some original labels and the corresponding proposed labels.
getMetricValue(double) - Method in class trainableSegmentation.metrics.Metrics
getMetricValue(double) - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error in 2D between some original labels and the corresponding proposed labels.
getMetricValue() - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error in 2D between some original labels and the corresponding proposed labels (without thresholding).
getMetricValue(double) - Method in class trainableSegmentation.metrics.RandError: Calculate the Rand error in 2D between some original labels and the corresponding proposed labels.
getMetricValue(double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get variation of information between original and proposed labels for a given threshold
getMetricValue(double) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the classic topology-preserving warping error \cite{Jain10} in 2D between some original labels and the corresponding proposed labels.
getMetricValue(double, boolean) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the topology-preserving warping error in 2D between some original labels and the corresponding proposed labels.
getMetricValue(double, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the topology-preserving warping error in 2D between some original labels and the corresponding proposed labels.
getMin(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Get original image after minimum filtering (to be called from an ExecutorService)
getMinimum(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Minimum features (to be submitted to an ExecutorService).
getMinimumForegroundRestrictedMetricValue(double, double, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Get the minimum foreground-restricted metric value over a set of thresholds
getMinimumMetricValue(double, double, double) - Method in class trainableSegmentation.metrics.Metrics: Get the minimum metric value over a set of thresholds
getMinimumSigma() - Method in class trainableSegmentation.WekaSegmentation: Get the minimum sigma (radius) to use in the features
getMinimumSplitsAndMergersErrorValue(double, double, double, boolean) - Method in class trainableSegmentation.metrics.WarpingError: Calculate error with the minimum number of splits and mergers for different thresholds
getMinimumSplitsAndMergersErrorValue(double, double, double, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Calculate error with the minimum number of splits and mergers for different thresholds
getMismatchImage(WarpingResults, int) - Method in class trainableSegmentation.metrics.WarpingError: Get the image with the classified mismatches
getMismatchImage(WarpingResults, int, int) - Method in class trainableSegmentation.metrics.WarpingError: Get the image with the classified mismatches
getMismatchImage(WarpingResults, int[], int) - Method in class trainableSegmentation.metrics.WarpingError: Get the image with the classified mismatches
getModelVersion() - Method in class trainableSegmentation.WekaSegmentation: Get the version of the plugin used for creating the model.
getNeighborhood(ImageProcessor, Point, int, int) - Method in class trainableSegmentation.metrics.WarpingError: Get neighborhood of a pixel in a 2D image
getNeighbors(ImagePlus, int, int) - Method in class trainableSegmentation.FeatureStack: Calculate 8 neighbors concurrently
getNeighbors() - Method in class trainableSegmentation.utils.Neighborhood2D
getNeighbors() - Method in class trainableSegmentation.utils.Neighborhood2DC4
getNeighbors() - Method in class trainableSegmentation.utils.Neighborhood2DC8
getNumFeatures() - Method in class ai.BalancedRandomForest: Get the number of random features to use in each node of the random trees.
getNumFeatures() - Method in class hr.irb.fastRandomForest.FastRandomForest: Get the number of features used in random selection.
getNumOfClasses() - Method in class trainableSegmentation.WekaSegmentation: Get the current number of classes.
getNumOfFeatures() - Method in class trainableSegmentation.FeatureStackArray: Get the number of features of the reference stack (consistent all along the array)
getNumOfTrees() - Method in class trainableSegmentation.WekaSegmentation: Get current number of trees (for random forest training)
getNumRandomFeatures() - Method in class trainableSegmentation.WekaSegmentation: Get number of random features (random forest training)
getNumSamples() - Method in class trainableSegmentation.unsupervised.ColorClustering: Get number of samples that is being used
getNumThreads() - Method in class hr.irb.fastRandomForest.FastRandomForest: Get the number of simultaneous threads used in training, 0 for autodetect.
getNumTrees() - Method in class ai.BalancedRandomForest: Get the number of trees in the forest
getNumTrees() - Method in class hr.irb.fastRandomForest.FastRandomForest: Get the value of numTrees.
getOptions() - Method in class ai.BalancedRandomForest: Gets the current settings of the forest.
getOptions() - Method in class hr.irb.fastRandomForest.FastRandomForest: Gets the current settings of the forest.
getPixelErrorConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.PixelError: Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
getPixelErrorConcurrent(ImageProcessor, ImageProcessor) - Method in class trainableSegmentation.metrics.PixelError: Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
getPixelErrorMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.PixelError: Get the best F-score of the pixel error over a set of thresholds
getPixelErrorMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.WarpingError: Get the best F-score of the pixel error between proposed and original labels over a set of thresholds
getPrecisionRecallStats(double, double, double) - Method in class trainableSegmentation.metrics.PixelError: Calculate the precision-recall values based on pixel error between some 2D original labels and the corresponding proposed labels.
getPrecisionRecallStats(double) - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
getPrecisionRecallStats(double, ImagePlus) - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
getPrecisionRecallStats(double, double, double) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the precision-recall values based on pixel error between some warped 2D original labels and the corresponding proposed labels.
getPrecisionRecallStatsConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.PixelError: Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
getPrecisionRecallStatsConcurrent(ImageProcessor, ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.PixelError: Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
getPrecisionRecallStatsPerSlice(double) - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
getPrecisionRecallStatsSplitsAndMergers(double, double, double, int, boolean, boolean) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the precision-recall values based on pixel error between some warped 2D original labels and the corresponding proposed labels (taking into account only the split and merger pixels).
getPrecisionRecallStatsSplitsAndMergersPerSlice(double, int, boolean) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the precision-recall values based on pixel error between some warped 2D original labels and the corresponding proposed labels (taking into account only the split and merger pixels).
getPrecRecArea(ArrayList<ClassificationStatistics>) - Static method in class trainableSegmentation.utils.Utils: Get area under the Precision/Recall curve
getPrincipalComponents(ImagePlus, int, int) - Static method in class trainableSegmentation.utils.PrincipalComponentAnalysis
getProbability() - Static method in class trainableSegmentation.Weka_Segmentation: Display the current probability maps
getProcessor(int) - Method in class trainableSegmentation.FeatureStack: Get slice image processor.
getRandErrorConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Get standard Rand error between two images in a concurrent way (to be submitted to an Executor Service).
getRandIndexMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.RandError: Get the best F-score of the standard Rand index over a set of thresholds.
getRandIndexMaximalFScore(double, double, double) - Method in class trainableSegmentation.metrics.WarpingError: Get the best F-score of the Rand index based on Rand index between some warped 2D original labels and the corresponding proposed labels.
getRandIndexStats(double) - Method in class trainableSegmentation.metrics.RandError: Calculate the standard Rand index and its derived statistics in 2D between some original labels and the corresponding proposed labels.
getRandIndexStats(double, double, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the precision-recall values based on standard Rand index between some 2D original labels and the corresponding proposed labels.
getRandIndexStats(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.RandError: Calculate the standard Rand index between to clusters, as described by William M.
getRandIndexStats(double, double, double) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the precision-recall values based on Rand index between some warped 2D original labels and the corresponding proposed labels.
getRandIndexStatsConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Get standard Rand index value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
getRandomNumberGenerator(long) - Method in class hr.irb.fastRandomForest.DataCache: Returns a random number generator.
getReferenceSliceIndex() - Method in class trainableSegmentation.FeatureStackArray: Get the index of the reference slice in the feature stack array.
getResult() - Static method in class trainableSegmentation.Weka_Segmentation: Get the current result (labeled image)
getRevision() - Method in class hr.irb.fastRandomForest.FastRandomForest
getRevision() - Method in class hr.irb.fastRandomForest.FRFAttributeEval
getROCArea(ArrayList<ClassificationStatistics>) - Static method in class trainableSegmentation.utils.Utils: Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
getSeed() - Method in class ai.BalancedRandomForest: Get the seed used to initialize the random number generators
getSeed() - Method in class hr.irb.fastRandomForest.FastRandomForest: Gets the seed for the random number generations
getSelectedClusterer() - Method in class trainableSegmentation.unsupervised.PixelClustering: Get selected clusterer
getSize() - Method in class trainableSegmentation.FeatureStack: Get stack size
getSize() - Method in class trainableSegmentation.FeatureStackArray: Get the number of feature stacks
getSliceLabel(int) - Method in class trainableSegmentation.FeatureStack: Get slice label
getSplitFunction(Instances, ArrayList<Integer>) - Method in class ai.Splitter: Calculate split function based on the input data
getSplitsAndMergers(double, double, double, boolean) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the number of splits and mergers for different thresholds
getSplitsAndMergers(double, double, double, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the number of splits and mergers for different thresholds
getStack() - Method in class trainableSegmentation.FeatureStack: Get current stack of image features.
getStructure(ImagePlus, double, double) - Method in class trainableSegmentation.FeatureStack: Get structure tensor features (to be submitted in an ExecutorService).
getStructure(ImagePlus, double, double) - Method in class trainableSegmentation.FeatureStack3D: Get structure tensor features (to be submitted in an ExecutorService).
getTechnicalInformation() - Method in class ai.BalancedRandomForest: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTestConfusionMatrix(ImagePlus, ImagePlus, int, int) - Method in class trainableSegmentation.WekaSegmentation: Get the confusion matrix for an input image and its expected labels
getTestConfusionMatrix(ImagePlus, ImagePlus, ImagePlus, int, int) - Method in class trainableSegmentation.WekaSegmentation: Get the confusion matrix for an input image and its expected labels
getTestError(ImagePlus, ImagePlus, int, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Get test error of current classifier on a specific image and its binary labels
getTestError(ImagePlus, ImagePlus, ImagePlus, int, int, boolean) - Method in class trainableSegmentation.WekaSegmentation: Get test error of current classifier on a specific image and its binary labels
getTheClusterer() - Method in class trainableSegmentation.unsupervised.ColorClustering: Get abstract clusterer that has been created
getTraceTrainingData() - Method in class trainableSegmentation.WekaSegmentation: Returns a the trace training data or null, if no examples have been given.
getTrainHeader() - Method in class trainableSegmentation.WekaSegmentation: Get the current training header
getTrainingError(boolean) - Method in class trainableSegmentation.WekaSegmentation: Get training error (from loaded data).
getTrainingInstances() - Method in class trainableSegmentation.WekaSegmentation: Get loaded (or accumulated) training instances
getValue() - Method in class trainableSegmentation.utils.PixelRecord
getVariance(ImagePlus, float) - Method in class trainableSegmentation.FeatureStack: Calculate variance filter concurrently
getVariance(ImagePlus, double) - Method in class trainableSegmentation.FeatureStack3D: Get Variance features (to be submitted to an ExecutorService).
getVI(ShortProcessor, ShortProcessor) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate the variation of information between two clusters (N^2 normalization)
getVIConcurrent(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate variation of information between two images in a concurrent way (to be submitted to an Executor Service).
getWarpingResults(double, boolean, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Calculate warping error and return the related result images and values.
getWarpingResultsConcurrent(ImageProcessor, ImageProcessor, ImageProcessor, double, boolean, int, int, boolean) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the simple point warping in a concurrent way (to be submitted to an Executor Service)
getWidth() - Method in class trainableSegmentation.FeatureStack: Get stack width
getWidth() - Method in class trainableSegmentation.FeatureStackArray: Get the width of the feature stacks.
getX() - Method in class trainableSegmentation.utils.Cursor2D
getY() - Method in class trainableSegmentation.utils.Cursor2D
GiniFunction - Class in ai: This class implements a split function based on the Gini coefficient
GiniFunction(int, Random) - Constructor for class ai.GiniFunction: Constructs a Gini function (initialize it)
globalInfo() - Method in class ai.BalancedRandomForest: Returns a string describing classifier
globalInfo() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns a string describing classifier

