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/*=========================================================================
*
* Copyright UMC Utrecht and contributors
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkAdvancedMeanSquaresImageToImageMetric_h
#define itkAdvancedMeanSquaresImageToImageMetric_h
#include "itkAdvancedImageToImageMetric.h"
namespace itk
{
/** \class AdvancedMeanSquaresImageToImageMetric
* \brief Compute Mean square difference between two images, based on AdvancedImageToImageMetric...
*
* This Class is templated over the type of the fixed and moving
* images to be compared.
*
* This metric computes the sum of squared differenced between pixels in
* the moving image and pixels in the fixed image. The spatial correspondance
* between both images is established through a Transform. Pixel values are
* taken from the Moving image. Their positions are mapped to the Fixed image
* and result in general in non-grid position on it. Values at these non-grid
* position of the Fixed image are interpolated using a user-selected Interpolator.
*
* This implementation of the MeanSquareDifference is based on the
* AdvancedImageToImageMetric, which means that:
* \li It uses the ImageSampler-framework
* \li It makes use of the compact support of B-splines, in case of B-spline transforms.
* \li Image derivatives are computed using either the B-spline interpolator's implementation
* or by nearest neighbor interpolation of a precomputed central difference image.
* \li A minimum number of samples that should map within the moving image (mask) can be specified.
*
* \ingroup RegistrationMetrics
* \ingroup Metrics
*/
template <class TFixedImage, class TMovingImage>
class ITK_TEMPLATE_EXPORT AdvancedMeanSquaresImageToImageMetric
: public AdvancedImageToImageMetric<TFixedImage, TMovingImage>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(AdvancedMeanSquaresImageToImageMetric);
/** Standard class typedefs. */
using Self = AdvancedMeanSquaresImageToImageMetric;
using Superclass = AdvancedImageToImageMetric<TFixedImage, TMovingImage>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(AdvancedMeanSquaresImageToImageMetric, AdvancedImageToImageMetric);
/** Typedefs from the superclass. */
using typename Superclass::CoordinateRepresentationType;
using typename Superclass::MovingImageType;
using typename Superclass::MovingImagePixelType;
using typename Superclass::MovingImageConstPointer;
using typename Superclass::FixedImageType;
using typename Superclass::FixedImageConstPointer;
using typename Superclass::FixedImageRegionType;
using typename Superclass::TransformType;
using typename Superclass::TransformPointer;
using typename Superclass::InputPointType;
using typename Superclass::OutputPointType;
using typename Superclass::TransformParametersType;
using typename Superclass::TransformJacobianType;
using typename Superclass::NumberOfParametersType;
using typename Superclass::InterpolatorType;
using typename Superclass::InterpolatorPointer;
using typename Superclass::RealType;
using typename Superclass::GradientPixelType;
using typename Superclass::GradientImageType;
using typename Superclass::GradientImagePointer;
using typename Superclass::FixedImageMaskType;
using typename Superclass::FixedImageMaskPointer;
using typename Superclass::MovingImageMaskType;
using typename Superclass::MovingImageMaskPointer;
using typename Superclass::MeasureType;
using typename Superclass::DerivativeType;
using typename Superclass::DerivativeValueType;
using typename Superclass::ParametersType;
using typename Superclass::FixedImagePixelType;
using typename Superclass::MovingImageRegionType;
using typename Superclass::ImageSamplerType;
using typename Superclass::ImageSamplerPointer;
using typename Superclass::ImageSampleContainerType;
using typename Superclass::ImageSampleContainerPointer;
using typename Superclass::FixedImageLimiterType;
using typename Superclass::MovingImageLimiterType;
using typename Superclass::FixedImageLimiterOutputType;
using typename Superclass::MovingImageLimiterOutputType;
using typename Superclass::MovingImageDerivativeScalesType;
using typename Superclass::ThreadInfoType;
/** The fixed image dimension. */
itkStaticConstMacro(FixedImageDimension, unsigned int, FixedImageType::ImageDimension);
/** The moving image dimension. */
itkStaticConstMacro(MovingImageDimension, unsigned int, MovingImageType::ImageDimension);
/** Get the value for single valued optimizers. */
virtual MeasureType
GetValueSingleThreaded(const TransformParametersType & parameters) const;
MeasureType
GetValue(const TransformParametersType & parameters) const override;
/** Get the derivatives of the match measure. */
void
GetDerivative(const TransformParametersType & parameters, DerivativeType & derivative) const override;
/** Get value and derivative. */
void
GetValueAndDerivativeSingleThreaded(const TransformParametersType & parameters,
MeasureType & value,
DerivativeType & derivative) const;
void
GetValueAndDerivative(const TransformParametersType & parameters,
MeasureType & value,
DerivativeType & derivative) const override;
/** Initialize the Metric by making sure that all the components
* are present and plugged together correctly.
* \li Call the superclass' implementation
* \li Estimate the normalization factor, if asked for. */
void
Initialize() override;
/** Set/Get whether to normalize the mean squares measure.
* This divides the MeanSquares by a factor (range/10)^2,
* where range represents the maximum gray value range of the
* images. Based on the ad hoc assumption that range/10 is the
* maximum average difference that will be observed.
* Dividing by range^2 sounds less ad hoc, but will yield
* very small values. */
itkSetMacro(UseNormalization, bool);
itkGetConstMacro(UseNormalization, bool);
protected:
AdvancedMeanSquaresImageToImageMetric();
~AdvancedMeanSquaresImageToImageMetric() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** Protected Typedefs ******************/
/** Typedefs inherited from superclass */
using typename Superclass::FixedImageIndexType;
using typename Superclass::FixedImageIndexValueType;
using typename Superclass::MovingImageIndexType;
using typename Superclass::FixedImagePointType;
using typename Superclass::MovingImagePointType;
using typename Superclass::MovingImageContinuousIndexType;
using typename Superclass::BSplineInterpolatorType;
using typename Superclass::MovingImageDerivativeType;
using typename Superclass::NonZeroJacobianIndicesType;
/** Compute a pixel's contribution to the measure and derivatives;
* Called by GetValueAndDerivative(). */
void
UpdateValueAndDerivativeTerms(const RealType fixedImageValue,
const RealType movingImageValue,
const DerivativeType & imageJacobian,
const NonZeroJacobianIndicesType & nzji,
MeasureType & measure,
DerivativeType & deriv) const;
/** Get value for each thread. */
void
ThreadedGetValue(ThreadIdType threadID) const override;
/** Gather the values from all threads. */
void
AfterThreadedGetValue(MeasureType & value) const override;
/** Get value and derivatives for each thread. */
void
ThreadedGetValueAndDerivative(ThreadIdType threadID) const override;
/** Gather the values and derivatives from all threads. */
void
AfterThreadedGetValueAndDerivative(MeasureType & value, DerivativeType & derivative) const override;
private:
double m_NormalizationFactor{ 1.0 };
bool m_UseNormalization{ false };
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkAdvancedMeanSquaresImageToImageMetric.hxx"
#endif
#endif // end #ifndef itkAdvancedMeanSquaresImageToImageMetric_h
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