Computes similarity between two binary objects to be registered. More...

#include <itkKappaStatisticImageToImageMetric.h>

Inheritance diagram for itk::KappaStatisticImageToImageMetric< TFixedImage, TMovingImage >:

Collaboration diagram for itk::KappaStatisticImageToImageMetric< TFixedImage, TMovingImage >:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef Superclass::DerivativeType	DerivativeType
typedef Superclass::FixedImageConstPointer	FixedImageConstPointer
typedef Superclass::FixedImageRegionType	FixedImageRegionType
typedef Superclass::FixedImageType	FixedImageType
typedef Superclass::GradientImageType	GradientImageType
typedef Superclass::GradientPixelType	GradientPixelType
typedef Superclass::InputPointType	InputPointType
typedef Superclass::MeasureType	MeasureType
typedef Superclass::MovingImageConstPointer	MovingImageConstPointer
typedef Superclass::MovingImageType	MovingImageType
typedef Superclass::OutputPointType	OutputPointType
typedef SmartPointer< Self >	Pointer
typedef Superclass::RealType	RealType
typedef KappaStatisticImageToImageMetric	Self
typedef ImageToImageMetric < TFixedImage, TMovingImage >	Superclass
typedef Superclass::TransformJacobianType	TransformJacobianType
typedef Superclass::TransformParametersType	TransformParametersType
typedef Superclass::TransformPointer	TransformPointer
typedef Superclass::TransformType	TransformType
Public Member Functions
void	ComputeGradient ()
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
void	GetDerivative (const TransformParametersType &, DerivativeType &derivative) const
virtual const char *	GetNameOfClass () const
MeasureType	GetValue (const TransformParametersType &parameters) const
void	GetValueAndDerivative (const TransformParametersType &parameters, MeasureType &Value, DerivativeType &Derivative) const

virtual void	SetForegroundValue (RealType _arg)
virtual RealType	GetForegroundValue () const
Static Public Member Functions
static Pointer	New ()
Private Member Functions
	KappaStatisticImageToImageMetric (const Self &)
void	operator= (const Self &)
Private Attributes
bool	m_Complement
RealType	m_ForegroundValue
virtual void	SetComplement (bool _arg)
virtual void	ComplementOn ()
virtual void	ComplementOff ()
virtual bool	GetComplement () const
	KappaStatisticImageToImageMetric ()
virtual	~KappaStatisticImageToImageMetric ()
void	PrintSelf (std::ostream &os, Indent indent) const

Detailed Description

template<class TFixedImage, class TMovingImage>
class itk::KappaStatisticImageToImageMetric< TFixedImage, TMovingImage >

Computes similarity between two binary objects to be registered.

This Class is templated over the type of the fixed and moving images to be compared. The metric here is designed for matching pixels in two images with the same exact value. Only one value can be considered (the default is 255) and can be specified with the SetForegroundValue method. In the computation of the metric, only foreground pixels are considered. The metric value is given by 2*|A&B|/(|A|+|B|), where A is the foreground region in the moving image, B is the foreground region in the fixed image, & is intersection, and |.| indicates the area of the enclosed set. The metric is described in "Morphometric Analysis of White Matter Lesions in MR Images: Method and Validation", A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, A. C. Palmer.

This metric is especially useful when considering the similarity between binary images. Given the nature of binary images, a nearest neighbor interpolator is the preferred interpolator.

Metric values range from 0.0 (no foreground alignment) to 1.0 (perfect foreground alignment). When dealing with optimizers that can only minimize a metric, use the ComplementOn() method.

Definition at line 54 of file itkKappaStatisticImageToImageMetric.h.