H

HESSIAN - Static variable in class trainableSegmentation.FeatureStack: Hessian filter flag index
HESSIAN - Static variable in class trainableSegmentation.FeatureStack3D: Hessian filter flag index
HOLE_ADDITION - Static variable in class trainableSegmentation.metrics.WarpingError: hole addition error flag
HOLE_DELETION - Static variable in class trainableSegmentation.metrics.WarpingError: hole deletion error flag
homogenizeTrainingData(Instances) - Static method in class trainableSegmentation.WekaSegmentation: Deprecated.
use balanceTrainingData
homogenizeTrainingData() - Method in class trainableSegmentation.WekaSegmentation: Deprecated.
use balanceTrainingData
hr.irb.fastRandomForest - package hr.irb.fastRandomForest
hs - Variable in class trainableSegmentation.metrics.InformationStatistics: entropy of predicted segmentation S
hst - Variable in class trainableSegmentation.metrics.InformationStatistics: conditional entropy of S given T
ht - Variable in class trainableSegmentation.metrics.InformationStatistics: entropy of ground truth T
hts - Variable in class trainableSegmentation.metrics.InformationStatistics: conditional entropy of T given S

I

image - Variable in class trainableSegmentation.unsupervised.Color_Clustering: input image
ImageOverlay - Class in trainableSegmentation: This class implements an overlay based on an image.
ImageOverlay() - Constructor for class trainableSegmentation.ImageOverlay: Empty constructor.
ImageOverlay(ImageProcessor) - Constructor for class trainableSegmentation.ImageOverlay: Construct an image overlay.
ImageScience - Class in trainableSegmentation: Helper class which encapsulates access to the optional ImageScience library.
IMAGESCIENCE_FEATURES - Static variable in class trainableSegmentation.FeatureStack: Features only available if the ImageScience library is present.
IMAGESCIENCE_FEATURES - Static variable in class trainableSegmentation.FeatureStack3D: Features only available if the ImageScience library is present.
imH - Variable in class trainableSegmentation.filters.Kuwahara
imW - Variable in class trainableSegmentation.filters.Kuwahara
inBag - Variable in class ai.VotesCollector
inBag - Variable in class hr.irb.fastRandomForest.DataCache: Is instance in 'bag' created by bootstrap sampling.
inBag - Variable in class hr.irb.fastRandomForest.VotesCollector
inBag - Variable in class hr.irb.fastRandomForest.VotesCollectorDataCache: NumTrees x numInstances indicating out-of-bag instances.
index - Variable in class ai.SplitFunction
InformationGainFunction - Class in ai
InformationGainFunction(int, Random) - Constructor for class ai.InformationGainFunction: Construct an information gain split function (it needs to be initialize with the corresponding data and indices)
InformationStatistics - Class in trainableSegmentation.metrics
InformationStatistics(double, double, double, double, double) - Constructor for class trainableSegmentation.metrics.InformationStatistics: Create information statistics
init(Instances, ArrayList<Integer>) - Method in class ai.GiniFunction: Create split function based on Gini coefficient
init(Instances, ArrayList<Integer>) - Method in class ai.InformationGainFunction: Initialize the function given a specific data set
init(Instances, ArrayList<Integer>) - Method in class ai.SplitFunction
insertImage(ImagePlus, ImagePlus, int[]) - Static method in class trainableSegmentation.utils.Utils: Insert an image into another one (2D or 3D).
instanceIdx - Variable in class ai.VotesCollector
instanceIdx - Variable in class hr.irb.fastRandomForest.VotesCollector
instanceIdx - Variable in class hr.irb.fastRandomForest.VotesCollectorDataCache
instClassValues - Variable in class hr.irb.fastRandomForest.DataCache: The class an instance belongs to.
instWeights - Variable in class hr.irb.fastRandomForest.DataCache: Weights of instances.
isAttrNominal(int) - Method in class hr.irb.fastRandomForest.DataCache: Is an attribute with the given index nominal?
isAvailable() - Static method in class trainableSegmentation.ImageScience: Tests whether the ImageScience library is installed.
isEmpty() - Method in class trainableSegmentation.FeatureStack: Check if the stack has been initialized or not
isEmpty() - Method in class trainableSegmentation.FeatureStackArray: Check if the array has not been yet initialized
isOldColorFormat() - Method in class trainableSegmentation.FeatureStack: Check if the feature stack is using the old Hessian format (previous to TWS 3.2.25).
isOldColorFormat() - Method in class trainableSegmentation.FeatureStackArray: Check if the old color format is used.
isOldHessianFormat() - Method in class trainableSegmentation.FeatureStack: Check if the feature stack is using the old Hessian format (previous to TWS version 3.2.25).
isOldoldHessianFormatFormat() - Method in class trainableSegmentation.FeatureStackArray: Check if the old Hessian format is used.
isProcessing3D() - Method in class trainableSegmentation.WekaSegmentation: Check the use of 3D or 2D features
isRGB() - Method in class trainableSegmentation.FeatureStackArray: Return if image stacks are RGB or grayscale.
isValueMissing(int, int) - Method in class hr.irb.fastRandomForest.DataCache: Does the given attribute - instance combination contain a missing value?

