mitk::GIFNeighbouringGreyLevelDependenceFeature Class Reference

Calculates the Neighbouring Grey Level Dependence Features. More...

#include <mitkGIFNeighbouringGreyLevelDependenceFeatures.h>

Inheritance diagram for mitk::GIFNeighbouringGreyLevelDependenceFeature:

Collaboration diagram for mitk::GIFNeighbouringGreyLevelDependenceFeature:

Public Member Functions
	mitkClassMacro (GIFNeighbouringGreyLevelDependenceFeature, AbstractGlobalImageFeature)
Pointer	Clone () const
	GIFNeighbouringGreyLevelDependenceFeature ()
FeatureListType	CalculateFeatures (const Image *image, const Image *mask, const Image *maskNoNAN) override
	Calculate neighbouring grey level dependence features for the given image and mask. More...
virtual std::vector< double >	GetRanges () const
	Get the distance ranges for neighbourhood computation. More...
void	SetRanges (std::vector< double > ranges)
	Set multiple distance ranges for neighbourhood computation. More...
void	SetRange (double range)
	Set a single distance range for neighbourhood computation. More...
virtual int	GetAlpha () const
	Get the alpha coarseness parameter for dependence counting. More...
virtual void	SetAlpha (int _arg)
	Set the alpha coarseness parameter for dependence counting. More...
void	AddArguments (mitkCommandLineParser &parser) const override
	Add command line arguments for configuring this feature class. More...
Public Member Functions inherited from mitk::AbstractGlobalImageFeature
	mitkClassMacro (AbstractGlobalImageFeature, BaseData)
FeatureListType	CalculateFeatures (const Image *image, const Image *mask)
	Calculates the feature of this abstract interface. Does not necessarily considers the parameter settings. More...
FeatureListType	CalculateFeaturesSlicewise (const Image::Pointer &image, const Image::Pointer &mask, int sliceID)
	Calculates the given feature Slice-wise. Might not be available for an individual filter! More...
virtual void	CalculateAndAppendFeaturesSliceWise (const Image::Pointer &image, const Image::Pointer &mask, int sliceID, FeatureListType &featureList, bool checkParameterActivation=true)
	Calculates the feature of this abstract interface. Does not necessarily considers the parameter settings. More...
void	CalculateAndAppendFeatures (const Image *image, const Image *mask, const Image *maskNoNaN, FeatureListType &featureList, bool checkParameterActivation=true)
	Calculates the feature of this abstract interface. Does not necessarily considers the parameter settings. More...
virtual void	SetPrefix (std::string _arg)
	Set the prefix prepended to command line option names. More...
virtual void	SetShortName (std::string _arg)
	Set the short name used in option keys and feature names. More...
virtual void	SetLongName (std::string _arg)
	Set the long (human-readable) name of this feature class. More...
virtual void	SetFeatureClassName (std::string _arg)
	Set the feature class name used in FeatureID. More...
virtual void	SetDirection (int _arg)
	Set the direction index for directional feature computation. More...
void	SetParameters (ParametersType param)
	Set the parameter map and reconfigure the instance accordingly. More...
virtual std::string	GetPrefix () const
	Get the command line option prefix. More...
virtual std::string	GetShortName () const
	Get the short name of this feature class. More...
virtual std::string	GetLongName () const
	Get the long name of this feature class. More...
virtual std::string	GetFeatureClassName () const
	Get the feature class name. More...
virtual ParametersType	GetParameters () const
	Get the current parameter map. More...
virtual IntensityQuantifier::Pointer	GetQuantifier ()
	Get the IntensityQuantifier used for histogram-based feature computation. More...
virtual int	GetDirection () const
	Get the direction index. More...
virtual void	SetMinimumIntensity (double _arg)
	Set the minimum intensity for histogram initialization. More...
virtual void	SetUseMinimumIntensity (bool _arg)
	Set whether to use the explicit minimum intensity. More...
virtual void	SetMaximumIntensity (double _arg)
	Set the maximum intensity for histogram initialization. More...
virtual void	SetUseMaximumIntensity (bool _arg)
	Set whether to use the explicit maximum intensity. More...
virtual double	GetMinimumIntensity () const
