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itk::Statistics Namespace Reference

Namespaces

namespace  Algorithm

Classes

class  BackPropagationLayer
class  BatchSupervisedTrainingFunction
class  ChiSquareDistribution
 ChiSquareDistribution class defines the interface for a univariate Chi-Square distribution (pdfs, cdfs, etc.). More...
class  CompletelyConnectedWeightSet
class  CovarianceCalculator
 Calculates the covariance matrix of the target sample data. More...
class  CovarianceSampleFilter
 Calculates the covariance matrix of the target sample data. More...
class  DecisionRule
 Base class that allows the setting of usage of different decision rules used in classification This class has the pure virtual function, Evaluate(). Therefore, any subclass should implement the function to be instantiated. More...
class  DenseFrequencyContainer
 his class is a container for frequencies of bins in an histogram. More...
class  DenseFrequencyContainer2
 his class is a container for frequencies of bins in an histogram. More...
class  DensityFunction
 DensityFunction class defines common interfaces for density functions. More...
class  DistanceMetric
 this class declares common interfaces for distance functions. More...
class  DistanceToCentroidMembershipFunction
 class represents DistanceToCentroid Density Function. More...
class  ErrorBackPropagationLearningFunctionBase
class  ErrorBackPropagationLearningWithMomentum
class  ErrorFunctionBase
class  EuclideanDistance
 Euclidean distance function. More...
class  EuclideanDistanceMetric
 Euclidean distance function. More...
class  EuclideanSquareDistanceMetric
 Computes Euclidean distance between origin and given measurement vector. More...
class  ExpectationMaximizationMixtureModelEstimator
 This class generates the parameter estimates for a mixture model using expectation maximization strategy. More...
class  GaussianDensityFunction
 GaussianDensityFunction class represents Gaussian Density Function. More...
class  GaussianDistribution
 GaussianDistribution class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.). More...
class  GaussianGoodnessOfFitComponent
 is a GoodnessOfFitComponent for Gaussian distribution. More...
class  GaussianMembershipFunction
 GaussianMembershipFunction class represents Gaussian function. More...
class  GaussianMixtureModelComponent
 is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More...
class  GaussianRadialBasisFunction
class  GaussianTransferFunction
struct  GetAdaptorMeasurementVectorLength
struct  GetHistogramDimension
class  GoodnessOfFitComponentBase
 provides component (module) type specific functionalities for GoodnessOfFitMixtureModelCostFunction. More...
class  GoodnessOfFitFunctionBase
 base class for classes calculates different types of goodness-of-fit statistics More...
class  GoodnessOfFitMixtureModelCostFunction
 calculates the goodness-of-fit statstics for multivarate mixture model More...
class  GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator
 This class computes texture feature coefficients from a grey level co-occurrence matrix. More...
class  HardLimitTransferFunction
class  Histogram
 This class stores measurement vectors in the context of n-dimensional histogram. More...
class  HistogramToTextureFeaturesFilter
 This class computes texture feature coefficients from a grey level co-occurrence matrix. More...
class  HypersphereKernelMeanShiftModeSeeker
 Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More...
class  IdentityTransferFunction
class  ImageClassifierFilter
 Image classification class. More...
class  ImageJointDomainTraits
 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  ImageToCooccurrenceListAdaptor
 Converts pixel data into a list of pairs in order to compute a cooccurrence Histogram. More...
class  ImageToHistogramFilter
 This class generates an histogram from an image. More...
class  ImageToHistogramGenerator
 This class generates an histogram from an image. More...
class  ImageToListAdaptor
 This class provides ListSampleBase interfaces to ITK Image. More...
class  ImageToListGenerator
 The class takes an image as input and generates a list sample as output. More...
class  ImageToListSampleAdaptor
 This class provides ListSample interface to ITK Image. More...
class  ImageToListSampleFilter
 The class takes an image as input and generates a list sample as output. More...
class  InputFunctionBase
class  IterativeSupervisedTrainingFunction
class  JointDomainImageToListAdaptor
 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  JointDomainImageToListSampleAdaptor
 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  KdTree
 This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More...
class  KdTreeBasedKmeansEstimator
