ITK  4.1.0
Insight Segmentation and Registration Toolkit
Classes
Module ITKStatistics
Group Numerics
+ Collaboration diagram for Module ITKStatistics:

Classes

class  itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector
 Candidate Vector. More...
class  itk::Statistics::ChiSquareDistribution
 ChiSquareDistribution class defines the interface for a univariate Chi-Square distribution (pdfs, cdfs, etc.). More...
class  itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::ConstIterator
 class that walks through the elements of the histogram. More...
class  itk::Statistics::CovarianceSampleFilter< TSample >
 Calculates the covariance matrix of the target sample data. More...
class  itk::Statistics::DecisionRule
 Base class for decision rules that return a class label based on a set of discriminant scores. More...
class  itk::Statistics::DenseFrequencyContainer2
 This class is a container for frequencies of bins in an histogram. More...
class  itk::Statistics::DistanceMetric< TVector >
 this class declares common interfaces for distance functions. More...
class  itk::Statistics::DistanceToCentroidMembershipFunction< TVector >
 DistanceToCentroidMembershipFunction models class membership using a distance metric. More...
class  itk::Statistics::EuclideanDistanceMetric< TVector >
 Euclidean distance function. More...
class  itk::Statistics::EuclideanSquareDistanceMetric< TVector >
 Computes Euclidean distance between origin and given measurement vector. More...
class  itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample >
 This class generates the parameter estimates for a mixture model using expectation maximization strategy. More...
class  itk::Statistics::GaussianDistribution
 GaussianDistribution class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.). More...
class  itk::Statistics::GaussianMembershipFunction< TMeasurementVector >
 GaussianMembershipFunction models class membership through a multivariate Gaussian function. More...
class  itk::Statistics::GaussianMixtureModelComponent< TSample >
 is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More...
class  itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >
 This class stores measurement vectors in the context of n-dimensional histogram. More...
class  itk::HistogramToEntropyImageFilter< THistogram, TImage >
 The class takes a histogram as an input and gives the entropy image as the output. A pixel, at position I, in the output image is given by. More...
class  itk::HistogramToImageFilter< THistogram, TImage, TFunction >
 This class takes a histogram as an input and returns an image of type specified by the functor. More...
class  itk::HistogramToIntensityImageFilter< THistogram, TImage >
 The class takes a histogram as an input and produces an image as the output. A pixel, at position I, in the output image is given by. More...
class  itk::HistogramToLogProbabilityImageFilter< THistogram, TImage >
 The class takes a histogram as an input and gives the log probability image as the output. A pixel, at position I, in the output image is given by. More...
class  itk::HistogramToProbabilityImageFilter< THistogram, TImage >
 The class takes a histogram as an input and gives the probability image as the output. A pixel, at position I, in the output image is given by. More...
class  itk::Statistics::HistogramToRunLengthFeaturesFilter< THistogram >
 This class computes texture feature coefficients from a grey level run-length matrix. More...
class  itk::Statistics::HistogramToTextureFeaturesFilter< THistogram >
 This class computes texture feature coefficients from a grey level co-occurrence matrix. More...
class  itk::Statistics::ImageClassifierFilter< TSample, TInputImage, TOutputImage >
 Image classification class. More...
class  itk::Statistics::ImageJointDomainTraits< TImage >
 This class provides the type defintion for the measurement vector in the joint domain (range domain -- pixel values + spatial domain -- pixel's physical coordinates). More...
class  itk::Statistics::ImageToHistogramFilter< TImage >
 This class generates an histogram from an image. More...
class  itk::Statistics::ImageToListSampleAdaptor< TImage >
 This class provides ListSample interface to ITK Image. More...
class  itk::Statistics::ImageToListSampleFilter< TImage, TMaskImage >
 The class takes an image as input and generates a list sample as output. More...
class  itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::Iterator
 class that walks through the elements of the histogram. More...
class  itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >
 This adaptor returns measurement vectors composed of an image pixel's range domain value (pixel value) and spatial domain value (pixel's physical coordiantes). More...
class  itk::KalmanLinearEstimator< T, VEstimatorDimension >
 Implement a linear recursive estimator. More...
class  itk::Statistics::KdTree< TSample >
 This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More...
class  itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >
 fast k-means algorithm implementation using k-d tree structure More...
class  itk::Statistics::KdTreeGenerator< TSample >
 This class generates a KdTree object without centroid information. More...
class  itk::Statistics::KdTreeNode< TSample >
 This class defines the interface of its derived classes. More...
class  itk::Statistics::KdTreeNonterminalNode< TSample >
 This is a subclass of the KdTreeNode. More...
class  itk::Statistics::KdTreeTerminalNode< TSample >
 This class is the node that doesn't have any child node. The IsTerminal method returns true for this class. This class stores the instance identifiers belonging to this node, while the nonterminal nodes do not store them. The AddInstanceIdentifier and GetInstanceIdentifier are storing and retrieving the instance identifiers belonging to this node. More...
class  itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample >
 This is a subclass of the KdTreeNode. More...
class  itk::Statistics::ListSample< TMeasurementVector >