K

kH - Variable in class trainableSegmentation.filters.Kuwahara
KUWAHARA - Static variable in class trainableSegmentation.FeatureStack: Kuwahara filter flag index
Kuwahara - Class in trainableSegmentation.filters: A version of the Kuwahara filter that uses linear kernels rather than square ones.
Kuwahara() - Constructor for class trainableSegmentation.filters.Kuwahara
kW - Variable in class trainableSegmentation.filters.Kuwahara

L

LAPLACIAN - Static variable in class trainableSegmentation.FeatureStack: Laplacian filter flag index
LAPLACIAN - Static variable in class trainableSegmentation.FeatureStack3D: Laplacian filter flag index
LAUNCH_WEKA - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to launch the Weka Chooser
launchWeka() - Static method in class trainableSegmentation.Weka_Segmentation: Call the Weka chooser
LIPSCHITZ - Static variable in class trainableSegmentation.FeatureStack: Lipschitz filter flag index
Lipschitz2D(ImageProcessor) - Method in class trainableSegmentation.filters.Lipschitz_
Lipschitz_ - Class in trainableSegmentation.filters
Lipschitz_() - Constructor for class trainableSegmentation.filters.Lipschitz_
listOptions() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns an enumeration describing the available options.
LOAD_CLASSIFIER - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to load a classifier from file
LOAD_DATA - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to load data from an ARFF file
loadClassifier() - Method in class trainableSegmentation.Weka_Segmentation: Load a Weka classifier from a file
loadClassifier(String) - Static method in class trainableSegmentation.Weka_Segmentation: Load a new classifier
loadClassifier(InputStream) - Method in class trainableSegmentation.WekaSegmentation: load a binary classifier from a stream
loadClassifier(String) - Method in class trainableSegmentation.WekaSegmentation: Read header classifier from a .model file
loadClusterer() - Method in class trainableSegmentation.unsupervised.Color_Clustering: Load clusterer from a file and set selected channels
loadClusterer(String) - Method in class trainableSegmentation.unsupervised.ColorClustering: Load clusterer from path
loadData(String) - Static method in class trainableSegmentation.Weka_Segmentation: Load training data from file
loadNewImage(ImagePlus) - Method in class trainableSegmentation.WekaSegmentation: Load a new image to segment (no GUI)
loadTrainingData() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Load previously saved data
loadTrainingData(String) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Load training data (no GUI)
loadTrainingData() - Method in class trainableSegmentation.Weka_Segmentation: Load previously saved data
loadTrainingData(String) - Method in class trainableSegmentation.WekaSegmentation: Load training data

M

m_bagger - Variable in class hr.irb.fastRandomForest.FastRandomForest: The bagger.
m_Classifiers - Variable in class hr.irb.fastRandomForest.VotesCollector
m_Classifiers - Variable in class hr.irb.fastRandomForest.VotesCollectorDataCache
m_imp - Variable in class trainableSegmentation.filters.Lipschitz_
m_Info - Variable in class hr.irb.fastRandomForest.FastRandomForest: The header information.
m_KValue - Variable in class hr.irb.fastRandomForest.FastRandomForest: Final number of features that were considered in last build.
m_MaxDepth - Variable in class hr.irb.fastRandomForest.FastRandomForest: The maximum depth of the trees (0 = unlimited)
m_numFeatures - Variable in class hr.irb.fastRandomForest.FastRandomForest: Number of features to consider in random feature selection.
m_NumThreads - Variable in class hr.irb.fastRandomForest.FastRandomForest: Number of simultaneous threads to use in computation (0 = autodetect).
m_numTrees - Variable in class hr.irb.fastRandomForest.FastRandomForest: Number of trees in forest.
m_randomSeed - Variable in class hr.irb.fastRandomForest.FastRandomForest: The random seed.
m_roi - Variable in class trainableSegmentation.filters.Lipschitz_
m_Slope - Variable in class trainableSegmentation.filters.Lipschitz_
m_stack - Variable in class trainableSegmentation.filters.Lipschitz_
m_stack_out - Variable in class trainableSegmentation.filters.Lipschitz_
m_ZeroR - Variable in class hr.irb.fastRandomForest.FastRandomForest: a ZeroR model in case no model can be built from the data
main(String[]) - Static method in class ai.BalancedRandomForest: Main method for this class.
main(String[]) - Static method in class hr.irb.fastRandomForest.Benchmark
main(String[]) - Static method in class hr.irb.fastRandomForest.FastRandomForest: Main method for this class.
main(String[]) - Static method in class trainableSegmentation.metrics.PixelError: Main method for calculate the pixel error metrics from the command line
main(String[]) - Static method in class trainableSegmentation.metrics.RandError: Main method for calculating the Rand error metrics from the command line
main(String[]) - Static method in class trainableSegmentation.metrics.WarpingError: Main method for calcualte the warping error metrics from the command line
MAX_NUM_CLASSES - Static variable in class trainableSegmentation.WekaSegmentation: maximum number of classes (labels) allowed
maxDepthTipText() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the tip text for this property
MAXIMUM - Static variable in class trainableSegmentation.FeatureStack: Maximum filter flag index
MAXIMUM - Static variable in class trainableSegmentation.FeatureStack3D: Maximum flag index
maxPool(ImagePlus, ImagePlus, int, int) - Static method in class trainableSegmentation.utils.Utils: Experimental max pooling method.
maxPoolNoReduction(ImagePlus, ImagePlus, int, int) - Static method in class trainableSegmentation.utils.Utils: Experimental max pooling method without size reduction.
MEAN - Static variable in class trainableSegmentation.FeatureStack: Mean filter flag index
MEAN - Static variable in class trainableSegmentation.FeatureStack3D: Mean flag index
measureOutOfBagError() - Method in class ai.BalancedRandomForest: Gets the out of bag error that was calculated as the classifier was built.
measureOutOfBagError() - Method in class hr.irb.fastRandomForest.FastRandomForest: Gets the out of bag error that was calculated as the classifier was built.
MEDIAN - Static variable in class trainableSegmentation.FeatureStack: Median filter flag index
MEDIAN - Static variable in class trainableSegmentation.FeatureStack3D: Median flag index
MEMBRANE - Static variable in class trainableSegmentation.FeatureStack: Membrane filter flag index
MERGE - Static variable in class trainableSegmentation.metrics.WarpingError: merger flag
mergeDataInPlace(Instances, Instances) - Method in class trainableSegmentation.WekaSegmentation: Merge two datasets of Weka instances in place
Metrics - Class in trainableSegmentation.metrics: This is the mother class for 2D segmentation metrics
Metrics(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.Metrics
metricValue - Variable in class trainableSegmentation.metrics.ClassificationStatistics: value of the classification metric
MINIMUM - Static variable in class trainableSegmentation.FeatureStack: Minimum filter flag index
MINIMUM - Static variable in class trainableSegmentation.FeatureStack3D: Minimum flag index
mismatches - Variable in class trainableSegmentation.metrics.WarpingResults