	Get the minimum intensity for histogram initialization. More...
virtual bool	GetUseMinimumIntensity () const
	Get whether the explicit minimum intensity is used. More...
virtual double	GetMaximumIntensity () const
	Get the maximum intensity for histogram initialization. More...
virtual bool	GetUseMaximumIntensity () const
	Get whether the explicit maximum intensity is used. More...
virtual void	SetBinsize (double _arg)
	Set the histogram bin size. More...
virtual void	SetUseBinsize (bool _arg)
	Set whether to use the explicit bin size for histogram initialization. More...
virtual double	GetBinsize () const
	Get the histogram bin size. More...
virtual bool	GetUseBinsize () const
	Get whether the explicit bin size is used. More...
virtual void	SetMorphMask (mitk::Image::Pointer _arg)
	Set the morphological mask image used by some feature classes. More...
virtual mitk::Image::Pointer	GetMorphMask () const
	Get the morphological mask image. More...
virtual void	SetBins (int _arg)
	Set the number of histogram bins. More...
virtual void	SetUseBins (bool _arg)
	Set whether to use the explicit bin count for histogram initialization. More...
virtual bool	GetUseBins () const
	Get whether the explicit bin count is used. More...
virtual int	GetBins () const
	Get the number of histogram bins. More...
virtual void	SetIgnoreMask (bool _arg)
	Set whether to ignore the mask when computing histogram ranges. More...
virtual bool	GetIgnoreMask () const
	Get whether the mask is ignored for histogram range computation. More...
virtual void	SetEncodeParametersInFeaturePrefix (bool _arg)
	Set whether to encode parameter values in the feature name prefix. More...
virtual bool	GetEncodeParametersInFeaturePrefix () const
	Get whether parameter values are encoded in the feature name prefix. More...
virtual void	EncodeParametersInFeaturePrefixOn ()
	Toggle encoding of parameter values in the feature name prefix. More...
virtual void	EncodeParametersInFeaturePrefixOff ()
std::string	GetOptionPrefix () const
	Build the full option prefix string for command line parameters. More...
void	SetRequestedRegionToLargestPossibleRegion () override
	Set the RequestedRegion to the LargestPossibleRegion. More...
bool	RequestedRegionIsOutsideOfTheBufferedRegion () override
	Determine whether the RequestedRegion is outside of the BufferedRegion. More...
bool	VerifyRequestedRegion () override
	Verify that the RequestedRegion is within the LargestPossibleRegion. More...
void	SetRequestedRegion (const itk::DataObject *) override
	Set the requested region from this data object to match the requested region of the data object passed in as a parameter. More...
bool	IsEmpty () const override
	Check whether the object contains data at any time step. More...
Public Member Functions inherited from mitk::BaseData
	mitkClassMacroItkParent (BaseData, itk::DataObject)
BaseProperty::ConstPointer	GetConstProperty (const std::string &propertyKey, const std::string &contextName="", bool fallBackOnDefaultContext=true) const override
	Get a const property by its key. More...
std::vector< std::string >	GetPropertyKeys (const std::string &contextName="", bool includeDefaultContext=false) const override
	Get all property keys stored in the property list. More...
std::vector< std::string >	GetPropertyContextNames () const override
	Get the names of all property contexts. More...
BaseProperty *	GetNonConstProperty (const std::string &propertyKey, const std::string &contextName="", bool fallBackOnDefaultContext=true) override
	Get a non-const property by its key. More...
void	SetProperty (const std::string &propertyKey, BaseProperty *property, const std::string &contextName="", bool fallBackOnDefaultContext=false) override
	Set a property in the property list. More...
void	RemoveProperty (const std::string &propertyKey, const std::string &contextName="", bool fallBackOnDefaultContext=false) override
	Remove a property from the property list. More...
const mitk::TimeGeometry *	GetTimeGeometry () const
	Return the TimeGeometry of the data as const pointer. More...
mitk::TimeGeometry *	GetTimeGeometry ()
	Return the TimeGeometry of the data as non-const pointer. More...
const mitk::TimeGeometry *	GetUpdatedTimeGeometry ()
	Return the TimeGeometry of the data after ensuring it is up-to-date. More...