 fast k-means algorithm implementation using k-d tree structure More...
class  KdTreeGenerator
 This class generates a KdTree object without centroid information. More...
class  KdTreeNode
 This class defines the interface of its derived classes. More...
class  KdTreeNonterminalNode
 This is a subclass of the KdTreeNode. More...
class  KdTreeTerminalNode
 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  KdTreeWeightedCentroidNonterminalNode
 This is a subclass of the KdTreeNode. More...
class  LayerBase
class  LearningFunctionBase
class  ListSample
 This class is the native implementation of the ListSampleBase. More...
class  ListSampleBase
 This class is the base class for Samples that store measurements in a list. More...
class  ListSampleToHistogramFilter
 Imports data from ListSample object to Histogram object. More...
class  ListSampleToHistogramGenerator
 Generates a Histogram using the data from the ListSample object. More...
class  LogLikelihoodGoodnessOfFitFunction
 calculates loglikelihood ratio statistics More...
class  LogSigmoidTransferFunction
class  MahalanobisDistanceMembershipFunction
 MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density Function. More...
class  MahalanobisDistanceMetric
 MahalanobisDistanceMetric class computes a Mahalanobis distance given a mean and covariance. More...
class  ManhattanDistanceMetric
 Euclidean distance function. More...
class  MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator
 This class computes a grey-level co-occurence matrix (histogram) from a given image and mask. GLCM's are used for image texture description. More...
class  MaximumDecisionRule2
 A Decision rule that choose the class of which discriminant score is the largest. This class will replace the MaximumDecisionRule in Code/Common. More...
class  MaximumRatioDecisionRule2
 This rule returns $i$ if $\frac{f_{i}(\overrightarrow{x})}{f_{j}(\overrightarrow{x})} > \frac{K_{j}}{K_{i}}$ for all $j \not= i$, where the $i$ is the index of a class which has membership function $f_{i}$ and its prior value (usually, the a priori probability or the size of a class) is $K_{i}$. More...
class  MeanCalculator
 calculates sample mean More...
class  MeanSampleFilter
 Given a sample, this filter computes the sample mean. More...
class  MeanShiftModeCacheMethod
 This class stores mappings between a query point and its resulting mode point. More...
class  MeanShiftModeSeekerBase
 Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. More...
class  MeanSquaredErrorFunction
class  MeasurementVectorPixelTraits
class  MeasurementVectorPixelTraits< char >
class  MeasurementVectorPixelTraits< double >
class  MeasurementVectorPixelTraits< float >
class  MeasurementVectorPixelTraits< signed char >
class  MeasurementVectorPixelTraits< signed int >
class  MeasurementVectorPixelTraits< signed long >
class  MeasurementVectorPixelTraits< signed short >
class  MeasurementVectorPixelTraits< unsigned char >
class  MeasurementVectorPixelTraits< unsigned int >
class  MeasurementVectorPixelTraits< unsigned long >
class  MeasurementVectorPixelTraits< unsigned short >
class  MeasurementVectorTraits
class  MeasurementVectorTraitsTypes
class  MeasurementVectorTraitsTypes< std::vector< T > >
class  MembershipFunctionBase
 MembershipFunctionBase class declares common interfaces for membership functions. More...
class  MembershipSample
 Container for storing the instance-identifiers of other sample with their associated class labels. More...
class  MembershipSampleGenerator
 MembershipSampleGenerator generates a MembershipSample object using a class mask sample. More...
class  MersenneTwisterRandomVariateGenerator
 MersenneTwisterRandom random variate generator. More...
class  MinimumDecisionRule2
 A Decision rule that choose the class of which discriminant score is the largest. This class will replace the MinimumDecisionRule in Code/Common. More...
class  MixtureModelComponentBase
 base class for distribution modules that supports analytical way to update the distribution parameters More...
class  MultilayerNeuralNetworkBase
class  MultiquadricRadialBasisFunction
class  NeighborhoodSampler
 generates a Subsample that is sampled from the input sample using a spherical kernel. More...
class  NeuralNetworkObject
class  NNetDistanceMetricBase
class  NormalVariateGenerator
 Normal random variate generator. More...
class  OneHiddenLayerBackPropagationNeuralNetwork
class  PointSetToListAdaptor
 This class provides ListSampleBase interfaces to ITK PointSet. More...
class  PointSetToListSampleAdaptor
 This class provides ListSample interface to ITK PointSet. More...
class  ProbabilityDistribution
 ProbabilityDistribution class defines common interface for statistical distributions (pdfs, cdfs, etc.). More...
class  ProductInputFunction