N

nAngles - Variable in class trainableSegmentation.filters.Kuwahara
Neighborhood2D - Class in trainableSegmentation.utils
Neighborhood2D() - Constructor for class trainableSegmentation.utils.Neighborhood2D
Neighborhood2DC4 - Class in trainableSegmentation.utils
Neighborhood2DC4() - Constructor for class trainableSegmentation.utils.Neighborhood2DC4
Neighborhood2DC8 - Class in trainableSegmentation.utils
Neighborhood2DC8() - Constructor for class trainableSegmentation.utils.Neighborhood2DC8
NEIGHBORS - Static variable in class trainableSegmentation.FeatureStack: neighbors feature flag index
newInstance() - Method in class ai.GiniFunction
newInstance() - Method in class ai.InformationGainFunction
newInstance() - Method in class ai.SplitFunction
normalize(double[]) - Static method in class hr.irb.fastRandomForest.FastRfUtils: Normalizes the doubles in the array by their sum.
normalize(ImageStack) - Static method in class trainableSegmentation.utils.Utils: Normalize an image stack so it has 0 mean and unit variance
normalize01(FloatProcessor) - Static method in class trainableSegmentation.utils.Utils: Normalize float image so the pixel are between 0 and 1
numAttributes - Variable in class hr.irb.fastRandomForest.DataCache: Number of attributes, including the class attribute.
numChannels() - Static method in enum trainableSegmentation.unsupervised.ColorClustering.Channel: Returns total number of channels, static method.
numClasses - Variable in class hr.irb.fastRandomForest.DataCache: Number of classes.
numFeaturesTipText() - Method in class ai.BalancedRandomForest: Returns the tip text for this property
numFeaturesTipText() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the tip text for this property
numFolds - Static variable in class hr.irb.fastRandomForest.Benchmark
numInBag - Variable in class hr.irb.fastRandomForest.DataCache: How many instances are in 'bag' created by bootstrap sampling.
numInstances - Variable in class hr.irb.fastRandomForest.DataCache: Number of instances.
numOfHoleAdditions - Variable in class trainableSegmentation.metrics.ClusteredWarpingMismatches
numOfHoleDeletions - Variable in class trainableSegmentation.metrics.ClusteredWarpingMismatches
numOfMergers - Variable in class trainableSegmentation.metrics.ClusteredWarpingMismatches
numOfObjectAdditions - Variable in class trainableSegmentation.metrics.ClusteredWarpingMismatches
numOfObjectDeletions - Variable in class trainableSegmentation.metrics.ClusteredWarpingMismatches
numOfSplits - Variable in class trainableSegmentation.metrics.ClusteredWarpingMismatches
numRuns - Static variable in class hr.irb.fastRandomForest.Benchmark
numThreadsTipText() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the tip text for this property
numTreesTipText() - Method in class ai.BalancedRandomForest: Returns the tip text for this property
numTreesTipText() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the tip text for this property

O

OBJECT_ADDITION - Static variable in class trainableSegmentation.metrics.WarpingError: object addition error flag
OBJECT_DELETION - Static variable in class trainableSegmentation.metrics.WarpingError: object deletion error flag
originalImage - Variable in class trainableSegmentation.unsupervised.Color_Clustering: original image
originalLabels - Variable in class trainableSegmentation.metrics.Metrics: original labels (single 2D image or stack)

P

paint(Graphics, int, int, double) - Method in class trainableSegmentation.ImageOverlay
paint(Graphics, int, int, double) - Method in class trainableSegmentation.RoiListOverlay
PixelClustering - Class in trainableSegmentation.unsupervised: This class contains all the library methods to perform unsupervised learning on pixels based on the Weka clusterers.
PixelClustering() - Constructor for class trainableSegmentation.unsupervised.PixelClustering: Empty constructor
PixelClustering(Instances, AbstractClusterer) - Constructor for class trainableSegmentation.unsupervised.PixelClustering: Construct PixelClustering object with a specific set of instances and clustering model
PixelError - Class in trainableSegmentation.metrics: This class implements the pixel error metric
PixelError(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.PixelError: Initialize pixel error metric
PixelRecord - Class in trainableSegmentation.utils: Class to store integer pixel coordinates and its corresponding double value
PixelRecord(Cursor2D, double) - Constructor for class trainableSegmentation.utils.PixelRecord
PixelRecord(int, int, double) - Constructor for class trainableSegmentation.utils.PixelRecord
PLOT_RESULT - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to plot the threshold curves
plotPrecisionRecall(ArrayList<ClassificationStatistics>) - Static method in class trainableSegmentation.utils.Utils: Plot the precision-recall curve
plotResultGraphs() - Static method in class trainableSegmentation.Weka_Segmentation: Plot the current result (threshold curves)
plotROC(ArrayList<ClassificationStatistics>) - Static method in class trainableSegmentation.utils.Utils: Plot the Receiver operating characteristic curve
PLUGIN_NAME - Static variable in class trainableSegmentation.Weka_Segmentation: plugin's name
PLUGIN_VERSION - Static variable in class trainableSegmentation.Weka_Segmentation: plugin's current version
postProcess(FloatProcessor, int, double, int, boolean) - Static method in class trainableSegmentation.utils.Utils: Post-process probability image to get more reasonable objects at a certain threshold
precision - Variable in class trainableSegmentation.metrics.ClassificationStatistics: precision: true positives / ( true positives + false positives )
precision - Variable in class trainableSegmentation.metrics.InformationStatistics: precision: C(T|S) = ( H(T) - H(T|S) ) / H(T)
precisionRecallStats(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels.
precisionRecallStats(ImageProcessor, ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.PixelError: Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels.
PrincipalComponentAnalysis - Class in trainableSegmentation.utils: This class implements methods to calculate the principal components of an image.
proposedLabels - Variable in class trainableSegmentation.metrics.Metrics: proposed new labels (single 2D image or stack of the same as as the original labels)