virtual void	Expand (unsigned int timeSteps)
	Expand the TimeGeometry to a number of time steps. More...
const mitk::BaseGeometry *	GetUpdatedGeometry (int t=0)
	Return the BaseGeometry of the data at time step t after ensuring it is up-to-date. More...
mitk::BaseGeometry *	GetGeometry (int t=0) const
	Return the BaseGeometry of the data at time step t as a non-const pointer. More...
void	UpdateOutputInformation () override
	Update the information for this BaseData so that it can be used as an output of a BaseProcess. More...
void	CopyInformation (const itk::DataObject *data) override
	Copy information from the specified data set. More...
virtual bool	IsInitialized () const
	Check whether the data has been initialized. More...
virtual void	Clear ()
	Reset the data object by calling ClearData() and InitializeEmpty(). More...
virtual bool	IsEmptyTimeStep (unsigned int t) const
	Check whether the object contains data at the specified time step. More...
void	ExecuteOperation (Operation *operation) override
	Execute an operation on this data object. More...
virtual void	SetGeometry (BaseGeometry *aGeometry3D)
	Set the BaseGeometry of the data, which will be referenced (not copied!). More...
virtual void	SetTimeGeometry (TimeGeometry *geometry)
	Set the TimeGeometry of the data, which will be referenced (not copied!). More...
virtual void	SetClonedGeometry (const BaseGeometry *aGeometry3D)
	Set a clone of the provided geometry as the BaseGeometry of the data. More...
virtual void	SetClonedTimeGeometry (const TimeGeometry *geometry)
	Set a clone of the provided TimeGeometry as the TimeGeometry of the data. More...
virtual void	SetClonedGeometry (const BaseGeometry *aGeometry3D, unsigned int time)
	Set a clone of the provided geometry as the BaseGeometry of a given time step. More...
mitk::PropertyList::Pointer	GetPropertyList () const
	Get the data's property list. More...
void	SetPropertyList (PropertyList *propertyList)
	Set the data's property list, replacing the existing one. More...
mitk::BaseProperty::Pointer	GetProperty (const char *propertyKey) const
	Get a property by its key from the PropertyList. More...
void	SetProperty (const char *propertyKey, BaseProperty *property)
	Set a property in the PropertyList. More...
virtual void	SetOrigin (const Point3D &origin)
	Convenience method for setting the origin of the BaseGeometry instances of all time steps. More...
itk::SmartPointer< mitk::BaseDataSource >	GetSource () const
	Get the process object that generated this data object. More...
unsigned int	GetTimeSteps () const
	Get the number of time steps from the TimeGeometry. More...
itk::ModifiedTimeType	GetMTime () const override
	Get the modification time of this data object or its geometry, whichever was modified most recently. More...
void	Graft (const DataObject *) override
	Graft data and information from another data object. More...
Public Member Functions inherited from mitk::OperationActor
	itkTypeMacroNoParent (OperationActor) virtual ~OperationActor()
Public Member Functions inherited from mitk::Identifiable
	Identifiable ()
	Default constructor. Generates a new random UID. More...
	Identifiable (const UIDType &uid)
	Construct with a pre-existing UID. More...
	Identifiable (const Identifiable &)=delete
	Copy construction is deleted to prevent UID duplication. More...
	Identifiable (Identifiable &&) noexcept
	Move constructor. Transfers ownership of the UID. More...
virtual	~Identifiable ()
	Virtual destructor. More...
Identifiable &	operator= (const Identifiable &)=delete
	Copy assignment is deleted to prevent UID duplication. More...
Identifiable &	operator= (Identifiable &&other) noexcept
	Move assignment operator. Transfers ownership of the UID. More...
virtual UIDType	GetUID () const
	Get the unique identifier of this object. More...
UIDType	GetRuntimeUID () const noexcept
	Get a runtime-unique ID for this object instance. More...
Public Member Functions inherited from mitk::IPropertyOwner
	~IPropertyOwner () override
virtual bool	PropertyIsOwned (const std::string &propertyKey, const std::string &contextName="", bool fallBackOnDefaultContext=true) const
	Checks if a certain property exists. More...
Public Member Functions inherited from mitk::IPropertyProvider
virtual	~IPropertyProvider ()
	Virtual destructor. More...

