ITK  6.0.0
Insight Toolkit
Classes | Enumerations
Module ITKStatistics
Group Numerics
+ Collaboration diagram for Module ITKStatistics:

Classes

class  itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector
 
class  itk::Statistics::ChiSquareDistribution
 
class  itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >::ConstIterator
 
class  itk::Statistics::PointSetToListSampleAdaptor< TPointSet >::ConstIterator
 
class  itk::Statistics::ListSample< TMeasurementVector >::ConstIterator
 
class  itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >::ConstIterator
 
class  itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >::ConstIterator
 
class  itk::Statistics::ImageToListSampleAdaptor< TImage >::ConstIterator
 
class  itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::ConstIterator
 
class  itk::Statistics::CovarianceSampleFilter< TSample >
 
class  itk::Statistics::DecisionRule
 
class  itk::Statistics::DenseFrequencyContainer2
 
class  itk::Statistics::DistanceMetric< TVector >
 
class  itk::Statistics::DistanceToCentroidMembershipFunction< TVector >
 
class  itk::Statistics::EuclideanDistanceMetric< TVector >
 
class  itk::Statistics::EuclideanSquareDistanceMetric< TVector >
 
class  itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample >
 
class  itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums
 
class  itk::Statistics::GaussianDistribution
 
class  itk::Statistics::GaussianMembershipFunction< TMeasurementVector >
 
class  itk::Statistics::GaussianMixtureModelComponent< TSample >
 
class  itk::Statistics::GaussianRandomSpatialNeighborSubsampler< TSample, TRegion >
 
class  itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >
 
class  itk::HistogramToEntropyImageFilter< THistogram, TImage >
 
class  itk::HistogramToImageFilter< THistogram, TImage, TFunction >
 
class  itk::HistogramToIntensityImageFilter< THistogram, TImage >
 
class  itk::HistogramToLogProbabilityImageFilter< THistogram, TImage >
 
class  itk::HistogramToProbabilityImageFilter< THistogram, TImage >
 
class  itk::Statistics::HistogramToRunLengthFeaturesFilter< THistogram >
 
class  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums
 
class  itk::Statistics::HistogramToTextureFeaturesFilter< THistogram >
 
class  itk::Statistics::HistogramToTextureFeaturesFilterEnums
 
class  itk::Statistics::ImageClassifierFilter< TSample, TInputImage, TOutputImage >
 