R

RandError - Class in trainableSegmentation.metrics: This class implements the Rand error metric.
RandError(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.RandError: Initialize Rand error metric
randError(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the standard Rand error between some 2D original labels and the corresponding proposed labels.
randIndexStats(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.RandError: Calculate the standard Rand index between some 2D original labels and the corresponding proposed labels.
randomPermutation(int, Random) - Static method in class hr.irb.fastRandomForest.FastRfUtils: Produces a random permutation using Knuth shuffle.
readClassifier(String) - Static method in class trainableSegmentation.Weka_Segmentation: Load a Weka model (classifier) from a file
readDataFromARFF(String) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Read ARFF file
readDataFromARFF(String) - Method in class trainableSegmentation.WekaSegmentation: Read ARFF file
readInstances(String) - Static method in class hr.irb.fastRandomForest.FastRfUtils: Load a dataset into memory.
recall - Variable in class trainableSegmentation.metrics.ClassificationStatistics: recall (also called sensitivity of hit rate): true positives / ( true positives + false negatives )
recall - Variable in class trainableSegmentation.metrics.InformationStatistics: recall C(S|T) = ( H(S) - H(S|T) ) / H(S)
record(String, String...) - Static method in class trainableSegmentation.Weka_Segmentation: Macro-record a specific command.
removeFeature(String) - Method in class trainableSegmentation.FeatureStack: Remove feature from stack
reorderFeatures(Instances) - Method in class trainableSegmentation.FeatureStack: Reorder the feature stack to match the order given by a set of instances.
reorderFeatures(Instances) - Method in class trainableSegmentation.FeatureStackArray: Reorder the features of each stack based on the order of attributes given by a set of instances.
resample(int, Random) - Method in class hr.irb.fastRandomForest.DataCache: Uses sampling with replacement to create a new DataCache from an existing one.
resetReference() - Method in class trainableSegmentation.FeatureStackArray: Reset the reference index (used when the are changes in the features)
ReusableDenseInstance - Class in trainableSegmentation: This class extends the WEKA DenseInstance so it can be reused.
ReusableDenseInstance(double, double[]) - Constructor for class trainableSegmentation.ReusableDenseInstance: Construct reusable dense instance
reusableRandomGenerator - Variable in class hr.irb.fastRandomForest.DataCache: Used in training of FastRandomTrees.
RoiListOverlay - Class in trainableSegmentation: This class implements an overlay based on the image ROI.
RoiListOverlay() - Constructor for class trainableSegmentation.RoiListOverlay: Empty constructor
RoiListOverlay(ArrayList<Roi>, Composite, Color) - Constructor for class trainableSegmentation.RoiListOverlay: Create a RoiOverlay based on a Roi and a specific color and composite mode.
run(ImageProcessor) - Method in class trainableSegmentation.filters.Entropy_Filter: Main method when called as a plugin filter
run(ImageProcessor) - Method in class trainableSegmentation.filters.Kuwahara: Run method for plugin filter
run(ImageProcessor) - Method in class trainableSegmentation.filters.Lipschitz_
run(String) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Plugin run method
run(String) - Method in class trainableSegmentation.unsupervised.Color_Clustering: Run function for plug-in
run(String) - Method in class trainableSegmentation.Weka_Segmentation: Plugin run method
runLipschitz(ImageProcessor) - Method in class trainableSegmentation.filters.Lipschitz_

