Base class for fuzzy classifiers that take an image as input and produce a membership itk::VectorImage as output. More...

#include <itkFuzzyClassifierInitializationImageFilter.h>

Inheritance diagram for itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >:

Collaboration diagram for itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >:

Classes
struct	ThreadStruct
	Internal structure used for passing image data into the threading library. More...

Public Types
typedef std::vector< CentroidType >	CentroidArrayType
	Typdef for the array of centroids. More...

typedef itk::Vector< CentroidValueType, InputImagePixelDimension >	CentroidType
	Centroid typedef. More...

typedef NumericTraits< CentroidValueType >	CentroidValueNumericTraitsType
	Other centroid typedefs. More...

typedef TCentroidValuePrecision	CentroidValueType

typedef SmartPointer< const Self >	ConstPointer

typedef ImageRegionConstIterator< InputImageType >	InputImageConstIterator

typedef Superclass::InputImageConstPointer	InputImageConstPointer

typedef InputImageType::IndexType	InputImageIndexType

typedef NumericTraits< InputImagePixelType >	InputImagePixelNumericTraitsType
	Input image pixel typedefs. More...

typedef PixelTraits< InputImagePixelType >	InputImagePixelTraitsType

typedef Superclass::InputImagePixelType	InputImagePixelType

typedef InputImagePixelTraitsType::ValueType	InputImagePixelValueType

typedef Superclass::InputImagePointer	InputImagePointer
	Input image typedef support (types inherited from the superclass). More...

typedef Superclass::InputImageRegionType	InputImageRegionType

typedef InputImageType::SizeType	InputImageSizeType
	Other input image typedefs. More...

typedef TInputImage	InputImageType
	Convenient typedefs for simplifying declarations. More...

typedef ImageRegionConstIterator< InternalImageType >	InternalImageConstIterator

typedef InternalImageType::ConstPointer	InternalImageConstPointer

typedef ImageRegionIterator< InternalImageType >	InternalImageIterator
	Other internal image typedefs. More...

typedef InternalImageType::Pointer	InternalImagePointer

typedef Image< CentroidType, itkGetStaticConstMacro(InputImageDimension) >	InternalImageType
	Internal image typedef support. More...

typedef ImageRegionIterator< MembershipImageType >	MembershipImageIterator

typedef MembershipImageType::PixelType	MembershipImagePixelType

typedef MembershipImageType::Pointer	MembershipImagePointer

typedef OutputImageType	MembershipImageType
	Membership image typedef support. More...

typedef TProbabilityPrecision	MembershipValueType

typedef VectorImage< MembershipValueType, itkGetStaticConstMacro(InputImageDimension) >	OutputImageType
	Convenient typedefs for simplifying declarations. More...

typedef SmartPointer< Self >	Pointer

typedef FuzzyClassifierInitializationImageFilter	Self
	Standard class typedefs. More...

typedef ImageToImageFilter< InputImageType, OutputImageType >	Superclass
	Standard class typedefs. More...

Public Member Functions
virtual double	ComputeDifference (const CentroidArrayType &arrayOfCentroids)
	Computes the mean square error between centroids and the centroid array passed as parameter. More...

virtual CentroidArrayType	GetCentroids () const
	Get cluster centroids. More...

	itkBooleanMacro (IgnoreBackgroundPixels)
	Turn on and off the IgnoreBackgroundPixels flag. More...

	itkGetConstMacro (BackgroundPixel, InputImagePixelType)
	Get the pixel used as background value in the input image. More...

	itkGetConstMacro (Error, double)
	Get error between the new class centroids and their values from the previous iteration. More...

	itkGetConstMacro (IgnoreBackgroundPixels, bool)
	Get true/false depending on whether the algorithm ignores the background pixels or not. More...

	itkGetConstMacro (M, double)
	Get m fuzziness degree value. More...

	itkGetConstMacro (MaximumError, double)
	Get maximum error used to stop the algorithm. More...

	itkGetConstMacro (MaximumNumberOfIterations, unsigned int)
	Get maximum number of iterations. More...

	itkGetConstMacro (NumberOfClasses, unsigned int)
	Get the number of classes. More...