class  QuickPropLearningRule
class  RadialBasisFunctionBase
class  RandomVariateGeneratorBase
 this class defines common interfaces for random variate generators More...
class  RBFBackPropagationLearningFunction
class  RBFLayer
class  RBFNetwork
class  Sample
 A collection of measurements for statistical analysis. More...
class  SampleAlgorithmBase
 This class is a base class for algorithms that operate on Sample data. The class is templated over the SampleType, which it takes as input using the SetInputSample() method. Derived classes that operate or calculate statistics on this input sample data and can access it using the GetInputSample() method. More...
class  SampleClassifier
 Integration point for MembershipCalculator, DecisionRule, and target sample data. More...
class  SampleClassifierFilter
 Sample classification class. More...
class  SampleClassifierWithMask
 Integration point for MembershipCalculator, DecisionRule, and target sample data. This class is functionally identical to the SampleClassifier, except that users can perform only part of the input sample that belongs to the subset of classes. More...
class  SampleMeanShiftBlurringFilter
 This filter blurs the input sample data using mean shift algorithm. More...
class  SampleMeanShiftClusteringFilter
 This filter create a cluster map from an input sample. More...
class  SampleSelectiveMeanShiftBlurringFilter
 This filter blurs the input sample data using mean shift algorithm selectively. More...
class  SampleToHistogramFilter
 Computes the Histogram corresponding to a Sample. More...
class  SampleToHistogramProjectionFilter
 projects measurement vectors on to an axis to generate an 1D histogram. More...
class  SampleToSubsampleFilter
 Base class of filters intended to select subsamples from samples. More...
class  ScalarImageTextureCalculator
 This class computes texture descriptions from an image. More...
class  ScalarImageToCooccurrenceListSampleFilter
 Converts pixel data into a list of pairs in order to compute a cooccurrence Histogram. More...
class  ScalarImageToCooccurrenceMatrixFilter
 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  ScalarImageToGreyLevelCooccurrenceMatrixGenerator
 This class computes a grey-level co-occurence matrix (histogram) from a given image. GLCM's are used for image texture description. More...
class  ScalarImageToHistogramGenerator
 TODO. More...
class  ScalarImageToListAdaptor
 This class provides ListSampleBase interfaces to ITK Image. More...
class  ScalarImageToTextureFeaturesFilter
 This class computes texture descriptions from an image. More...
class  SelectiveSubsampleGenerator
 SelectiveSubsampleGenerator generates a Subsample object that includes measurement vectors that belong to the classes that are specified by the SetSelectedClassLabels method. More...
class  SigmoidTransferFunction
class  SignedHardLimitTransferFunction
class  SparseFrequencyContainer
 his class is a container for an histogram. More...
class  SparseFrequencyContainer2
 his class is a container for an histogram. More...
class  SquaredDifferenceErrorFunction
class  StandardDeviationPerComponentSampleFilter
 Calculates the covariance matrix of the target sample data. More...
class  Subsample
 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  SumInputFunction
class  SymmetricSigmoidTransferFunction
class  TanHTransferFunction
class  TanSigmoidTransferFunction
class  TDistribution
 TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.). More...
class  TrainingFunctionBase
class  TransferFunctionBase
class  TwoHiddenLayerBackPropagationNeuralNetwork
class  VariableDimensionHistogram
 This class is similar to the Histogram class. It however allows you to specify the histogram dimension at run time. (and is therefore not templated over the size of a measurement vector). Users who know that the length of a measurement vector will be fixed, for instance joint statistics on pixel values of 2 images, (where the dimension will be 2), etc should use the Histogram class instead. More...
class  WeightedCentroidKdTreeGenerator
 This class generates a KdTree object with centroid information. More...
class  WeightedCovarianceCalculator
 Calculates the covariance matrix of the target sample data where each measurement vector has an associated weight value. More...
class  WeightedCovarianceSampleFilter
 Calculates the covariance matrix of the target sample data. where each measurement vector has an associated weight value. More...
class  WeightedMeanCalculator
 calculates sample mean where each measurement vector has associated weight value More...
class  WeightedMeanSampleFilter
 Given a sample where each measurement vector has associated weight value, this filter computes the sample mean. More...
class  WeightSetBase