S

SAVE_CLASSIFIER - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to save the current classifier into a file
SAVE_DATA - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to save the current data into an ARFF file
SAVE_FEATURE_STACK - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to save the feature stack into a file or files
saveClassifier() - Method in class trainableSegmentation.Weka_Segmentation: Save current classifier into a file
saveClassifier(AbstractClassifier, Instances, String) - Static method in class trainableSegmentation.Weka_Segmentation: Write classifier into a file
saveClassifier(String) - Static method in class trainableSegmentation.Weka_Segmentation: Save current classifier into a file
saveClassifier(String) - Method in class trainableSegmentation.WekaSegmentation: Write current classifier into a file
saveClassifier(OutputStream) - Method in class trainableSegmentation.WekaSegmentation: Save classifier into OutputStream
saveClusterer() - Method in class trainableSegmentation.unsupervised.Color_Clustering: Save the clusterer to a file
saveClusterer(String) - Method in class trainableSegmentation.unsupervised.ColorClustering: Save clusterer to a file
saveData(String) - Static method in class trainableSegmentation.Weka_Segmentation: Save training data into an ARFF file
saveData(String) - Method in class trainableSegmentation.WekaSegmentation: Save training data into a file (.arff)
saveFeatureStack(String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Save current feature stack(s)
saveFeatureStack(int, String, String) - Method in class trainableSegmentation.WekaSegmentation: Save specific slice feature stack
saveStackAsTiff(String) - Method in class trainableSegmentation.FeatureStack: Save current whole stack as TIFF
saveTrainingData() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Save training model into a file
saveTrainingData() - Method in class trainableSegmentation.Weka_Segmentation: Save training model into a file
scramble(Instances, Instances, int, int[]) - Static method in class hr.irb.fastRandomForest.FastRfUtils: Produces a random permutation of the values of an attribute in a dataset using Knuth shuffle.
scrambleOneAttribute(int, Random) - Method in class hr.irb.fastRandomForest.DataCache: Randomizes one attribute in the vals[][]; returns a copy of the vals[] before randomization.
seedTipText() - Method in class ai.BalancedRandomForest: Returns the tip text for this property
seedTipText() - Method in class hr.irb.fastRandomForest.FastRandomForest: Returns the tip text for this property
selectAttributes() - Method in class trainableSegmentation.WekaSegmentation: Select attributes of current data by BestFirst search.
selectAttributes(Instances) - Static method in class trainableSegmentation.WekaSegmentation: Select attributes using BestFirst search to reduce the number of parameters per instance of a dataset
set(FeatureStack, int) - Method in class trainableSegmentation.FeatureStackArray: Set a member of the list
set(int, int) - Method in class trainableSegmentation.utils.Cursor2D
SET_BALANCE - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to enable/disable the class balance
SET_CLASSIFIER - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to set a new classifier
SET_FEATURE - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to enable/disable a feature
SET_HOMOGENIZATION - Static variable in class trainableSegmentation.Weka_Segmentation: Deprecated.
use SET_BALANCE instead
SET_MAXIMUM_SIGMA - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to set the maximum kernel radius
SET_MEMBRANE_PATCH - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to set the membrane patch size
SET_MEMBRANE_THICKNESS - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to set the membrane thickness
SET_MINIMUM_SIGMA - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to set the minimum kernel radius
SET_OPACITY - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to set the overlay opacity
setChannels(ArrayList<ColorClustering.Channel>) - Method in class trainableSegmentation.unsupervised.ColorClustering: Set channels to use
setClassBalance(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set the class balance flag for training
setClassBalance(boolean) - Method in class trainableSegmentation.WekaSegmentation: Set flag to balance classes before training
setClassHomogenization(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set the class homogenization flag for training
setClassifier(String, String) - Static method in class trainableSegmentation.Weka_Segmentation: Set classifier for current segmentation
setClassifier(AbstractClassifier) - Method in class trainableSegmentation.WekaSegmentation: Set current classifier
setClassLabel(int, String) - Method in class trainableSegmentation.WekaSegmentation: Set the name of a class.
setClassLabels(String[]) - Method in class trainableSegmentation.WekaSegmentation: Set the labels for each class
setColor(Color) - Method in class trainableSegmentation.RoiListOverlay: Set the color of the ROIs
setComposite(Composite) - Method in class trainableSegmentation.ImageOverlay: Set composite mode
setComposite(Composite) - Method in class trainableSegmentation.RoiListOverlay: Set the composite to be used in the overlay.
setComputeImportances(boolean) - Method in class hr.irb.fastRandomForest.FastRandomForest
setConnectivity(int) - Method in class trainableSegmentation.utils.WatershedTransform2D: Set the pixel connectivity (4 or 8)
setCriterionMethod(int) - Method in class trainableSegmentation.filters.Kuwahara: Set the criterion method
setCursor(Cursor2D) - Method in class trainableSegmentation.utils.Neighborhood2D
setDoClassBalance(boolean) - Method in class trainableSegmentation.WekaSegmentation: Set the flag to balance the class distributions
setDoHomogenizeClasses(boolean) - Method in class trainableSegmentation.WekaSegmentation: Deprecated.
use setDoClassBalance
setDownHat(boolean) - Method in class trainableSegmentation.filters.Lipschitz_
setEnabledFeature(String, boolean) - Method in class trainableSegmentation.FeatureStack: Enable/disable feature by name
setEnabledFeatures(boolean[]) - Method in class trainableSegmentation.FeatureStack: Set list of boolean flags for enabled features
setEnabledFeatures(boolean[]) - Method in class trainableSegmentation.FeatureStackArray: Set the features enabled for the reference stack
setEnabledFeatures(boolean[]) - Method in class trainableSegmentation.WekaSegmentation: Set the new enabled features
setEnableFeatures(boolean[]) - Method in class trainableSegmentation.FeatureStack3D: Set the boolean flags to enable/disable features
setFeature(String) - Static method in class trainableSegmentation.Weka_Segmentation: Enable/disable a single feature of the feature stack(s)
setFeatures(ArrayList<String>) - Method in class trainableSegmentation.WekaSegmentation: Set features to use during training
setFeaturesDirty() - Method in class trainableSegmentation.WekaSegmentation: Forces the feature stack to be updated whenever it is needed next.
setFeaturesInstances(Instances) - Method in class trainableSegmentation.unsupervised.ColorClustering: Set features instances that are to be used
setFeaturesInstances(Instances) - Method in class trainableSegmentation.unsupervised.PixelClustering: Set feature instances to be used
setFeatureStackArray(FeatureStackArray) - Method in class trainableSegmentation.unsupervised.ColorClustering: Set FeatureStackArray that is to be used
setFeatureStackArray(FeatureStackArray) - Method in class trainableSegmentation.WekaSegmentation: Assign an arbitrary filter stack array
setFrfProto(FastRandomForest) - Method in class hr.irb.fastRandomForest.FRFAttributeEval
setHomogenizeClasses(boolean) - Method in class trainableSegmentation.WekaSegmentation: Deprecated.
use setClassBalance
setImage(ImageProcessor) - Method in class trainableSegmentation.ImageOverlay: Set image processor to be painted in the overlay
setImage(ImagePlus) - Method in class trainableSegmentation.unsupervised.ColorClustering: Set image that is to be used
setInstance(int, int, ReusableDenseInstance, double[]) - Method in class trainableSegmentation.FeatureStack: without class parameter
setInstance(int, int, int, ReusableDenseInstance, double[]) - Method in class trainableSegmentation.FeatureStack: Set values to an instance (feature vector) of a specific coordinate.
setLoadedClassNames(ArrayList<String>) - Method in class trainableSegmentation.WekaSegmentation: Set the list of loaded class names
setLoadedTrainingData(Instances) - Method in class trainableSegmentation.WekaSegmentation: Set pre-loaded training data (not from the user traces)
setMaxDepth(int) - Method in class hr.irb.fastRandomForest.FastRandomForest: Set the maximum depth of the tree, 0 for unlimited.
setMaximumSigma(float) - Method in class trainableSegmentation.FeatureStack: Set the maximum sigma to use in the filters
setMaximumSigma(float) - Method in class trainableSegmentation.FeatureStack3D: Set the maximum sigma (or filter radius) of the features
setMaximumSigma(float) - Method in class trainableSegmentation.FeatureStackArray: Set maximum sigma.
setMaximumSigma(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set a new maximum radius for the feature filters
setMaximumSigma(float) - Method in class trainableSegmentation.WekaSegmentation: Set the maximum sigma/radius to use in the features
setMembranePatchSize(int) - Method in class trainableSegmentation.FeatureStack: Set the membrane patch size (it must be an odd number)
setMembranePatchSize(int) - Method in class trainableSegmentation.FeatureStackArray: Set membrane patch size.
setMembranePatchSize(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set a new membrane patch size for current feature stack
setMembranePatchSize(int) - Method in class trainableSegmentation.WekaSegmentation: Set the membrane patch size (it must be an odd number)
setMembraneSize(int) - Method in class trainableSegmentation.FeatureStack: Set the expected membrane thickness
setMembraneSize(int) - Method in class trainableSegmentation.FeatureStackArray: Set expected membrane thickness
setMembraneThickness(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set membrane thickness for current feature stack
setMembraneThickness(int) - Method in class trainableSegmentation.WekaSegmentation: Set expected membrane thickness (this will be the width of the center column of the membrane filter)
setMinimumSigma(float) - Method in class trainableSegmentation.FeatureStack: Set the minimum sigma to use in the filters
setMinimumSigma(float) - Method in class trainableSegmentation.FeatureStack3D: Set the minimum sigma (or filter radius) of the features
setMinimumSigma(float) - Method in class trainableSegmentation.FeatureStackArray: Set minimum sigma.
setMinimumSigma(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set a new minimum radius for the feature filters
setMinimumSigma(float) - Method in class trainableSegmentation.WekaSegmentation: Set the minimum sigma (radius) to use in the features
setNumberOfAngles(int) - Method in class trainableSegmentation.filters.Kuwahara
setNumFeatures(int) - Method in class ai.BalancedRandomForest: Set the number of random features to use in each node of the random trees
setNumFeatures(int) - Method in class hr.irb.fastRandomForest.FastRandomForest: Set the number of features to use in random selection.
setNumOfClasses(int) - Method in class trainableSegmentation.WekaSegmentation: Set the current number of classes.
setNumSamples(int) - Method in class trainableSegmentation.unsupervised.ColorClustering: Set number of samples that is to be used
setNumThreads(int) - Method in class hr.irb.fastRandomForest.FastRandomForest: Set the number of simultaneous threads used in training, 0 for autodetect.
setNumTrees(int) - Method in class ai.BalancedRandomForest: Set the number of trees in the forest
setNumTrees(int) - Method in class hr.irb.fastRandomForest.FastRandomForest: Set the value of numTrees.
setOldColorFormat(boolean) - Method in class trainableSegmentation.FeatureStack: Set the use of old color format.
setOldColorFormat(boolean) - Method in class trainableSegmentation.FeatureStackArray: Set the use of the old color format.
setOldHessianFormat(boolean) - Method in class trainableSegmentation.FeatureStack: Set the use of old Hessian format.
setOldHessianFormat(boolean) - Method in class trainableSegmentation.FeatureStackArray: Specify the use of the old (wrong) Hessian format (fixed in version 3.2.25 of TWS)
setOpacity(String) - Static method in class trainableSegmentation.Weka_Segmentation: Set overlay opacity
setOptions(String[]) - Method in class ai.BalancedRandomForest: Parses a given list of options.
setOptions(String[]) - Method in class hr.irb.fastRandomForest.FastRandomForest: Parses a given list of options.
setProjectFolder(String) - Method in class trainableSegmentation.WekaSegmentation: Set the project folder
setReference(int) - Method in class trainableSegmentation.FeatureStackArray: Set the reference index (used when the are changes in the features).
setRoi(ArrayList<Roi>) - Method in class trainableSegmentation.RoiListOverlay: Set the list of ROIs.
setSeed(int) - Method in class ai.BalancedRandomForest: Set the seed used to initialize the random number generators
setSeed(int) - Method in class hr.irb.fastRandomForest.FastRandomForest: Set the seed for random number generation.
setSelectedClusterer(AbstractClusterer) - Method in class trainableSegmentation.unsupervised.PixelClustering: Set selected clusterer
setSize(int) - Method in class trainableSegmentation.filters.Kuwahara
setStack(ImageStack) - Method in class trainableSegmentation.FeatureStack: Set an arbitrary stack as feature stack.
setTheClusterer(AbstractClusterer) - Method in class trainableSegmentation.unsupervised.ColorClustering: Set abstract clusterer to be used
setTopHat(boolean) - Method in class trainableSegmentation.filters.Lipschitz_
setTrainHeader(Instances) - Method in class trainableSegmentation.WekaSegmentation: Set current training header, and attempt to adjust segmentation state to it.
setTrainingImage(ImagePlus) - Method in class trainableSegmentation.WekaSegmentation: Set the training image (single image or stack)
setup(String, ImagePlus) - Method in class trainableSegmentation.filters.Entropy_Filter: Setup method
setup(String, ImagePlus) - Method in class trainableSegmentation.filters.Kuwahara
setup(String, ImagePlus) - Method in class trainableSegmentation.filters.Lipschitz_
setUpdateFeatures(boolean) - Method in class trainableSegmentation.WekaSegmentation: Set feature update flag
setUseNeighbors(boolean) - Method in class trainableSegmentation.FeatureStack: Set the use of the neighbors as features
setUseNeighbors(boolean) - Method in class trainableSegmentation.FeatureStackArray: Set the use of neighbor features.
setUseNeighbors(boolean) - Method in class trainableSegmentation.WekaSegmentation: Set the use of the neighbor features
setValues(double, double[]) - Method in class trainableSegmentation.ReusableDenseInstance: Set instance values
setVerbose(boolean) - Method in class trainableSegmentation.utils.WatershedTransform2D: Set verbose flag
setVerboseMode(boolean) - Method in class trainableSegmentation.metrics.Metrics: Set verbose mode
setVerboseMode(boolean) - Method in class trainableSegmentation.metrics.RandError: Set verbose mode
show() - Method in class trainableSegmentation.FeatureStack: Display feature stack
showKernels - Variable in class trainableSegmentation.filters.Kuwahara
showSettingsDialog() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Show advanced settings dialog
showSettingsDialog() - Method in class trainableSegmentation.Weka_Segmentation: Show advanced settings dialog
shutDownNow() - Method in class trainableSegmentation.FeatureStack: Shut down the executor service
shutDownNow() - Method in class trainableSegmentation.FeatureStackArray: Shut down the executor service
shutDownNow() - Method in class trainableSegmentation.WekaSegmentation: Shut down the executor service for training and feature creation
simple2D(ImagePlus, int) - Method in class trainableSegmentation.metrics.WarpingError: Check if a point is simple (in 2D)
simple2DBertrand(ImagePlus, int) - Method in class trainableSegmentation.metrics.WarpingError: Check if a point is simple (in 2D) based on 3D code from Mark Richardson inspired in the work of Bertrand et al.
SIMPLE_POINT_THRESHOLD - Static variable in class trainableSegmentation.metrics.WarpingError: simple point threshold value
simplePointWarp2d(ImageProcessor, ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.WarpingError: Use simple point relaxation to warp 2D source into 2D target.
simplePointWarp2d(ImagePlus, ImagePlus, ImagePlus, double) - Method in class trainableSegmentation.metrics.WarpingError: Use simple point relaxation to warp 2D source into 2D target.
simplePointWarp2DConcurrent(ImageProcessor, ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the simple point warping in a concurrent way (to be submitted to an Executor Service)
simplePointWarp2DConcurrent(ImageProcessor, ImageProcessor, ImageProcessor, double, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Calculate the simple point warping in a concurrent way (to be submitted to an Executor Service)
simplePointWarp2dMT(ImagePlus, ImagePlus, ImagePlus, double, ArrayList<Point3f>[]) - Method in class trainableSegmentation.metrics.WarpingError: Use simple point relaxation to warp 2D source into 2D target.
simplePointWarp2dMT(ImagePlus, ImagePlus, ImagePlus, double) - Method in class trainableSegmentation.metrics.WarpingError: Use simple point relaxation to warp 2D source into 2D target.
simplePointWarp2dMT(double, boolean, boolean, int) - Method in class trainableSegmentation.metrics.WarpingError: Use simple point relaxation to warp 2D labels into the 2D proposal.
size - Variable in class trainableSegmentation.filters.Kuwahara
smooth(FloatProcessor, float, float, float) - Static method in class trainableSegmentation.utils.Utils: Blurs probability image with a given symmetrically weighted kernel.
SOBEL - Static variable in class trainableSegmentation.FeatureStack: Sobel filter flag index
sort(float[]) - Static method in class hr.irb.fastRandomForest.FastRfUtils: Sorts a given array of floats in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sortedIndices - Variable in class hr.irb.fastRandomForest.DataCache: Ordering of instances, indexed by attribute, then by instance.
specificity - Variable in class trainableSegmentation.metrics.ClassificationStatistics: specificity, also called true negative rate (TNR): true negatives / (true negatives + false negatives)
SPLIT - Static variable in class trainableSegmentation.metrics.WarpingError: split flag
SplitCriteria - Class in hr.irb.fastRandomForest: Functions used for finding best splits in FastRfTree.
SplitFunction - Class in ai
SplitFunction() - Constructor for class ai.SplitFunction
Splitter - Class in ai
Splitter(SplitFunction) - Constructor for class ai.Splitter: Construct a split function producer
STRUCTURE - Static variable in class trainableSegmentation.FeatureStack: structure tensor filter flag index
STRUCTURE - Static variable in class trainableSegmentation.FeatureStack3D: structure tensor filter flag index