	itkGetConstMacro (NumberOfIterations, unsigned int)
	Get number of iterations performed. More...

	itkSetClampMacro (M, double, 1.0, NumericTraits< double >::max())
	Set m fuzziness degree value. More...

	itkSetClampMacro (MaximumError, double, 0.0, NumericTraits< double >::max())
	Set maximum error used to stop the algorithm. More...

	itkSetClampMacro (MaximumNumberOfIterations, unsigned int, 1, NumericTraits< unsigned int >::max())
	Set maximum number of iterations. More...

	itkSetMacro (BackgroundPixel, InputImagePixelType)
	Set the pixel used as background value in the input image. More...

	itkSetMacro (IgnoreBackgroundPixels, bool)
	Set true/false depending on whether the algorithm ignores the background pixels or not. More...

	itkStaticConstMacro (InputImageDimension, unsigned int, InputImageType::ImageDimension)
	Extract dimension from input image. More...

	itkStaticConstMacro (InputImagePixelDimension, unsigned int, InputImagePixelTraitsType::Dimension)
	Extract dimension from the pixel of the input image. More...

	itkTypeMacro (FuzzyClassifierInitializationImageFilter, ImageToImageFilter)
	Run-time type information (and related methods). More...

virtual void	SetCentroids (const CentroidArrayType newCentroids)
	Set class centroids. More...

virtual void	SetNumberOfClasses (const unsigned int &numOfClasses)
	Set the number of classes. More...

Protected Member Functions
void	ComputeImageToProcess (InternalImageType *imageToProcess)
	Get the internal image to process. More...

void	ComputePixelAsCentroid (const InputImagePixelType &pixel, CentroidType &pixelAsCentroid)
	Transforms a given pixel of the input image to the equivalent pixel of centroid type. More...

	FuzzyClassifierInitializationImageFilter ()
	Constructor. More...

void	GenerateData () override
	A version of GenerateData() specific for fuzzy classifiers. More...

void	GenerateOutputInformation () override
	Generate the information describing the output data. More...

virtual void	Initialize ()
	If a subclass of FuzzyClassifierInitializationImageFilter needs to initialize some data or perform some calculations before to start the first iteration, it should provide an implementation of Initialize(). More...

void	PrintSelf (std::ostream &os, Indent indent) const override
	Write the name-value pairs of the filter data members to the supplied output stream. More...

virtual	~FuzzyClassifierInitializationImageFilter ()
	Destructor. More...

Protected Attributes
InputImagePixelType	m_BackgroundPixel
	Pixel of the input image used as background value. More...

CentroidArrayType	m_Centroids
	Array of class centroids. More...

double	m_Error
	Error obtained between the new class centroids and the old values of the centroids (i.e. More...

bool	m_IgnoreBackgroundPixels
	Flag indicating whether to ignore the background pixels. More...

InternalImagePointer	m_ImageToProcess
	Pointer to the image to be used internally by the algorithm. More...

double	m_M
	Fuzzification parameter (m) in the range [1, n], which determines the degree of fuzziness in the clusters. More...

double	m_MaximumError
	Error threshold used to stop the algorithm. More...

unsigned int	m_MaximumNumberOfIterations
	Maximum number of iterations allowed. More...

unsigned int	m_NumberOfClasses
	Number of fuzzy clusters used to classify the image. More...

unsigned int	m_NumberOfIterations
	Number of iterations performed. More...

Detailed Description

template<class TInputImage, class TProbabilityPrecision = double, class TCentroidValuePrecision = double>
class itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >

Base class for fuzzy classifiers that take an image as input and produce a membership itk::VectorImage as output.

FuzzyClassifierInitializationImageFilter class provides the infrastructure for supporting multithread. This class provides a GenerateData() method based on the superclass (ImageSource) implementation. If a derived class provides an implementation of ThreadedGenerateData(), this implementation of GenerateData() allocates the output buffer and an image used internally. It also calls the method called Initialize(). This method is used by derived classes that need to initialize some data or perform some calculations before to start the first iteration. Then it performs the iterations of the fuzzy algorithm. At each iteration, the BeforeThreadedGenerateData() method is called (if provided). Then, the image is divided into a number of pieces and a number of threads are spawned each calling ThreadedGenerateData(). After all the threads have completed processing, the AfterThreadedGenerateData() method is called (if provided). To halt the iteration process the error (usually the difference between the new values of the centroids and the previous values) must lie below a determined threshold or the number of iteration must exceed the maximum number of iterations, otherwise a new iteration will start.