Static Public Member Functions
static Pointer	New ()
Static Public Member Functions inherited from mitk::AbstractGlobalImageFeature
static std::string	GenerateLegacyFeatureNameWOEncoding (const FeatureID &id)

Protected Member Functions
std::string	GenerateLegacyFeatureEncoding (const FeatureID &id) const override
FeatureListType	DoCalculateFeatures (const Image *image, const Image *mask) override
void	ConfigureSettingsByParameters (const ParametersType &parameters) override
Protected Member Functions inherited from mitk::AbstractGlobalImageFeature
std::vector< double >	SplitDouble (std::string str, char delimiter)
void	AddQuantifierArguments (mitkCommandLineParser &parser) const
void	ConfigureQuantifierSettingsByParameters ()
void	InitializeQuantifier (const Image *image, const Image *mask, unsigned int defaultBins=256)
std::string	QuantifierParameterString () const
FeatureID	CreateTemplateFeatureID (std::string settingsSuffix="", FeatureID::ParametersType additionalParams={})
virtual std::string	GenerateLegacyFeatureName (const FeatureID &id) const
virtual std::string	GenerateLegacyFeatureNamePart (const FeatureID &id) const
Protected Member Functions inherited from mitk::BaseData
	BaseData ()
	BaseData (const BaseData &other)
	~BaseData () override
virtual void	InitializeTimeGeometry (unsigned int timeSteps=1)
	Initialize the TimeGeometry for a number of time steps. The TimeGeometry is initialized empty and evenly timed. In many cases it will be necessary to overwrite this in sub-classes. More...
virtual void	ClearData ()
	reset to non-initialized state, release memory More...
virtual void	InitializeEmpty ()
	Pure virtual; Must be used in subclasses to get a data object to a valid state. Should at least create one empty object and call Superclass::InitializeTimeGeometry() to ensure an existing valid geometry. More...
void	PrintSelf (std::ostream &os, itk::Indent indent) const override
Protected Member Functions inherited from mitk::Identifiable
virtual void	SetUID (const UIDType &uid)

Additional Inherited Members
Public Types inherited from mitk::AbstractGlobalImageFeature
typedef std::vector< std::pair< FeatureID, double > >	FeatureListType
using	ParametersType = FeatureID::ParametersType
Public Types inherited from mitk::Identifiable
using	UIDType = std::string
	Type alias for unique identifiers. More...
Protected Attributes inherited from mitk::BaseData
bool	m_LastRequestedRegionWasOutsideOfTheBufferedRegion
unsigned int	m_SourceOutputIndexDuplicate
bool	m_Initialized

Detailed Description

Calculates the Neighbouring Grey Level Dependence Features.

The Neighbouring Grey Level Dependence Features were proposed by Sun and Wee (1983) and capture the coarsness of the image texture. They are rotational invariant.

The features are calculated on a matrix \( m \). To obtain the matrix, a neighbourhood around each feature is calculated and the number of voxels within the neighbourhood that are greater than the center voxel plus \( \alpha \) is counted. This is called the number of dependence voxels. The matrix gives the number of voxels with an intensity \( x \) and \( d \) dependence neighbourhood voxels.

The image is quantified prior to the calculation of the features. This reduces the number of available intensity values. Instead of using the pure intensity value, the features are calculated using the number of the bins as intensity value \( x_i \). The parameter of the quantification of the image can be controlled using the general binning parameters as defined in AbstractGlobalImageFeature.

By default, the calculation is based on a 26 neighbourhood for 3D and a 8 neighbourhood in 2D. It is further possible to exclude directions from the calculation, e.g. calculating the feature in 2D, even if a 3D image is passed. This is controlled by determine the dimensionality of the neighbourhood using direction-related commands as described in AbstractGlobalImageFeature.

In addition to this, the size of the neighbourhood can be controlled by setting the parameter ngld::range. By default it is one. To pass more than one range, separate the ranges with a semicolon. E.g. 1;2;3 would calculate the features for the ranges 1, 2, and 3.

This feature calculator is activated by the option -neighbouring-grey-level-dependence or -ngld.

The features are calculated based on a mask. It is assumed that the mask is a unsigned short image. All voxels with a value greater 0 are treated as masked.