T

threshold - Variable in class ai.SplitFunction
threshold(ImageProcessor, double) - Static method in class trainableSegmentation.utils.Utils: Apply binary threshold to input image
TOGGLE_OVERLAY - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to toggle the overlay image
toggleOverlay() - Static method in class trainableSegmentation.Weka_Segmentation: Toggle current result overlay image
topo(ImagePlus, int) - Method in class trainableSegmentation.metrics.WarpingError: Computes topological numbers for the central point of an image patch.
toString() - Method in class ai.BalancedRandomForest: Outputs a description of this classifier.
toString() - Method in class hr.irb.fastRandomForest.FastRandomForest: Outputs a description of this classifier.
toString() - Method in class trainableSegmentation.RoiListOverlay: Convert the list of ROIs to text.
toString() - Method in enum trainableSegmentation.unsupervised.ColorClustering.Channel: Turn channel to string.
TRAIN_CLASSIFIER - Static variable in class trainableSegmentation.Weka_Segmentation: name of the macro method to train the current classifier
Trainable_Segmentation - Class in trainableSegmentation: Deprecated.
Trainable_Segmentation() - Constructor for class trainableSegmentation.Trainable_Segmentation: Deprecated.

Basic constructor
Trainable_Segmentation(ImagePlus) - Constructor for class trainableSegmentation.Trainable_Segmentation: Deprecated.

