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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 elxVarianceOverLastDimensionMetric_h
#define elxVarianceOverLastDimensionMetric_h
#include "elxIncludes.h" // include first to avoid MSVS warning
#include "itkVarianceOverLastDimensionImageMetric.h"
#include "itkAdvancedBSplineDeformableTransform.h"
#include "itkStackTransform.h"
namespace elastix
{
/** \class VarianceOverLastDimensionMetric
* \brief Compute the sum of variances over the slowest varying dimension in the moving image.
*
* For a description of this metric see the paper:\n
* <em>Nonrigid registration of dynamic medical imaging data using
* nD+t B-splines and a groupwise optimization approach</em>,
* C.T. Metz, S. Klein, M. Schaap, T. van Walsum and W.J. Niessen,
* Medical Image Analysis, in press.
*
* This metric is based on the AdvancedImageToImageMetric.
* It is templated over the type of the fixed and moving images to be compared.
*
* This metric computes the sum of variances over the slowest varying dimension in
* the moving image. The spatial positions of the moving image are established
* through a Transform. Pixel values are taken from the Moving image.
*
* This implementation 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.
*
* \parameter SampleLastDimensionRandomly: randomly sample a number of time points to
* to compute the variance from. When set to "false", all time points are taken into
* account. When set to "true", a random number of time points is selected, which can
* be set with parameter NumSamplesLastDimension. \n
* \parameter NumSamplesLastDimension: the number of random samples to take in the time
* time direction of the data when SampleLastDimensionRandomly is set to true.
* \parameter UseZeroAverageDisplacementConstraint: uses the zero average displacement constraint, as described in
* <em>Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization
* approach</em>, Metz et al., Medical Image Analysis, 2011. Subtract the over time computed mean parameter value
* from each parameter. This should be used when registration is performed directly on the moving image, without
* using a fixed image. Possible values are "true" or "false". Default is "true".
*
* \ingroup RegistrationMetrics
* \ingroup Metrics
*/
template <class TElastix>
class ITK_TEMPLATE_EXPORT VarianceOverLastDimensionMetric
: public itk::VarianceOverLastDimensionImageMetric<typename MetricBase<TElastix>::FixedImageType,
typename MetricBase<TElastix>::MovingImageType>
, public MetricBase<TElastix>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(VarianceOverLastDimensionMetric);
/** Standard ITK-stuff. */
using Self = VarianceOverLastDimensionMetric;
using Superclass1 = itk::VarianceOverLastDimensionImageMetric<typename MetricBase<TElastix>::FixedImageType,
typename MetricBase<TElastix>::MovingImageType>;
using Superclass2 = MetricBase<TElastix>;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(VarianceOverLastDimensionMetric, itk::VarianceOverLastDimensionImageMetric);
/** Name of this class.
* Use this name in the parameter file to select this specific metric. \n
* example: <tt>(Metric "VarianceOverLastDimensionMetric")</tt>\n
*/
elxClassNameMacro("VarianceOverLastDimensionMetric");
/** Typedefs from the superclass. */
using typename Superclass1::CoordinateRepresentationType;
using typename Superclass1::ScalarType;
using typename Superclass1::MovingImageType;
using typename Superclass1::MovingImagePixelType;
using typename Superclass1::MovingImageConstPointer;
using typename Superclass1::FixedImageType;
using typename Superclass1::FixedImageConstPointer;
using typename Superclass1::FixedImageRegionType;
using typename Superclass1::FixedImageSizeType;
using typename Superclass1::TransformType;
using typename Superclass1::TransformPointer;
using typename Superclass1::InputPointType;
using typename Superclass1::OutputPointType;
using typename Superclass1::TransformParametersType;
using typename Superclass1::TransformJacobianType;
using typename Superclass1::InterpolatorType;
using typename Superclass1::InterpolatorPointer;
using typename Superclass1::RealType;
using typename Superclass1::GradientPixelType;
using typename Superclass1::GradientImageType;
using typename Superclass1::GradientImagePointer;
using typename Superclass1::FixedImageMaskType;
using typename Superclass1::FixedImageMaskPointer;
using typename Superclass1::MovingImageMaskType;
using typename Superclass1::MovingImageMaskPointer;
using typename Superclass1::MeasureType;
using typename Superclass1::DerivativeType;
using typename Superclass1::ParametersType;
using typename Superclass1::FixedImagePixelType;
using typename Superclass1::MovingImageRegionType;
using typename Superclass1::ImageSamplerType;
using typename Superclass1::ImageSamplerPointer;
using typename Superclass1::ImageSampleContainerType;
using typename Superclass1::ImageSampleContainerPointer;
using typename Superclass1::FixedImageLimiterType;
using typename Superclass1::MovingImageLimiterType;
using typename Superclass1::FixedImageLimiterOutputType;
using typename Superclass1::MovingImageLimiterOutputType;
using typename Superclass1::MovingImageDerivativeScalesType;
/** The fixed image dimension. */
itkStaticConstMacro(FixedImageDimension, unsigned int, FixedImageType::ImageDimension);
/** The moving image dimension. */
itkStaticConstMacro(MovingImageDimension, unsigned int, MovingImageType::ImageDimension);
/** Typedef's inherited from Elastix. */
using typename Superclass2::ElastixType;
using typename Superclass2::RegistrationType;
using ITKBaseType = typename Superclass2::ITKBaseType;
/** Typedef's for the B-spline transform. */
using BSplineTransformBaseType = itk::AdvancedBSplineDeformableTransformBase<ScalarType, FixedImageDimension>;
using CombinationTransformType = itk::AdvancedCombinationTransform<ScalarType, FixedImageDimension>;
using StackTransformType = itk::StackTransform<ScalarType, FixedImageDimension, MovingImageDimension>;
using ReducedDimensionBSplineTransformBaseType =
itk::AdvancedBSplineDeformableTransformBase<ScalarType, FixedImageDimension - 1>;
/** Sets up a timer to measure the initialization time and
* calls the Superclass' implementation.
*/
void
Initialize() override;
/**
* Do some things before registration:
* \li check the direction cosines
*/
void
BeforeRegistration() override;
/**
* Do some things before each resolution:
* \li Set CheckNumberOfSamples setting
* \li Set UseNormalization setting
*/
void
BeforeEachResolution() override;
protected:
/** The constructor. */
VarianceOverLastDimensionMetric() = default;
/** The destructor. */
~VarianceOverLastDimensionMetric() override = default;
private:
elxOverrideGetSelfMacro;
};
} // end namespace elastix
#ifndef ITK_MANUAL_INSTANTIATION
# include "elxVarianceOverLastDimensionMetric.hxx"
#endif
#endif // end #ifndef elxVarianceOverLastDimensionMetric_h
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