If a derived class provides an implementation of GenerateData() instead, the fuzzy algorithm will run in a single thread and the implementation is responsible for allocating its output data.

Inputs and Outputs: The input to this filter is the image to classify.

: The output of the filter is an itk::VectorImage, that represents pixel memberships to 'n' classes.

Template parameters: This filter is templated over the input image type, the data type used to represent the probabilities (defaults to double) and the data type used to represent the values of the centroids (defaults to double). Note that the precision types used to instantiate this filter should be real valued scalar types. In other words: doubles or floats.

Internal image: This class uses an internal image to speed up execution. This image is obtained by a numeric conversion of the pixels of the input image to the type of centroids. This will avoid doing a conversion each time the algorithm accesses the pixels.

Caveats: When using Iterators on the output vector image, you cannot use the it.Value(). You must use Set/Get() methods instead.

Version: 0.1

Author: Alberto Rey, Alfonso Castro and Bernardino Arcay. University of A Coruña. Spain

See also: FuzzyClassifierImageFilter; FCMClassifierInitializationImageFilter; KFCMSClassifierInitializationImageFilter; MSKFCMClassifierInitializationImageFilter; MSFKCMClassifierInitializationImageFilter; VectorImage

Member Typedef Documentation

◆ CentroidArrayType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef std::vector< CentroidType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::CentroidArrayType

Typdef for the array of centroids.

◆ CentroidType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef itk::Vector< CentroidValueType, InputImagePixelDimension > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::CentroidType

Centroid typedef.

The type of the components of the centroid is the template parameter TCentroidValuePrecision (i.e. CentroidValueType).

◆ CentroidValueNumericTraitsType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef NumericTraits< CentroidValueType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::CentroidValueNumericTraitsType

Other centroid typedefs.

◆ CentroidValueType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef TCentroidValuePrecision itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::CentroidValueType

◆ ConstPointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef SmartPointer< const Self > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::ConstPointer

◆ InputImageConstIterator

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef ImageRegionConstIterator< InputImageType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImageConstIterator

◆ InputImageConstPointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef Superclass::InputImageConstPointer itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImageConstPointer

◆ InputImageIndexType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef InputImageType::IndexType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImageIndexType

◆ InputImagePixelNumericTraitsType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef NumericTraits< InputImagePixelType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImagePixelNumericTraitsType

Input image pixel typedefs.

◆ InputImagePixelTraitsType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef PixelTraits< InputImagePixelType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImagePixelTraitsType

◆ InputImagePixelType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef Superclass::InputImagePixelType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImagePixelType

◆ InputImagePixelValueType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef InputImagePixelTraitsType::ValueType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImagePixelValueType

◆ InputImagePointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef Superclass::InputImagePointer itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImagePointer

Input image typedef support (types inherited from the superclass).

◆ InputImageRegionType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef Superclass::InputImageRegionType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImageRegionType

◆ InputImageSizeType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef InputImageType::SizeType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImageSizeType

Other input image typedefs.

◆ InputImageType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef TInputImage itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InputImageType

Convenient typedefs for simplifying declarations.

◆ InternalImageConstIterator

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef ImageRegionConstIterator< InternalImageType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InternalImageConstIterator

◆ InternalImageConstPointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef InternalImageType::ConstPointer itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InternalImageConstPointer

◆ InternalImageIterator

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef ImageRegionIterator< InternalImageType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InternalImageIterator

Other internal image typedefs.

◆ InternalImagePointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef InternalImageType::Pointer itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InternalImagePointer

◆ InternalImageType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef Image< CentroidType, itkGetStaticConstMacro(InputImageDimension) > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::InternalImageType

Internal image typedef support.

This image is used for internal processing. It's obtained by converting the pixels of the input image to the type of centroid.