Several values are defined for the definition of the features. \( N_v \) is the number of masked voxels, \(N_s \) is the number of neighbourhoods, \( m_{x,\cdot} = \sum_d m{x,d} \) is the number of neighbourhoods with a given intensity value, and likewise \( m_{\cdot, d} = \sum_x m{x,d} \) is the number of neighbourhoods with a given number of dependence features:

• Neighbouring Grey Level Dependence::Low Dependence Emphasis:

  \[ \textup{Low dependence emphasis}= \frac{1}{N_s} \sum_d { \frac{m_{\cdot, d}}{d^2} } \]

• Neighbouring Grey Level Dependence::High Dependence Emphasis:

  \[ \textup{High dependence emphasis}= \frac{1}{N_s} \sum_d { m_{\cdot, d} d^2} \]

• Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis:

  \[ \textup{Low grey level count emphasis}= \frac{1}{N_s} \sum_x { \frac{m_{x,\cdot}}{x^2} } \]

• Neighbouring Grey Level Dependence::High Grey Level Count Emphasis:

  \[ \textup{High grey level count emphasis}= \frac{1}{N_s} \sum_x { m_{x,\cdot} x^2} \]

• Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis:

  \[ \textup{Low Dependence Low Grey Level Emphasis}= \frac{1}{N_s} \sum_x \sum_d { \frac{m_{x,d}}{x^2 d^2} } \]

• Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis:

  \[ \textup{Low dependence high grey level emphasis}= \frac{1}{N_s} \sum_x \sum_d { \frac{x^2 m_{x,d}}{d^2} } \]

• Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis:

  \[ \textup{High Dependence Low Grey Level Emphasis}= \frac{1}{N_s} \sum_x \sum_d { \frac{d^2 m_{x,d}}{x^2} } \]

• Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis:

  \[ \textup{High dependence high grey level emphasis}= \frac{1}{N_s} \sum_x \sum_d { x^2 d^2 m_{x,d} } \]

• Neighbouring Grey Level Dependence::Grey level nonuniformity:

  \[ \textup{Grey level nonuniformity}= \frac{1}{N_s} \sum_x m_{x,\cdot}^2 \]

• Neighbouring Grey Level Dependence::Grey level nonuniformity normalized:

  \[ \textup{Grey level nonuniformity normalized}= \frac{1}{N_s^2} \sum_x m_{x,\cdot}^2 \]

• Neighbouring Grey Level Dependence::Dependence Count Nonuniformity:

  \[ \textup{Dependence count nonuniformity}= \frac{1}{N_s} \sum_d m_{\cdot, d}^2 \]

• Neighbouring Grey Level Dependence::Dependence Count Nonuniformity Normalized:

  \[ \textup{Dependence count nonuniformity normalized}= \frac{1}{N_s^2} \sum_d m_{\cdot, d}^2 \]

• Neighbouring Grey Level Dependence::DEpendence Count Percentage THe number of realized neighbourhoods relativ to the theoretical maximum of realized neighbourhoods. This feature is always one for this implementation as partial neighbourhoods are still considered.
• Neighbouring Grey Level Dependence::Grey Level Mean: The mean value of all grey level.

  \[ \textup{Grey Level Mean} = \mu_x = \frac{1}{N_s} \sum_x x m_{x,\cdot} \]

• Neighbouring Grey Level Dependence::Grey Level Variance:

  \[ \textup{Grey level variance} = \frac{1}{N_s} \sum_x (x -mu_x)^2 m_{x, \cdot} \]

• Neighbouring Grey Level Dependence::Dependence Count Mean: The mean value of all dependence counts.

  \[ \textup{Dependence count mean} = \mu_d = \frac{1}{N_s} \sum_d d m_{\cdot,d} \]

• Neighbouring Grey Level Dependence::Dependence Count Variance:

  \[ \textup{Dependence count variance} = \frac{1}{N_s} \sum_d (d -mu_d)^2 m_{\cdot, d} \]

• Neighbouring Grey Level Dependence::Dependence Count Entropy: This feature would be equivalent with the Grey Level Entropy, which is therefore not included. It is based on the likelihood for a given intensity- size combination \( p_{x,d} = \frac{m_{x,d}}{N_s} \). :

  \[ \textup{Dependence count entropy} = \sum_x \sum_d p_{x,d} \textup{log}_2 \left( p_{x,d} \right) \]

• Neighbouring Grey Level Dependence::Dependence Count Energy: This feature would be equivalent with the Grey Level Energy, which is therefore not included. It is based on the likelihood for a given intensity- size combination \( p_{x,d} = \frac{m_{x,d}}{N_s} \). :

  \[ \textup{Dependence count energy} = \sum_x \sum_d p_{x,d}^2 \]

• Neighbouring Grey Level Dependence::Expected Neighbourhood Size: The expected size of a full neighbourhood. It depends on the dimension of the area that is looked at.
• Neighbouring Grey Level Dependence::Average Neighbourhood Size: The feature calculation allows to consider partially masked neighbourhoods. Due to that, some neighbourhoods might be smaller. This feature gives not the theoretical neighbourhood size but the average realized neighbourhood sizes.
• Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size: Gives the average size of all neighbourhoods that are not complete.
• Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods: Gives the percentage of all complete neighbourhoods from all realized neighbourhoods.
• Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels: Gives the percentage of voxels in all neighbourhoods compared to the expected number of voxels.

