This class computes texture descriptions from an image. More...

#include <itkEnhancedScalarImageToTextureFeaturesFilter.h>

Inheritance diagram for itk::Statistics::EnhancedScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer >:

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Collaboration diagram for itk::Statistics::EnhancedScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer >:

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Public Types
typedef EnhancedScalarImageToTextureFeaturesFilter	Self

typedef ProcessObject	Superclass

typedef SmartPointer< Self >	Pointer

typedef SmartPointer< const Self >	ConstPointer

typedef THistogramFrequencyContainer	FrequencyContainerType

typedef TImageType	ImageType

typedef ImageType::Pointer	ImagePointer

typedef ImageType::PixelType	PixelType

typedef ImageType::OffsetType	OffsetType

typedef VectorContainer< unsigned char, OffsetType >	OffsetVector

typedef OffsetVector::Pointer	OffsetVectorPointer

typedef OffsetVector::ConstPointer	OffsetVectorConstPointer

typedef ScalarImageToCooccurrenceMatrixFilter< ImageType, FrequencyContainerType >	CooccurrenceMatrixFilterType

typedef CooccurrenceMatrixFilterType::HistogramType	HistogramType

typedef EnhancedHistogramToTextureFeaturesFilter< HistogramType >	TextureFeaturesFilterType

typedef short	TextureFeatureName

typedef VectorContainer< unsigned char, TextureFeatureName >	FeatureNameVector

typedef FeatureNameVector::Pointer	FeatureNameVectorPointer

typedef FeatureNameVector::ConstPointer	FeatureNameVectorConstPointer

typedef VectorContainer< unsigned char, double >	FeatureValueVector

typedef FeatureValueVector::Pointer	FeatureValueVectorPointer

typedef DataObject::Pointer	DataObjectPointer

typedef DataObjectDecorator< FeatureValueVector >	FeatureValueVectorDataObjectType

Public Member Functions
virtual const char *	GetClassName () const

const FeatureValueVectorDataObjectType *	GetFeatureMeansOutput () const

const FeatureValueVectorDataObjectType *	GetFeatureStandardDeviationsOutput () const

void	SetInput (const ImageType *)

const ImageType *	GetInput () const

virtual const FeatureValueVector ::Pointer &	GetFeatureMeans () const

virtual const FeatureValueVector ::Pointer &	GetFeatureStandardDeviations () const

virtual void	SetRequestedFeatures (const FeatureNameVector *_arg)

virtual const FeatureNameVector *	GetRequestedFeatures ()

virtual void	SetOffsets (const OffsetVector *_arg)

virtual const OffsetVector *	GetOffsets ()

void	SetNumberOfBinsPerAxis (unsigned int)

void	SetPixelValueMinMax (PixelType min, PixelType max)

void	SetMaskImage (const ImageType *)

const ImageType *	GetMaskImage () const

void	SetInsidePixelValue (PixelType InsidePixelValue)

virtual bool	GetFastCalculations () const

virtual void	SetFastCalculations (bool _arg)

virtual void	FastCalculationsOn ()

virtual void	FastCalculationsOff ()

Static Public Member Functions
static Pointer	New ()

Protected Types
typedef ProcessObject::DataObjectPointerArraySizeType	DataObjectPointerArraySizeType

Protected Member Functions
	EnhancedScalarImageToTextureFeaturesFilter ()

	~EnhancedScalarImageToTextureFeaturesFilter () override

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

void	FastCompute ()

void	FullCompute ()

void	GenerateData () ITK_OVERRIDE

DataObjectPointer	MakeOutput (DataObjectPointerArraySizeType) ITK_OVERRIDE

Detailed Description

template<typename TImageType, typename THistogramFrequencyContainer = DenseFrequencyContainer2>
class itk::Statistics::EnhancedScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer >

This class computes texture descriptions from an image.

This class computes features that summarize the texture of a given image. The texture features are computed a la Haralick, and have proven to be useful in image classification for biological and medical imaging. This class computes the texture features of an image (optionally in a masked region), averaged across several spatial directions so that they are invariant to rotation.

By default, texture 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 GLCM for each offset given. To compute a single GLCM 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 image type.

Template Parameters: The image type, and the type of histogram frequency container. If you are using a large number of bins per axis, a sparse frequency container may be advisable. The default is to use a dense frequency container.

Inputs and parameters:

An image
A mask defining the region over which texture features will be calculated. (Optional)
The pixel value that defines the "inside" of the mask. (Optional, defaults to 1 if a mask is set.)
The set of features to be calculated. These features are defined in the GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator class. (Optional, defaults to {Energy, Entropy, InverseDifferenceMoment, Inertia, ClusterShade, ClusterProminence}, as in Conners, Trivedi and Harlow.)
The number of intensity bins. (Optional, defaults to 256.)
The set of directions (offsets) to average across. (Optional, defaults to {(-1, 0), (-1, -1), (0, -1), (1, -1)} for 2D images and scales analogously for ND images.)
The pixel intensity range over which the features will be calculated. (Optional, defaults to the full dynamic range of the pixel type.)

In general, the default parameter values should be sufficient.

Outputs: (1) The average value of each feature. (2) The standard deviation in the values of each feature.

Web reference: https://prism.ucalgary.ca/handle/1880/51900

Print references: Haralick, R.M., K. Shanmugam and I. Dinstein. 1973. Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics. SMC-3(6):610-620.

Haralick, R.M. 1979. Statistical and Structural Approaches to Texture. Proceedings of the IEEE, 67:786-804.

R.W. Conners and C.A. Harlow. A Theoretical Comparison of Texture Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:204-222, 1980.

R.W. Conners, M.M. Trivedi, and C.A. Harlow. Segmentation of a High-Resolution Urban Scene using Texture Operators. Computer Vision, Graphics and Image Processing, 25:273-310, 1984.

See also: ScalarImageToCooccurrenceMatrixFilter; HistogramToTextureFeaturesFilter

Author: Zachary Pincus

Definition at line 118 of file itkEnhancedScalarImageToTextureFeaturesFilter.h.