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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkShapePriorMAPCostFunction_h
#define itkShapePriorMAPCostFunction_h
#include "itkShapePriorMAPCostFunctionBase.h"
#include "itkGaussianKernelFunction.h"
namespace itk
{
/** \class ShapePriorMAPCostFunction
* \brief Represents the maximum aprior (MAP) cost function used
* ShapePriorSegmentationLevelSetImageFilter to estimate the shape parameters.
*
* This class follows the shape and pose parameters estimation developed in [1].
* Note that this class returns the negative log of the MAP function.
* Using the negative function make this cost function compatible
* with generic optimizers which seeks the minimum of a cost function.
*
* This class has two template parameters, the feature image type representing the
* edge potential map and the pixel type used to
* represent the output level set in the ShapePriorSegmentationLevelSetImageFilter.
*
* \sa ShapePriorSegmentationLevelSetImageFilter
*
* \par REFERENCES
* \par
* [1] Leventon, M.E. et al. "Statistical Shape Influence in Geodesic Active Contours", CVPR 2000.
*
*
* \ingroup Numerics Optimizers
* \ingroup ITKLevelSets
*/
template <typename TFeatureImage, typename TOutputPixel>
class ITK_TEMPLATE_EXPORT ShapePriorMAPCostFunction : public ShapePriorMAPCostFunctionBase<TFeatureImage, TOutputPixel>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(ShapePriorMAPCostFunction);
/** Standard class type aliases. */
using Self = ShapePriorMAPCostFunction;
using Superclass = ShapePriorMAPCostFunctionBase<TFeatureImage, TOutputPixel>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(ShapePriorMAPCostFunction);
/** ParametersType type alias.
* It defines a position in the optimization search space. */
using typename Superclass::ParametersType;
/** Type of the feature image representing the edge potential map. */
using typename Superclass::FeatureImageType;
using typename Superclass::FeatureImagePointer;
/** Type of the return measure value. */
using typename Superclass::MeasureType;
/** Dimension constant. */
static constexpr unsigned int ImageDimension = TFeatureImage::ImageDimension;
/** Type of pixel used to represent the level set. */
using typename Superclass::PixelType;
/** Type of node used to represent the active region around the zero set. */
using typename Superclass::NodeType;
/** Type of container used to store the level set nodes. */
using typename Superclass::NodeContainerType;
/** Type of the shape signed distance function. */
using typename Superclass::ShapeFunctionType;
/** Type of the array for storing shape parameter mean and standard deviation.
* FIXME: should be templated.
*/
using ArrayType = Array<double>;
/** Set/Get the array of shape parameters mean. */
itkSetMacro(ShapeParameterMeans, ArrayType);
itkGetConstMacro(ShapeParameterMeans, ArrayType);
/** Set/Get the array of shape parameters standard deviation. */
itkSetMacro(ShapeParameterStandardDeviations, ArrayType);
itkGetConstMacro(ShapeParameterStandardDeviations, ArrayType);
/** Set/Get the weights for each term. Default is a vector of all ones.
* The weights are applied to terms in the following order:
* LogInsideTerm, LogGradientTerm, LogShapePriorTerm and
* LogPosePriorTerm. */
using WeightsType = FixedArray<double, 4>;
itkSetMacro(Weights, WeightsType);
itkGetConstReferenceMacro(Weights, WeightsType);
/** Compute the inside term component of the MAP cost function.
* In particular, the method sums the number of pixels inside
* the current contour (defined by nodes of the active region
* that are less than zero) which are outside the shape
* specified by the input parameters. */
MeasureType
ComputeLogInsideTerm(const ParametersType & parameters) const override;
/** Compute the gradient term component of the MAP cost function.
* In particular, this method assume that ( 1 - FeatureImage ) approximates
* a Gaussian (zero mean, unit variance) along the normal of the evolving contour.
* The gradient term is then given by a Laplacian of the goodness of fit of
* the Gaussian. */
MeasureType
ComputeLogGradientTerm(const ParametersType & parameters) const override;
/** Compute the shape prior component of the MAP cost function.
* In particular, the method assumes that the shape parameters comes from
* independent Gaussian distributions defined by the ShapeParameterMeans
* and ShapeParameterVariances array. */
MeasureType
ComputeLogShapePriorTerm(const ParametersType & parameters) const override;
/** Compute the pose prior component of the MAP cost function.
* In particular, this method assumes that the pose parameters are
* uniformly distributed and returns a constant of zero. */
MeasureType
ComputeLogPosePriorTerm(const ParametersType & parameters) const override;
/** Initialize the cost function by making sure that all the components
* are present. */
void
Initialize() override;
protected:
ShapePriorMAPCostFunction();
~ShapePriorMAPCostFunction() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
private:
ArrayType m_ShapeParameterMeans{};
ArrayType m_ShapeParameterStandardDeviations{};
WeightsType m_Weights{};
typename GaussianKernelFunction<double>::Pointer m_GaussianFunction{};
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkShapePriorMAPCostFunction.hxx"
#endif
#endif
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