No GUI constructor
trainableSegmentation - package trainableSegmentation
trainableSegmentation.filters - package trainableSegmentation.filters
trainableSegmentation.metrics - package trainableSegmentation.metrics
trainableSegmentation.unsupervised - package trainableSegmentation.unsupervised
trainableSegmentation.utils - package trainableSegmentation.utils
trainClassifier() - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Train classifier with the current instances
trainClassifier() - Static method in class trainableSegmentation.Weka_Segmentation: Train the current classifier
trainClassifier() - Method in class trainableSegmentation.WekaSegmentation: Train classifier with the current instances
tree - Variable in class ai.VotesCollector
trueNegatives - Variable in class trainableSegmentation.metrics.ClassificationStatistics: number of true negatives
truePositives - Variable in class trainableSegmentation.metrics.ClassificationStatistics: number of true positives

U

udpateDataClassification(ImagePlus, String, String) - Method in class trainableSegmentation.WekaSegmentation: Update the class attribute of "loadedTrainingData" from the input binary labels.
updateClassifiedImage(ImagePlus) - Method in class trainableSegmentation.Weka_Segmentation: Update the result image
updateClassifier(int, int, int) - Method in class trainableSegmentation.WekaSegmentation: Update fast random forest classifier with new values
updateDataClassification(Instances, ImagePlus, int, int) - Static method in class trainableSegmentation.WekaSegmentation: Update the class attribute of "data" from the input binary labels.
updateDataClassification(Instances, ImagePlus, int, int, ArrayList<Point3f>[]) - Static method in class trainableSegmentation.WekaSegmentation: Update the class attribute of "data" from the input binary labels.
updateFeatures() - Method in class trainableSegmentation.FeatureStack: Deprecated.
updateFeatures(FeatureStack) - Method in class trainableSegmentation.FeatureStackArray: Update features of a feature stack (to be submitted to an Executor Service)
updateFeaturesMT() - Method in class trainableSegmentation.FeatureStack: Update features with current list in a multi-thread fashion.
updateFeaturesMT(int) - Method in class trainableSegmentation.FeatureStack: Update features with current list in a multi-thread fashion.
updateFeaturesMT() - Method in class trainableSegmentation.FeatureStack3D: Update features with current list in a multi-thread fashion
updateFeaturesMT(boolean[]) - Method in class trainableSegmentation.FeatureStackArray: Update specific feature stacks in the list (multi-thread fashion)
updateFeaturesMT() - Method in class trainableSegmentation.FeatureStackArray: Update all feature stacks in the list (multi-thread fashion)
updateFeaturesST() - Method in class trainableSegmentation.FeatureStack: Update features with current list in a single-thread fashion
updateResultOverlay() - Method in class trainableSegmentation.Weka_Segmentation: Update the result image overlay with the corresponding slice
updateWholeImageData() - Method in class trainableSegmentation.WekaSegmentation: Create the whole image data (instances) from the current image and feature stack.
useAllFeatures() - Method in class trainableSegmentation.WekaSegmentation: Force segmentator to use all available features
useNeighborhood() - Method in class trainableSegmentation.FeatureStack: Check the use of the neighbors as features
useNeighborhood() - Method in class trainableSegmentation.FeatureStackArray: Check if neighbor features are used.
Utils - Class in trainableSegmentation.utils: This class implements useful methods for the Weka Segmentation library.

V

vals - Variable in class hr.irb.fastRandomForest.DataCache: The dataset, first indexed by attribute, then by instance.
valueOf(String) - Static method in enum trainableSegmentation.unsupervised.ColorClustering.Channel: Returns the enum constant of this type with the specified name.
values() - Static method in enum trainableSegmentation.unsupervised.ColorClustering.Channel: Returns an array containing the constants of this enum type, in the order they are declared.
VARIANCE - Static variable in class trainableSegmentation.FeatureStack: Variance filter flag index
VARIANCE - Static variable in class trainableSegmentation.FeatureStack3D: Variance flag index
VARIANCE - Static variable in class trainableSegmentation.filters.Kuwahara
VARIANCE_DIV_MEAN - Static variable in class trainableSegmentation.filters.Kuwahara
VARIANCE_DIV_MEAN_SQUARE - Static variable in class trainableSegmentation.filters.Kuwahara
VariationOfInformation - Class in trainableSegmentation.metrics: This class implements the variation of information metric, used to compare clusters or segmentations.
VariationOfInformation(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.VariationOfInformation: Initialize variation of information metric
variationOfInformationN2(ImageProcessor, ImageProcessor, double) - Method in class trainableSegmentation.metrics.VariationOfInformation: Calculate variation of information with N^2 normalization
verbose - Variable in class trainableSegmentation.metrics.Metrics: boolean flag to set the level of detail on the standard output messages
verbose - Variable in class trainableSegmentation.utils.WatershedTransform2D: flag to output the transform steps and their execution times in the log window
vi - Variable in class trainableSegmentation.metrics.InformationStatistics: value of the variation of information
VotesCollector - Class in ai: Used to retrieve the out-of-bag vote of an ensemble classifier for a single instance.
VotesCollector(BalancedRandomTree[], int, Instances, boolean[][]) - Constructor for class ai.VotesCollector
VotesCollector - Class in hr.irb.fastRandomForest: Used to retrieve the out-of-bag vote of an ensemble classifier for a single instance.
VotesCollector(Classifier[], int, Instances, boolean[][]) - Constructor for class hr.irb.fastRandomForest.VotesCollector
VotesCollectorDataCache - Class in hr.irb.fastRandomForest: Used to retrieve the out-of-bag vote of an FastRandomForest classifier for a single instance from a DataCache the forest was trained on.
VotesCollectorDataCache(Classifier[], int, DataCache, boolean[][]) - Constructor for class hr.irb.fastRandomForest.VotesCollectorDataCache

W

warpedSource - Variable in class trainableSegmentation.metrics.WarpingResults: warped source image after 2D simple point relaxation
WarpingError - Class in trainableSegmentation.metrics: This class implements the warping error metric \cite{Jain10} BibTeX:
WarpingError(ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.WarpingError: Initialize warping error metric
WarpingError(ImagePlus, ImagePlus, ImagePlus) - Constructor for class trainableSegmentation.metrics.WarpingError: Initialize warping error metric
WarpingError(ImagePlus, ImagePlus, ImagePlus, int) - Constructor for class trainableSegmentation.metrics.WarpingError: Initialize warping error metric
warpingError - Variable in class trainableSegmentation.metrics.WarpingResults: warping error
warpingErrorSingleThread(ImagePlus, ImagePlus, ImagePlus, double) - Method in class trainableSegmentation.metrics.WarpingError: Calculate warping error (single thread version)
WarpingResults - Class in trainableSegmentation.metrics: Results from simple point warping (2D)
WarpingResults() - Constructor for class trainableSegmentation.metrics.WarpingResults
WatershedTransform2D - Class in trainableSegmentation.utils: Class to apply the classic watershed algorithm [1] to a 2D image.
WatershedTransform2D(ImageProcessor) - Constructor for class trainableSegmentation.utils.WatershedTransform2D: Construct a watershed transform
WatershedTransform2D(ImageProcessor, int) - Constructor for class trainableSegmentation.utils.WatershedTransform2D: Construct a watershed transform
Weka_Segmentation - Class in trainableSegmentation: Segmentation plugin based on the machine learning library Weka
Weka_Segmentation() - Constructor for class trainableSegmentation.Weka_Segmentation: Basic constructor for graphical user interface use
WekaSegmentation - Class in trainableSegmentation: This class contains all the library methods to perform image segmentation based on the Weka classifiers.
WekaSegmentation(ImagePlus) - Constructor for class trainableSegmentation.WekaSegmentation: Default constructor.
WekaSegmentation() - Constructor for class trainableSegmentation.WekaSegmentation: No-image constructor.
WekaSegmentation(boolean) - Constructor for class trainableSegmentation.WekaSegmentation: Constructor for both 2D and 3D images.
whatGoesWhere - Variable in class hr.irb.fastRandomForest.DataCache: Used in training of FastRandomTrees.
writeClassifier(AbstractClassifier, String) - Static method in class trainableSegmentation.Weka_Segmentation: Write classifier into a file
writeConfigurationToFile(String) - Method in class trainableSegmentation.FeatureStack: Write feature names in a file
writeDataToARFF(Instances, String) - Method in class trainableSegmentation.Trainable_Segmentation: Deprecated.

Write current instances into an ARFF file
writeDataToARFF(Instances, String) - Method in class trainableSegmentation.WekaSegmentation: Write current instances into an ARFF file

A B C D E F G H I K L M N O P R S T U V W