◆ MembershipImageIterator

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef ImageRegionIterator< MembershipImageType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::MembershipImageIterator

◆ MembershipImagePixelType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef MembershipImageType::PixelType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::MembershipImagePixelType

◆ MembershipImagePointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef MembershipImageType::Pointer itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::MembershipImagePointer

◆ MembershipImageType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef OutputImageType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::MembershipImageType

Membership image typedef support.

The pixels of the vector image represent the membership of that pixel to each particular class. The pixels are arrays and the number of elements in the array is the same as the number of classes to be used.

◆ MembershipValueType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef TProbabilityPrecision itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::MembershipValueType

◆ OutputImageType

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef VectorImage< MembershipValueType, itkGetStaticConstMacro(InputImageDimension) > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::OutputImageType

Convenient typedefs for simplifying declarations.

◆ Pointer

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef SmartPointer< Self > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::Pointer

◆ Self

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef FuzzyClassifierInitializationImageFilter itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::Self

Standard class typedefs.

◆ Superclass

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

typedef ImageToImageFilter< InputImageType, OutputImageType > itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::Superclass

Standard class typedefs.

Constructor & Destructor Documentation

◆ FuzzyClassifierInitializationImageFilter()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::FuzzyClassifierInitializationImageFilter ( )

protected

Constructor.

◆ ~FuzzyClassifierInitializationImageFilter()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

virtual itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::~FuzzyClassifierInitializationImageFilter ( )

inlineprotectedvirtual

Destructor.

Member Function Documentation

◆ ComputeDifference()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

virtual double itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::ComputeDifference ( const CentroidArrayType & arrayOfCentroids )

virtual

Computes the mean square error between centroids and the centroid array passed as parameter.

◆ ComputeImageToProcess()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::ComputeImageToProcess ( InternalImageType * imageToProcess )

protected

Get the internal image to process.

This image will be used for internal processing and is obtained from the original input image by transforming its pixels to the type of centroids.

◆ ComputePixelAsCentroid()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::ComputePixelAsCentroid	(	const InputImagePixelType &	pixel,
		CentroidType &	pixelAsCentroid
	)

inlineprotected

Transforms a given pixel of the input image to the equivalent pixel of centroid type.

◆ GenerateData()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::GenerateData ( )

overrideprotected

A version of GenerateData() specific for fuzzy classifiers.

This implementation will split the processing across multiple threads. The buffer is allocated by this method. It also calls Initialize(). Then it performs the iterations of the fuzzy algorithm. At each iteration the BeforeThreadedGenerateData() method is called (if provided). Then, the image is divided into a number of pieces and a number of threads are spawned each calling ThreadedGenerateData(). After all the threads have completed processing, the AfterThreadedGenerateData() method is called (if provided). To halt the iteration process the error (i.e., the difference between the new values of the centroids and the previous values) must lie below a determined thresold or the number of iteration must exceed the maximum number of iterations, otherwise a new iteration will start. If a derived class cannot be threaded, that class should provide an implementation of GenerateData(). That implementation is responsible for allocating the output buffer. If a subclass can be threaded, it should NOT provide a GenerateData() method but should provide a ThreadedGenerateData() instead.

See also: ThreadedGenerateData()

◆ GenerateOutputInformation()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::GenerateOutputInformation ( )

overrideprotected

Generate the information describing the output data.

The default implementation of this method will copy information from the input to the output. The filter calls that superclass' implementation of this method and then it sets the length of the output image pixel (i.e., the array of memberships) to the number of classes.

◆ GetCentroids()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

virtual CentroidArrayType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::GetCentroids ( ) const

virtual

Get cluster centroids.

◆ Initialize()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

virtual void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::Initialize ( )

inlineprotectedvirtual

If a subclass of FuzzyClassifierInitializationImageFilter needs to initialize some data or perform some calculations before to start the first iteration, it should provide an implementation of Initialize().

Reimplemented in itk::MSKFCMClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >, and itk::KFCMSClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >.

◆ itkBooleanMacro()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkBooleanMacro ( IgnoreBackgroundPixels )

Turn on and off the IgnoreBackgroundPixels flag.