class  itk::Statistics::ImageJointDomainTraits< TImage >
 
class  itk::Statistics::ImageToHistogramFilter< TImage >
 
class  itk::Statistics::ImageToListSampleAdaptor< TImage >
 
class  itk::Statistics::ImageToListSampleFilter< TImage, TMaskImage >
 
class  itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >
 
class  itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >::Iterator
 
class  itk::Statistics::PointSetToListSampleAdaptor< TPointSet >::Iterator
 
class  itk::Statistics::ListSample< TMeasurementVector >::Iterator
 
class  itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >::Iterator
 
class  itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >::Iterator
 
class  itk::Statistics::ImageToListSampleAdaptor< TImage >::Iterator
 
class  itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::Iterator
 
class  itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >
 
class  itk::KalmanLinearEstimator< T, VEstimatorDimension >
 
class  itk::Statistics::KdTree< TSample >
 
class  itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >
 
class  itk::Statistics::KdTreeGenerator< TSample >
 
class  itk::Statistics::KdTreeNode< TSample >
 
class  itk::Statistics::KdTreeNonterminalNode< TSample >
 
class  itk::Statistics::KdTreeTerminalNode< TSample >
 
class  itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample >
 
class  itk::Statistics::ListSample< TMeasurementVector >
 
class  itk::Statistics::MahalanobisDistanceMembershipFunction< TVector >
 
class  itk::Statistics::MahalanobisDistanceMetric< TVector >
 
class  itk::Statistics::ManhattanDistanceMetric< TVector >
 
class  itk::Statistics::MaskedImageToHistogramFilter< TImage, TMaskImage >
 
class  itk::Statistics::MaximumDecisionRule
 
class  itk::Statistics::MaximumRatioDecisionRule
 
class  itk::Statistics::MeanSampleFilter< TSample >
 
class  itk::Statistics::MeasurementVectorTraits
 
class  itk::Statistics::MeasurementVectorTraitsTypes< TMeasurementVector >
 
class  itk::Statistics::MembershipFunctionBase< TVector >
 
class  itk::Statistics::MembershipSample< TSample >
 
class  itk::Statistics::MinimumDecisionRule
 
class  itk::Statistics::MixtureModelComponentBase< TSample >
 
class  itk::Statistics::KdTree< TSample >::NearestNeighbors
 
class  itk::Statistics::NeighborhoodSampler< TSample >
 
class  itk::Statistics::NormalVariateGenerator
 
class  itk::Statistics::PointSetToListSampleAdaptor< TPointSet >
 
class  itk::Statistics::ProbabilityDistribution
 
class  itk::Statistics::RegionConstrainedSubsampler< TSample, TRegion >
 
class  itk::Statistics::Sample< TMeasurementVector >
 
class  itk::Statistics::SampleClassifierFilter< TSample >
 
class  itk::Statistics::SampleToSubsampleFilter< TSample >
 
class  itk::Statistics::ScalarImageToCooccurrenceListSampleFilter< TImage >
 
class  itk::Statistics::ScalarImageToCooccurrenceMatrixFilter< TImageType, THistogramFrequencyContainer, TMaskImageType >
 
class  itk::Statistics::ScalarImageToHistogramGenerator< TImageType >
 
class  itk::Statistics::ScalarImageToRunLengthFeaturesFilter< TImageType, THistogramFrequencyContainer >
 
class  itk::Statistics::ScalarImageToRunLengthMatrixFilter< TImageType, THistogramFrequencyContainer >
 
class  itk::Statistics::ScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer, TMaskImageType >
 
class  itk::Statistics::SparseFrequencyContainer2
 
class  itk::Statistics::SpatialNeighborSubsampler< TSample, TRegion >
 
class  itk::Statistics::StandardDeviationPerComponentSampleFilter< TSample >
 
class  itk::Statistics::Subsample< TSample >
 
class  itk::Statistics::SubsamplerBase< TSample >
 
class  itk::Statistics::TDistribution
 
class  itk::Statistics::UniformRandomSpatialNeighborSubsampler< TSample, TRegion >
 
class  itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >
 
class  itk::Statistics::WeightedCentroidKdTreeGenerator< TSample >
 
class  itk::Statistics::WeightedCovarianceSampleFilter< TSample >
 
class  itk::Statistics::WeightedMeanSampleFilter< TSample >
 

Enumerations

enum  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature : uint8_t {
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::ShortRunEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::LongRunEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::GreyLevelNonuniformity,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::RunLengthNonuniformity,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::LowGreyLevelRunEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::HighGreyLevelRunEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::ShortRunLowGreyLevelEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::ShortRunHighGreyLevelEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::LongRunLowGreyLevelEmphasis,
  itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature::LongRunHighGreyLevelEmphasis
}
 
enum  itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE : uint8_t {
  itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE::CONVERGED = 0,
  itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE::NOT_CONVERGED = 1
}
 
enum  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature : uint8_t {
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::Energy,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::Entropy,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::Correlation,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::InverseDifferenceMoment,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::Inertia,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::ClusterShade,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::ClusterProminence,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::HaralickCorrelation,
  itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature::InvalidFeatureName
}
 

Detailed Description

The Statistics module contains basic data structures, statistical algorithms, and a classification for general statistical analysis and classification problems. This includes, for example, classes for calculating histograms, calculating sample statistics, creating decision rules, or for performing statistical pattern classification. Statistics are calculated on an itk::Sample, which contains measurement vectors.

Dependencies:

Enumeration Type Documentation

◆ RunLengthFeature

enum itk::Statistics::HistogramToRunLengthFeaturesFilterEnums::RunLengthFeature : uint8_t
strong

Run-length feature types.

Enumerator
ShortRunEmphasis 
LongRunEmphasis 
GreyLevelNonuniformity 
RunLengthNonuniformity 
LowGreyLevelRunEmphasis 
HighGreyLevelRunEmphasis 
ShortRunLowGreyLevelEmphasis 
ShortRunHighGreyLevelEmphasis 
LongRunLowGreyLevelEmphasis 
LongRunHighGreyLevelEmphasis 

Definition at line 41 of file itkHistogramToRunLengthFeaturesFilter.h.

◆ TERMINATION_CODE

enum itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE : uint8_t
strong

Termination status after running optimization

Enumerator
CONVERGED 
NOT_CONVERGED 

Definition at line 40 of file itkExpectationMaximizationMixtureModelEstimator.h.

◆ TextureFeature

enum itk::Statistics::HistogramToTextureFeaturesFilterEnums::TextureFeature : uint8_t
strong

Texture feature types

Enumerator
Energy 
Entropy 
Correlation 
InverseDifferenceMoment 
Inertia 
ClusterShade 
ClusterProminence 
HaralickCorrelation 
InvalidFeatureName 

Definition at line 41 of file itkHistogramToTextureFeaturesFilter.h.

Tarballs of the nightly generated Doxygen documentation are available for the html, xml, and tag file. Previous versions of this documentation for version: 5.3, 5.2, 5.1, 5.0, 4.13, 3.20.
Generated on unknown for ITK by   doxygen 1.8.16