#include <itkScalarImageToCooccurrenceMatrixFilter.h>

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

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

List of all members.

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef HistogramType::ConstPointer	HistogramConstPointer
typedef HistogramType::Pointer	HistogramPointer
typedef Histogram < MeasurementType, THistogramFrequencyContainer >	HistogramType
typedef ImageType::ConstPointer	ImageConstPointer
typedef ImageType::Pointer	ImagePointer
typedef TImageType	ImageType
typedef NumericTraits < PixelType >::RealType	MeasurementType
typedef HistogramType::MeasurementVectorType	MeasurementVectorType
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 ImageType::SizeType	RadiusType
typedef ImageType::RegionType	RegionType
typedef ScalarImageToCooccurrenceMatrixFilter	Self
typedef ProcessObject	Superclass
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
const ImageType *	GetInput () const
const ImageType *	GetMaskImage () const
virtual PixelType	GetMax () const
virtual PixelType	GetMin () const
virtual const char *	GetNameOfClass () const
const HistogramType *	GetOutput () const
void	SetInput (const ImageType *image)
void	SetMaskImage (const ImageType *image)
void	SetPixelValueMinMax (PixelType min, PixelType max)

virtual void	SetOffsets (const OffsetVector *_arg)
virtual const OffsetVector *	GetOffsets ()
void	SetOffset (const OffsetType offset)

virtual void	SetNumberOfBinsPerAxis (unsigned int _arg)
virtual unsigned int	GetNumberOfBinsPerAxis () const

virtual void	SetNormalize (bool _arg)
virtual bool	GetNormalize () const
virtual void	NormalizeOn ()
virtual void	NormalizeOff ()
Static Public Member Functions
static Pointer	New ()
Static Public Attributes
static const unsigned int	DefaultBinsPerAxis = 256
Protected Types
typedef DataObject::Pointer	DataObjectPointer
typedef ProcessObject::DataObjectPointerArraySizeType	DataObjectPointerArraySizeType
Protected Member Functions
virtual void	FillHistogram (RadiusType radius, RegionType region)
virtual void	FillHistogramWithMask (RadiusType radius, RegionType region, const ImageType *maskImage)
virtual void	GenerateData ()
virtual DataObjectPointer	MakeOutput (DataObjectPointerArraySizeType idx)
Private Member Functions
void	NormalizeHistogram (void)
void	operator= (const Self &)
	ScalarImageToCooccurrenceMatrixFilter (const Self &)
Private Attributes
PixelType	m_InsidePixelValue
MeasurementVectorType	m_LowerBound
PixelType	m_Max
PixelType	m_Min
bool	m_Normalize
unsigned int	m_NumberOfBinsPerAxis
OffsetVectorConstPointer	m_Offsets
MeasurementVectorType	m_UpperBound
virtual void	SetInsidePixelValue (PixelType _arg)
virtual PixelType	GetInsidePixelValue () const
	ScalarImageToCooccurrenceMatrixFilter ()
virtual	~ScalarImageToCooccurrenceMatrixFilter ()
void	PrintSelf (std::ostream &os, Indent indent) const

Detailed Description

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

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.

This filters creates a grey-level co-occurence matrix from a N-D scalar image. This is the first step in texture description a la Haralick. (See 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. See also Haralick, R.M. 1979. Statistical and Structural Approaches to Texture. Proceedings of the IEEE, 67:786-804.)

The basic idea is as follows: Given an image and an offset (e.g. (1, -1) for a 2-d image), grey-level co-occurences are pairs of intensity values for a specific pixel and the pixel at that offset from the specified pixel. These co-occurences can provide information about the visual texture of an image region -- for example, an eight-bit image of alternating pixel-wide white and black vertical lines would have a large number of (0, 255) and (255, 0) co-occurences for offset (1, 0).

The offset (or offsets) along which the co-occurences are calculated can be set by the user. Traditionally, only one offset is used per histogram, and offset components in the range [-1, 1] are used. For rotation-invariant features, averages of features computed over several histograms with different offsets are generally used, instead of computing features from one histogram created with several offsets. Additionally, instead of using offsets of two or more pixels in any direction, multy-resulution techniques (e.g. image pyramids) are generally used to deal with texture at different spatial resolutions.

This class calculates a 2-d histogram of all the co-occurence pairs in the given image's requested region, for a given set of offsets. That is, if a given offset falls outside of the requested region at a particular point, that co-occurrence pair will not be added to the matrix.

The number of histogram bins on each axis can be set (defaults to 256). Also, by default the histogram min and max corresponds to the largest and smallest possible pixel value of that pixel type. To customize the histogram bounds for a given image, the max and min pixel values that will be placed in the histogram can be set manually. NB: The min and max are INCLUSIVE.

Further, the type of histogram frequency container used is an optional template parameter. By default, a dense container is used, but for images with little texture or in cases where the user wants more histogram bins, a sparse container can be used for the histogram instead.

WARNING: This probably won't work for pixels of double or long-double type unless you set the histogram min and max manually. This is because the largest histogram bin by default has max value of the largest possible pixel value plus 1. For double and long-double types, whose "RealType" as defined by the NumericTraits class is the same, and thus cannot hold any larger values, this would cause a float overflow.

See also:: MaskedScalarImageToCooccurrenceMatrixFilter; HistogramToTextureFeaturesFilter; ScalarImageTextureCalculator

Author:: Zachary Pincus and Glenn Pierce

Definition at line 94 of file itkScalarImageToCooccurrenceMatrixFilter.h.