Computes the mutual information between two images to be registered. More...

#include <itkMutualInformationImageToImageMetric.h>

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

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

Classes
class	SpatialSample
Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef Superclass::DerivativeType	DerivativeType
typedef Superclass::FixedImageConstPointer	FixedImageConstPointer
typedef FixedImageType::IndexType	FixedImageIndexType
typedef FixedImageIndexType::IndexValueType	FixedImageIndexValueType
typedef TransformType::InputPointType	FixedImagePointType
typedef Superclass::FixedImageType	FixedImageType
typedef Superclass::InterpolatorType	InterpolatorType
typedef KernelFunctionBase < double >	KernelFunctionType
typedef Superclass::MeasureType	MeasureType
typedef Superclass::MovingImageConstPointer	MovingImageCosntPointer
typedef MovingImageType::IndexType	MovingImageIndexType
typedef TransformType::OutputPointType	MovingImagePointType
typedef Superclass::MovingImageType	MovingImageType
typedef Superclass::ParametersType	ParametersType
typedef SmartPointer< Self >	Pointer
typedef MutualInformationImageToImageMetric	Self
typedef ImageToImageMetric < TFixedImage, TMovingImage >	Superclass
typedef Superclass::TransformJacobianType	TransformJacobianType
typedef Superclass::TransformPointer	TransformPointer
typedef Superclass::TransformType	TransformType
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
void	GetDerivative (const ParametersType &parameters, DerivativeType &Derivative) const
virtual const char *	GetNameOfClass () const
virtual const unsigned int &	GetNumberOfSpatialSamples ()
MeasureType	GetValue (const ParametersType &parameters) const
void	GetValueAndDerivative (const ParametersType &parameters, MeasureType &Value, DerivativeType &Derivative) const
void	ReinitializeSeed ()
void	ReinitializeSeed (int)
void	SetNumberOfSpatialSamples (unsigned int num)

virtual void	SetMovingImageStandardDeviation (double _arg)
virtual const double &	GetMovingImageStandardDeviation ()

virtual void	SetFixedImageStandardDeviation (double _arg)
virtual double	GetFixedImageStandardDeviation () const

virtual void	SetKernelFunction (KernelFunctionType *_arg)
virtual KernelFunctionType *	GetKernelFunction ()
Static Public Member Functions
static Pointer	New ()
Static Public Attributes
static const unsigned int	MovingImageDimension = MovingImageType::ImageDimension
Protected Member Functions
	MutualInformationImageToImageMetric ()
void	PrintSelf (std::ostream &os, Indent indent) const
virtual	~MutualInformationImageToImageMetric ()
Private Types
typedef Superclass::CoordinateRepresentationType	CoordinateRepresentationType
typedef CentralDifferenceImageFunction < MovingImageType, CoordinateRepresentationType >	DerivativeFunctionType
typedef std::vector < SpatialSample >	SpatialSampleContainer
Private Member Functions
void	CalculateDerivatives (const FixedImagePointType &, DerivativeType &) const
	MutualInformationImageToImageMetric (const Self &)
void	operator= (const Self &)
virtual void	SampleFixedImageDomain (SpatialSampleContainer &samples) const
Private Attributes
DerivativeFunctionType::Pointer	m_DerivativeCalculator
double	m_FixedImageStandardDeviation
KernelFunctionType::Pointer	m_KernelFunction
double	m_MinProbability
double	m_MovingImageStandardDeviation
unsigned int	m_NumberOfSpatialSamples
int	m_RandomSeed
bool	m_ReseedIterator
SpatialSampleContainer	m_SampleA
SpatialSampleContainer	m_SampleB

Detailed Description

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

Computes the mutual information between two images to be registered.

MutualInformationImageToImageMetric computes the mutual information between a fixed and moving image to be registered.

This class is templated over the FixedImage type and the MovingImage type.

The fixed and moving images are set via methods SetFixedImage() and SetMovingImage(). This metric makes use of user specified Transform and Interpolator. The Transform is used to map points from the fixed image to the moving image domain. The Interpolator is used to evaluate the image intensity at user specified geometric points in the moving image. The Transform and Interpolator are set via methods SetTransform() and SetInterpolator().

Warning:: This metric assumes that the moving image has already been connected to the interpolator outside of this class.

The method GetValue() computes of the mutual information while method GetValueAndDerivative() computes both the mutual information and its derivatives with respect to the transform parameters.

The calculations are based on the method of Viola and Wells where the probability density distributions are estimated using Parzen windows.

By default a Gaussian kernel is used in the density estimation. Other option include Cauchy and spline-based. A user can specify the kernel passing in a pointer a KernelFunctionBase using the SetKernelFunction() method.

Mutual information is estimated using two sample sets: one to calculate the singular and joint pdf's and one to calculate the entropy integral. By default 50 samples points are used in each set. Other values can be set via the SetNumberOfSpatialSamples() method.

Quality of the density estimate depends on the choice of the kernel's standard deviation. Optimal choice will depend on the images. It is can be shown that around the optimal variance, the mutual information estimate is relatively insensitive to small changes of the standard deviation. In our experiments, we have found that a standard deviation of 0.4 works well for images normalized to have a mean of zero and standard deviation of 1.0. The variance can be set via methods SetFixedImageStandardDeviation() and SetMovingImageStandardDeviation().

Implementaton of this class is based on: Viola, P. and Wells III, W. (1997). "Alignment by Maximization of Mutual Information" International Journal of Computer Vision, 24(2):137-154

See also:: KernelFunctionBase; GaussianKernelFunction

Wiki Examples:

All Examples

Mutual Information

Mutual Information Affine

Definition at line 94 of file itkMutualInformationImageToImageMetric.h.