◆ itkGetConstMacro() [1/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	BackgroundPixel	,
		InputImagePixelType
	)

Get the pixel used as background value in the input image.

◆ itkGetConstMacro() [2/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	Error	,
		double
	)

Get error between the new class centroids and their values from the previous iteration.

◆ itkGetConstMacro() [3/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	IgnoreBackgroundPixels	,
		bool
	)

Get true/false depending on whether the algorithm ignores the background pixels or not.

◆ itkGetConstMacro() [4/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	M	,
		double
	)

Get m fuzziness degree value.

◆ itkGetConstMacro() [5/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	MaximumError	,
		double
	)

Get maximum error used to stop the algorithm.

◆ itkGetConstMacro() [6/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	MaximumNumberOfIterations	,
		unsigned int
	)

Get maximum number of iterations.

◆ itkGetConstMacro() [7/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	NumberOfClasses	,
		unsigned int
	)

Get the number of classes.

◆ itkGetConstMacro() [8/8]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkGetConstMacro	(	NumberOfIterations	,
		unsigned int
	)

Get number of iterations performed.

◆ itkSetClampMacro() [1/3]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkSetClampMacro	(	M	,
		double	,
		1.	0,
		NumericTraits< double >	::max()
	)

Set m fuzziness degree value.

This value should be greater than 1 .

◆ itkSetClampMacro() [2/3]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkSetClampMacro	(	MaximumError	,
		double	,
		0.	0,
		NumericTraits< double >	::max()
	)

Set maximum error used to stop the algorithm.

This error value can't be negative.

◆ itkSetClampMacro() [3/3]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkSetClampMacro	(	MaximumNumberOfIterations	,
		unsigned int	,
		1	,
		NumericTraits< unsigned int >	::max()
	)

Set maximum number of iterations.

This value should be positive and greater than 1.

◆ itkSetMacro() [1/2]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkSetMacro	(	BackgroundPixel	,
		InputImagePixelType
	)

Set the pixel used as background value in the input image.

The image pixels with this value will be ignored.

◆ itkSetMacro() [2/2]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkSetMacro	(	IgnoreBackgroundPixels	,
		bool
	)

Set true/false depending on whether the algorithm ignores the background pixels or not.

◆ itkStaticConstMacro() [1/2]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkStaticConstMacro	(	InputImageDimension	,
		unsigned int	,
		InputImageType::ImageDimension
	)

Extract dimension from input image.

◆ itkStaticConstMacro() [2/2]

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkStaticConstMacro	(	InputImagePixelDimension	,
		unsigned int	,
		InputImagePixelTraitsType::Dimension
	)

Extract dimension from the pixel of the input image.

◆ itkTypeMacro()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::itkTypeMacro	(	FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >	,
		ImageToImageFilter
	)

Run-time type information (and related methods).

◆ PrintSelf()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)		const

overrideprotected

Write the name-value pairs of the filter data members to the supplied output stream.

◆ SetCentroids()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

virtual void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::SetCentroids ( const CentroidArrayType newCentroids )

virtual

Set class centroids.

The number of centroids must equal the number of classes.

◆ SetNumberOfClasses()

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

virtual void itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::SetNumberOfClasses ( const unsigned int & numOfClasses )

virtual

Set the number of classes.

This method also sets the size of the vector of centroids. The user must supply it.

Reimplemented in itk::MSKFCMClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >, itk::KFCMSClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >, and itk::FCMClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >.

Member Data Documentation

◆ m_BackgroundPixel

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

InputImagePixelType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_BackgroundPixel

protected

Pixel of the input image used as background value.

◆ m_Centroids

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

CentroidArrayType itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_Centroids

protected

Array of class centroids.

◆ m_Error

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

double itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_Error

protected

Error obtained between the new class centroids and the old values of the centroids (i.e.

the values in the previous iteration). During the execution of the algorithm it represents the current error and it's updated at each iteration.

◆ m_IgnoreBackgroundPixels

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

bool itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_IgnoreBackgroundPixels

protected

Flag indicating whether to ignore the background pixels.

◆ m_ImageToProcess

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

InternalImagePointer itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_ImageToProcess

protected

Pointer to the image to be used internally by the algorithm.

This image is obtained by a numeric conversion of the pixels of the input image to the type of centroids.

◆ m_M

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

double itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_M

protected

Fuzzification parameter (m) in the range [1, n], which determines the degree of fuzziness in the clusters.

When m = 1 the effect is a crisp clustering of points. When m > 1 the degree of fuzziness among points in the decision space increases.

◆ m_MaximumError

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

double itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_MaximumError

protected

Error threshold used to stop the algorithm.

◆ m_MaximumNumberOfIterations

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

unsigned int itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_MaximumNumberOfIterations

protected

Maximum number of iterations allowed.

◆ m_NumberOfClasses

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

unsigned int itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_NumberOfClasses

protected

Number of fuzzy clusters used to classify the image.

The number of classes will determine the number of membership images that will be generated. The user must specify the number of expected classes before calling the classifier.

◆ m_NumberOfIterations

template<class TInputImage , class TProbabilityPrecision = double, class TCentroidValuePrecision = double>

unsigned int itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >::m_NumberOfIterations

protected

Number of iterations performed.

During the execution of the algorithm it represents the current iteration.

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

Toolkit/FuzzyCMeans/itkFuzzyClassifierInitializationImageFilter.h

Classes

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<class TInputImage, class TProbabilityPrecision = double, class TCentroidValuePrecision = double> class itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >

Member Typedef Documentation

◆ CentroidArrayType

◆ CentroidType

◆ CentroidValueNumericTraitsType

◆ CentroidValueType

◆ ConstPointer

◆ InputImageConstIterator

◆ InputImageConstPointer

◆ InputImageIndexType

◆ InputImagePixelNumericTraitsType

◆ InputImagePixelTraitsType

◆ InputImagePixelType

◆ InputImagePixelValueType

◆ InputImagePointer

◆ InputImageRegionType

◆ InputImageSizeType

◆ InputImageType

◆ InternalImageConstIterator

◆ InternalImageConstPointer

◆ InternalImageIterator

◆ InternalImagePointer

◆ InternalImageType

◆ MembershipImageIterator

◆ MembershipImagePixelType

◆ MembershipImagePointer

◆ MembershipImageType

◆ MembershipValueType

◆ OutputImageType

◆ Pointer

◆ Self

◆ Superclass

Constructor & Destructor Documentation

◆ FuzzyClassifierInitializationImageFilter()

◆ ~FuzzyClassifierInitializationImageFilter()

Member Function Documentation

◆ ComputeDifference()

◆ ComputeImageToProcess()

◆ ComputePixelAsCentroid()

◆ GenerateData()

◆ GenerateOutputInformation()

◆ GetCentroids()

◆ Initialize()

◆ itkBooleanMacro()

◆ itkGetConstMacro() [1/8]

◆ itkGetConstMacro() [2/8]

◆ itkGetConstMacro() [3/8]

◆ itkGetConstMacro() [4/8]

◆ itkGetConstMacro() [5/8]

◆ itkGetConstMacro() [6/8]

◆ itkGetConstMacro() [7/8]

◆ itkGetConstMacro() [8/8]

◆ itkSetClampMacro() [1/3]

◆ itkSetClampMacro() [2/3]

◆ itkSetClampMacro() [3/3]

◆ itkSetMacro() [1/2]

◆ itkSetMacro() [2/2]

◆ itkStaticConstMacro() [1/2]

◆ itkStaticConstMacro() [2/2]

◆ itkTypeMacro()

◆ PrintSelf()

◆ SetCentroids()

◆ SetNumberOfClasses()

Member Data Documentation

◆ m_BackgroundPixel

◆ m_Centroids

◆ m_Error

◆ m_IgnoreBackgroundPixels

◆ m_ImageToProcess

◆ m_M

◆ m_MaximumError

◆ m_MaximumNumberOfIterations

◆ m_NumberOfClasses

◆ m_NumberOfIterations

template<class TInputImage, class TProbabilityPrecision = double, class TCentroidValuePrecision = double>
class itk::FuzzyClassifierInitializationImageFilter< TInputImage, TProbabilityPrecision, TCentroidValuePrecision >