 This class is the native implementation of the a Sample with an STL container. More...
class  itk::Statistics::MahalanobisDistanceMembershipFunction< TVector >
 MahalanobisDistanceMembershipFunction models class membership using Mahalanobis distance. More...
class  itk::Statistics::MahalanobisDistanceMetric< TVector >
 MahalanobisDistanceMetric class computes a Mahalanobis distance given a mean and covariance. More...
class  itk::Statistics::ManhattanDistanceMetric< TVector >
 Euclidean distance function. More...
class  itk::Statistics::MaskedImageToHistogramFilter< TImage, TMaskImage >
 This class generates an histogram from an image. More...
class  itk::Statistics::MaximumDecisionRule
 A decision rule that returns the class label with the largest discriminant score. More...
class  itk::Statistics::MaximumRatioDecisionRule
 A decision rule that operates as a frequentist's approximation to Bayes rule. More...
class  itk::Statistics::MeanSampleFilter< TSample >
 Given a sample, this filter computes the sample mean. More...
class  itk::Statistics::MeasurementVectorTraits
class  itk::Statistics::MeasurementVectorTraitsTypes< TMeasurementVector >
class  itk::Statistics::MembershipFunctionBase< TVector >
 MembershipFunctionBase defines common interfaces for membership functions. More...
class  itk::Statistics::MembershipSample< TSample >
 Container for storing the instance-identifiers of other sample with their associated class labels. More...
class  itk::Statistics::MinimumDecisionRule
 A decision rule that returns the class label with the smallest discriminant score. More...
class  itk::Statistics::MixtureModelComponentBase< TSample >
 base class for distribution modules that supports analytical way to update the distribution parameters More...
class  itk::Statistics::KdTree< TSample >::NearestNeighbors
 data structure for storing k-nearest neighbor search result (k number of Neighbors) More...
class  itk::Statistics::NeighborhoodSampler< TSample >
 Generates a Subsample out of a Sample, based on a user-provided distance to a MeasurementVector. More...
class  itk::Statistics::NormalVariateGenerator
 Normal random variate generator. More...
class  itk::Statistics::PointSetToListSampleAdaptor< TPointSet >
 This class provides ListSample interface to ITK PointSet. More...
class  itk::Statistics::ProbabilityDistribution
 ProbabilityDistribution class defines common interface for statistical distributions (pdfs, cdfs, etc.). More...
class  itk::Statistics::Sample< TMeasurementVector >
 A collection of measurements for statistical analysis. More...
class  itk::Statistics::SampleClassifierFilter< TSample >
 Sample classification class. More...
class  itk::Statistics::SampleToHistogramFilter< TSample, THistogram >
 Computes the Histogram corresponding to a Sample. More...
class  itk::Statistics::SampleToSubsampleFilter< TSample >
 Base class of filters intended to select subsamples from samples. More...
class  itk::Statistics::ScalarImageToCooccurrenceListSampleFilter< TImage >
 Converts pixel data into a list of pairs in order to compute a cooccurrence Histogram. More...
class  itk::Statistics::ScalarImageToCooccurrenceMatrixFilter< TImageType, THistogramFrequencyContainer >
 This class computes a co-occurence matrix (histogram) from a given image and a mask image if provided. Coocurrence matrces are used for image texture description. More...
class  itk::Statistics::ScalarImageToHistogramGenerator< TImageType >
 TODO. More...
class  itk::Statistics::ScalarImageToRunLengthFeaturesFilter< TImageType, THistogramFrequencyContainer >
 This class computes run length descriptions from an image. More...
class  itk::Statistics::ScalarImageToRunLengthMatrixFilter< TImageType, THistogramFrequencyContainer >
 This class computes a run length matrix (histogram) from a given image and a mask image if provided. Run length matrces are used for image texture description. More...
class  itk::Statistics::ScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer >
 This class computes texture descriptions from an image. More...
class  itk::Statistics::SparseFrequencyContainer2
 his class is a container for an histogram. More...
class  itk::Statistics::StandardDeviationPerComponentSampleFilter< TSample >
 Calculates the covariance matrix of the target sample data. More...
class  itk::Statistics::Subsample< TSample >
 This class stores a subset of instance identifiers from another sample object. You can create a subsample out of another sample object or another subsample object. The class is useful when storing or extracting a portion of a sample object. Note that when the elements of a subsample are sorted, the instance identifiers of the subsample are sorted without changing the original source sample. Most Statistics algorithms (that derive from StatisticsAlgorithmBase accept Subsample objects as inputs). More...
class  itk::Statistics::TDistribution
 TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.). More...
class  itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >
 This class provides ListSample interface to ITK VectorContainer. More...
class  itk::Statistics::WeightedCentroidKdTreeGenerator< TSample >
 This class generates a KdTree object with centroid information. More...
class  itk::Statistics::WeightedCovarianceSampleFilter< TSample >
 Calculates the covariance matrix of the target sample data. where each measurement vector has an associated weight value. More...
class  itk::Statistics::WeightedMeanSampleFilter< TSample >
 Given a sample where each measurement vector has associated weight value, this filter computes the sample mean. More...

Detailed Description

The Statistics module contains basic data structures, statistical algorithms, and a classification for general statistical analysis and classification problems. This includes, for examples, classes for calculating histograms, calculating sample statistics, creating decision rules, or for performing statistical pattern classification. Statistics are calculated on a Sample, which contains MeasurementVector's.

Dependencies: