#include <itkScalarImageToTextureFeaturesFilter.h>

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

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

List of all members.

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef ScalarImageToCooccurrenceMatrixFilter < ImageType, FrequencyContainerType >	CooccurrenceMatrixFilterType
typedef DataObject::Pointer	DataObjectPointer
typedef VectorContainer < unsigned char, TextureFeatureName >	FeatureNameVector
typedef FeatureNameVector::ConstPointer	FeatureNameVectorConstPointer
typedef FeatureNameVector::Pointer	FeatureNameVectorPointer
typedef VectorContainer < unsigned char, double >	FeatureValueVector
typedef DataObjectDecorator < FeatureValueVector >	FeatureValueVectorDataObjectType
typedef FeatureValueVector::Pointer	FeatureValueVectorPointer
typedef THistogramFrequencyContainer	FrequencyContainerType
typedef CooccurrenceMatrixFilterType::HistogramType	HistogramType
typedef ImageType::Pointer	ImagePointer
typedef TImageType	ImageType
typedef ImageType::OffsetType	OffsetType
typedef VectorContainer < unsigned char, OffsetType >	OffsetVector
typedef OffsetVector::ConstPointer	OffsetVectorConstPointer
typedef OffsetVector::Pointer	OffsetVectorPointer
typedef ImageType::PixelType	PixelType
typedef SmartPointer< Self >	Pointer
typedef ScalarImageToTextureFeaturesFilter	Self
typedef ProcessObject	Superclass
typedef short	TextureFeatureName
typedef HistogramToTextureFeaturesFilter < HistogramType >	TextureFeaturesFilterType
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual void	FastCalculationsOff ()
virtual void	FastCalculationsOn ()
virtual bool	GetFastCalculations () const
const FeatureValueVectorDataObjectType *	GetFeatureMeansOutput () const
const FeatureValueVectorDataObjectType *	GetFeatureStandardDeviationsOutput () const
const ImageType *	GetInput () const
const ImageType *	GetMaskImage () const
virtual const char *	GetNameOfClass () const
virtual void	SetFastCalculations (bool _arg)
void	SetInput (const ImageType *)
void	SetInsidePixelValue (PixelType InsidePixelValue)
void	SetMaskImage (const ImageType *)
void	SetNumberOfBinsPerAxis (unsigned int)

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	SetPixelValueMinMax (PixelType min, PixelType max)
Static Public Member Functions
static Pointer	New ()
Protected Types
typedef ProcessObject::DataObjectPointerArraySizeType	DataObjectPointerArraySizeType
Protected Member Functions
void	FastCompute ()
void	FullCompute ()
virtual void	GenerateData ()
virtual DataObjectPointer	MakeOutput (DataObjectPointerArraySizeType)
void	PrintSelf (std::ostream &os, Indent indent) const
	ScalarImageToTextureFeaturesFilter ()
virtual	~ScalarImageToTextureFeaturesFilter ()
Private Attributes
bool	m_FastCalculations
FeatureValueVectorPointer	m_FeatureMeans
FeatureValueVectorPointer	m_FeatureStandardDeviations
CooccurrenceMatrixFilterType::Pointer	m_GLCMGenerator
OffsetVectorConstPointer	m_Offsets
FeatureNameVectorConstPointer	m_RequestedFeatures

Detailed Description

template<class TImageType, class THistogramFrequencyContainer = DenseFrequencyContainer2>
class itk::Statistics::ScalarImageToTextureFeaturesFilter< 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, texure 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: http://www.fp.ucalgary.ca/mhallbey/tutorial.htm

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 Comaprison 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 106 of file itkScalarImageToTextureFeaturesFilter.h.