Definition at line 116 of file mitkGIFNeighbouringGreyLevelDependenceFeatures.h.

Constructor & Destructor Documentation

GIFNeighbouringGreyLevelDependenceFeature()

mitk::GIFNeighbouringGreyLevelDependenceFeature::GIFNeighbouringGreyLevelDependenceFeature

(

)

Member Function Documentation

AddArguments()

void mitk::GIFNeighbouringGreyLevelDependenceFeature::AddArguments ( mitkCommandLineParser &  parser ) const

overridevirtual

Add command line arguments for configuring this feature class.

Parameters

[in,out]

parser

The command line parser to add arguments to.

Implements mitk::AbstractGlobalImageFeature.

CalculateFeatures()

FeatureListType mitk::GIFNeighbouringGreyLevelDependenceFeature::CalculateFeatures ( const Image *  image, const Image *  mask, const Image *  maskNoNAN  )

overridevirtual

Calculate neighbouring grey level dependence features for the given image and mask.

Parameters

[in]	image	The input intensity image.
[in]	mask	The binary mask defining the region of interest.
[in]	maskNoNAN	The mask with NaN voxels excluded.

Returns

A list of computed feature name-value pairs.

Implements mitk::AbstractGlobalImageFeature.

Clone()

Pointer mitk::GIFNeighbouringGreyLevelDependenceFeature::Clone

(

)

const

ConfigureSettingsByParameters()

void mitk::GIFNeighbouringGreyLevelDependenceFeature::ConfigureSettingsByParameters ( const ParametersType &  parameters )

overrideprotectedvirtual

Ensures that the instance is configured according to the information given in the passed parameters. This method will be called by SetParameters(...) after ConfigureQuantifierSettingsByParameters() was called.

Reimplemented from mitk::AbstractGlobalImageFeature.

DoCalculateFeatures()

FeatureListType mitk::GIFNeighbouringGreyLevelDependenceFeature::DoCalculateFeatures ( const Image *  image, const Image *  mask  )

overrideprotectedvirtual

Implements mitk::AbstractGlobalImageFeature.

GenerateLegacyFeatureEncoding()

std::string mitk::GIFNeighbouringGreyLevelDependenceFeature::GenerateLegacyFeatureEncoding ( const FeatureID &  id ) const

overrideprotectedvirtual

Reimplemented from mitk::AbstractGlobalImageFeature.

GetAlpha()

virtual int mitk::GIFNeighbouringGreyLevelDependenceFeature::GetAlpha ( ) const

virtual

Get the alpha coarseness parameter for dependence counting.

GetRanges()

virtual std::vector<double> mitk::GIFNeighbouringGreyLevelDependenceFeature::GetRanges ( ) const

virtual

Get the distance ranges for neighbourhood computation.

mitkClassMacro()

mitk::GIFNeighbouringGreyLevelDependenceFeature::mitkClassMacro	(	GIFNeighbouringGreyLevelDependenceFeature	,
		AbstractGlobalImageFeature
	)

New()

static Pointer mitk::GIFNeighbouringGreyLevelDependenceFeature::New ( )

static

SetAlpha()

virtual void mitk::GIFNeighbouringGreyLevelDependenceFeature::SetAlpha ( int  _arg )

virtual

Set the alpha coarseness parameter for dependence counting.

SetRange()

void mitk::GIFNeighbouringGreyLevelDependenceFeature::SetRange

(

double

range

)

Set a single distance range for neighbourhood computation.

Parameters

[in]

range

The neighbourhood distance (default: 1).

SetRanges()

void mitk::GIFNeighbouringGreyLevelDependenceFeature::SetRanges

(

std::vector< double >

ranges

)

Set multiple distance ranges for neighbourhood computation.

Parameters

[in]

ranges

Vector of neighbourhood distances.

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The documentation for this class was generated from the following file:

• mitkGIFNeighbouringGreyLevelDependenceFeatures.h