This class computes texture feature coefficients from a grey level run-length matrix. More...

#include <itkEnhancedHistogramToRunLengthFeaturesFilter.h>

Inheritance diagram for itk::Statistics::EnhancedHistogramToRunLengthFeaturesFilter< THistogram >:

Collaboration diagram for itk::Statistics::EnhancedHistogramToRunLengthFeaturesFilter< THistogram >:

Public Types
enum	RunLengthFeatureName { ShortRunEmphasis, LongRunEmphasis, GreyLevelNonuniformity, RunLengthNonuniformity, LowGreyLevelRunEmphasis, HighGreyLevelRunEmphasis, ShortRunLowGreyLevelEmphasis, ShortRunHighGreyLevelEmphasis, LongRunLowGreyLevelEmphasis, LongRunHighGreyLevelEmphasis, RunPercentage, NumberOfRuns }

typedef EnhancedHistogramToRunLengthFeaturesFilter	Self

typedef ProcessObject	Superclass

typedef SmartPointer< Self >	Pointer

typedef SmartPointer< const Self >	ConstPointer

typedef THistogram	HistogramType

typedef HistogramType::Pointer	HistogramPointer

typedef HistogramType::ConstPointer	HistogramConstPointer

typedef HistogramType::MeasurementType	MeasurementType

typedef HistogramType::MeasurementVectorType	MeasurementVectorType

typedef HistogramType::IndexType	IndexType

typedef HistogramType::TotalAbsoluteFrequencyType	FrequencyType

typedef DataObject::Pointer	DataObjectPointer

typedef SimpleDataObjectDecorator< MeasurementType >	MeasurementObjectType

Public Member Functions
virtual const char *	GetClassName () const

void	SetInput (const HistogramType *histogram)

const HistogramType *	GetInput () const

MeasurementType	GetShortRunEmphasis () const

const MeasurementObjectType *	GetShortRunEmphasisOutput () const

MeasurementType	GetLongRunEmphasis () const

const MeasurementObjectType *	GetLongRunEmphasisOutput () const

MeasurementType	GetGreyLevelNonuniformity () const

const MeasurementObjectType *	GetGreyLevelNonuniformityOutput () const

MeasurementType	GetRunLengthNonuniformity () const

const MeasurementObjectType *	GetRunLengthNonuniformityOutput () const

MeasurementType	GetLowGreyLevelRunEmphasis () const

const MeasurementObjectType *	GetLowGreyLevelRunEmphasisOutput () const

MeasurementType	GetHighGreyLevelRunEmphasis () const

const MeasurementObjectType *	GetHighGreyLevelRunEmphasisOutput () const

MeasurementType	GetShortRunLowGreyLevelEmphasis () const

const MeasurementObjectType *	GetShortRunLowGreyLevelEmphasisOutput () const

MeasurementType	GetShortRunHighGreyLevelEmphasis () const

const MeasurementObjectType *	GetShortRunHighGreyLevelEmphasisOutput () const

MeasurementType	GetLongRunLowGreyLevelEmphasis () const

const MeasurementObjectType *	GetLongRunLowGreyLevelEmphasisOutput () const

MeasurementType	GetLongRunHighGreyLevelEmphasis () const

const MeasurementObjectType *	GetLongRunHighGreyLevelEmphasisOutput () const

MeasurementType	GetRunPercentage () const

const MeasurementObjectType *	GetRunPercentageOutput () const

MeasurementType	GetNumberOfRuns () const

const MeasurementObjectType *	GetNumberOfRunsOutput () const

virtual unsigned long	GetTotalNumberOfRuns ()

virtual unsigned long	GetNumberOfVoxels () const

virtual void	SetNumberOfVoxels (unsigned long _arg)

MeasurementType	GetFeature (RunLengthFeatureName name)

Static Public Member Functions
static Pointer	New ()

Protected Types
typedef ProcessObject::DataObjectPointerArraySizeType	DataObjectPointerArraySizeType

Protected Member Functions
	EnhancedHistogramToRunLengthFeaturesFilter ()

	~EnhancedHistogramToRunLengthFeaturesFilter ()

virtual void	PrintSelf (std::ostream &os, Indent indent) const ITK_OVERRIDE

virtual DataObjectPointer	MakeOutput (DataObjectPointerArraySizeType) ITK_OVERRIDE

virtual void	GenerateData () ITK_OVERRIDE

Detailed Description

template<typename THistogram>
class itk::Statistics::EnhancedHistogramToRunLengthFeaturesFilter< THistogram >

This class computes texture feature coefficients from a grey level run-length matrix.

By default, run length features are computed for each spatial direction and then averaged afterward, so it is possible to access the standard deviations of the texture features. These values give a clue as to texture anisotropy. However, doing this is much more work, because it involved computing one for each offset given. To compute a single matrix using the first offset, call FastCalculationsOn(). If this is called, then the texture standard deviations will not be computed (and will be set to zero), but texture computation will be much faster.

This class is templated over the input histogram type.

Print references: M. M. Galloway. Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4:172-179, 1975.

A. Chu, C. M. Sehgal, and J. F. Greenleaf. Use of gray value distribution of run lengths for texture analysis. Pattern Recognition Letters, 11:415-420, 1990.

B. R. Dasarathy and E. B. Holder. Image characterizations based on joint gray-level run-length distributions. Pattern Recognition Letters, 12:490-502, 1991.

IJ article: http://hdl.handle.net/1926/1374

See also: ScalarImageToRunLengthFeaturesFilter; ScalarImageToRunLengthMatrixFilter; EnhancedHistogramToRunLengthFeaturesFilter

Author: : Nick Tustison

Definition at line 82 of file itkEnhancedHistogramToRunLengthFeaturesFilter.h.