Enumerations

enum  TextureFeatureName {
  Energy,
  Entropy,
  Correlation,
  InverseDifferenceMoment,
  Inertia,
  ClusterShade,
  ClusterProminence,
  HaralickCorrelation
}

Functions

template<class TSubsample >
void DownHeap (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int node)
template<class TSample >
void FindSampleBound (const TSample *sample, typename TSample::ConstIterator begin, typename TSample::ConstIterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max)
template<class TSubsample >
void FindSampleBoundAndMean (const TSubsample *sample, int beginIndex, int endIndex, typename TSubsample::MeasurementVectorType &min, typename TSubsample::MeasurementVectorType &max, typename TSubsample::MeasurementVectorType &mean)
template<class TSize >
TSize FloorLog (TSize size)
template<class TSubsample >
void HeapSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)
template<class TSubsample >
void InsertSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)
template<class TSubsample >
void IntrospectiveSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int sizeThreshold)
template<class TSubsample >
void IntrospectiveSortLoop (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int depthLimit, int sizeThreshold)
template<class TValue >
TValue MedianOfThree (const TValue a, const TValue b, const TValue c)
template<class TSubsample >
TSubsample::MeasurementType NthElement (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int nth)
template<class TSubsample >
int Partition (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, const typename TSubsample::MeasurementType partitionValue)
template<class TSubsample >
TSubsample::MeasurementType QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth, typename TSubsample::MeasurementType medianGuess)
template<class TSubsample >
TSubsample::MeasurementType QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth)

Enumeration Type Documentation

Texture feature types

Enumerator:
Energy 
Entropy 
Correlation 
InverseDifferenceMoment 
Inertia 
ClusterShade 
ClusterProminence 
HaralickCorrelation 

Definition at line 98 of file itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h.


Function Documentation

template<class TSubsample >
void itk::Statistics::DownHeap ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
int  node 
)
template<class TSample >
void itk::Statistics::FindSampleBound ( const TSample *  sample,
typename TSample::ConstIterator  begin,
typename TSample::ConstIterator  end,
typename TSample::MeasurementVectorType &  min,
typename TSample::MeasurementVectorType &  max 
)
template<class TSubsample >
void itk::Statistics::FindSampleBoundAndMean ( const TSubsample *  sample,
int  beginIndex,
int  endIndex,
typename TSubsample::MeasurementVectorType &  min,
typename TSubsample::MeasurementVectorType &  max,
typename TSubsample::MeasurementVectorType &  mean 
)
template<class TSize >
TSize itk::Statistics::FloorLog ( TSize  size  ) 
template<class TSubsample >
void itk::Statistics::HeapSort ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex 
)
template<class TSubsample >
void itk::Statistics::InsertSort ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex 
)
template<class TSubsample >
void itk::Statistics::IntrospectiveSort ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
int  sizeThreshold 
)
template<class TSubsample >
void itk::Statistics::IntrospectiveSortLoop ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
int  depthLimit,
int  sizeThreshold 
)
template<class TValue >
TValue itk::Statistics::MedianOfThree ( const TValue  a,
const TValue  b,
const TValue  c 
)
template<class TSubsample >
TSubsample::MeasurementType itk::Statistics::NthElement ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
int  nth 
)

NthElement is an algorithm for finding the n-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. This algorithm was based on the procedure used in the STL nth_element method.

template<class TSubsample >
int itk::Statistics::Partition ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
const typename TSubsample::MeasurementType  partitionValue 
)

The Partition algorithm performs partial sorting in a sample. Given a partitionValue, the algorithm moves to the beginning of the sample all MeasurementVectors whose component activeDimension is smaller than the partitionValue. In this way, the sample is partially sorted in two groups. First the group with activeDimension component smaller than the partitionValue, then the group of MeasurementVectors with activeDimension component larger than the partitionValue. The Partition algorithm takes as input a sample, and a range in that sample defined by [beginIndex,endIndex]. Only the activeDimension components of the MeasurementVectors in the sample will be considered by the algorithm. The Algorithm return an index in the range of [beginIndex,endIndex] pointing to the element with activeDimension component closest to the partitionValue.

template<class TSubsample >
TSubsample::MeasurementType itk::Statistics::QuickSelect ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
int  kth,
typename TSubsample::MeasurementType  medianGuess 
)

QuickSelect is an algorithm for finding the k-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. In this version, a guess value for the median index is provided in the argument medianGuess. The algoritm returns the value of the activeDimension component in the MeasurementVector located in the kth position. http://en.wikipedia.org/wiki/Selection_algorithm

template<class TSubsample >
TSubsample::MeasurementType itk::Statistics::QuickSelect ( TSubsample *  sample,
unsigned int  activeDimension,
int  beginIndex,
int  endIndex,
int  kth 
)

QuickSelect is an algorithm for finding the k-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. http://en.wikipedia.org/wiki/Selection_algorithm.


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