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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 itkShapeDetectionLevelSetImageFilter_h
#define itkShapeDetectionLevelSetImageFilter_h
#include "itkSegmentationLevelSetImageFilter.h"
#include "itkShapeDetectionLevelSetFunction.h"
namespace itk
{
/** \class ShapeDetectionLevelSetImageFilter
* \brief Segments structures in images based on a user supplied edge potential map.
*
* \par IMPORTANT
* The SegmentationLevelSetImageFilter class and the
* ShapeDetectionLevelSetFunction class contain additional information necessary
* to gain full understanding of how to use this filter.
*
* \par OVERVIEW
* This class is a level set method segmentation filter. An initial contour
* is propagated outwards (or inwards) until it ''sticks'' to the shape boundaries.
* This is done by using a level set speed function based on a user supplied
* edge potential map. This approach for segmentation follows that of
* Malladi et al (1995).
*
* \par INPUTS
* This filter requires two inputs. The first input is a initial level set.
* The initial level set is a real image which contains the initial contour/surface
* as the zero level set. For example, a signed distance function from the initial
* contour/surface is typically used. Note that for this algorithm the initial contour
* has to be wholly within (or wholly outside) the structure to be segmented.
*
* \par
* The second input is the feature image. For this filter, this is the edge
* potential map. General characteristics of an edge potential map is that
* it has values close to zero in regions near the edges and values close
* to one inside the shape itself. Typically, the edge potential map is compute
* from the image gradient, for example:
*
* \f[ g(I) = 1 / ( 1 + | (\nabla * G)(I)| ) \f]
* \f[ g(I) = \exp^{-|(\nabla * G)(I)|} \f]
*
* where \f$ I \f$ is image intensity and
* \f$ (\nabla * G) \f$ is the derivative of Gaussian operator.
*
* \par
* See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter
* for more information on Inputs.
*
* \par PARAMETERS
* The PropagationScaling parameter can be used to switch from propagation outwards
* (POSITIVE scaling parameter) versus propagating inwards (NEGATIVE scaling
* parameter).
*
* The smoothness of the resulting contour/surface can be adjusted using a combination
* of PropagationScaling and CurvatureScaling parameters. The larger the CurvatureScaling
* parameter, the smoother the resulting contour. The CurvatureScaling parameter should
* be non-negative for proper operation of this algorithm.
* To follow the implementation in Malladi et al paper,
* set the PropagationScaling to \f$\pm 1.0\f$ and CurvatureScaling to \f$ \epsilon \f$.
*
* Note that there is no advection term for this filter. Setting the
* advection scaling will have no effect.
*
* \par OUTPUTS
* The filter outputs a single, scalar, real-valued image.
* Negative values in the output image represent the inside of the segmented region
* and positive values in the image represent the outside of the segmented region. The
* zero crossings of the image correspond to the position of the propagating
* front.
*
* \par
* See SparseFieldLevelSetImageFilter and
* SegmentationLevelSetImageFilter for more information.
*
* \par REFERENCES
* \par
* "Shape Modeling with Front Propagation: A Level Set Approach",
* R. Malladi, J. A. Sethian and B. C. Vermuri.
* IEEE Trans. on Pattern Analysis and Machine Intelligence,
* Vol 17, No. 2, pp 158-174, February 1995
*
* \sa SegmentationLevelSetImageFilter
* \sa ShapeDetectionLevelSetFunction
* \sa SparseFieldLevelSetImageFilter
*
* \ingroup LevelSetSegmentation
* \ingroup ITKLevelSets
*/
template <typename TInputImage, typename TFeatureImage, typename TOutputPixelType = float>
class ITK_TEMPLATE_EXPORT ShapeDetectionLevelSetImageFilter
: public SegmentationLevelSetImageFilter<TInputImage, TFeatureImage, TOutputPixelType>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(ShapeDetectionLevelSetImageFilter);
/** Standard class type aliases */
using Self = ShapeDetectionLevelSetImageFilter;
using Superclass = SegmentationLevelSetImageFilter<TInputImage, TFeatureImage, TOutputPixelType>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Inherited type alias from the superclass. */
using typename Superclass::ValueType;
using typename Superclass::OutputImageType;
using typename Superclass::FeatureImageType;
/** Type of the segmentation function */
using ShapeDetectionFunctionType = ShapeDetectionLevelSetFunction<OutputImageType, FeatureImageType>;
using ShapeDetectionFunctionPointer = typename ShapeDetectionFunctionType::Pointer;
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(ShapeDetectionLevelSetImageFilter);
/** Method for creation through the object factory */
itkNewMacro(Self);
protected:
~ShapeDetectionLevelSetImageFilter() override = default;
ShapeDetectionLevelSetImageFilter();
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** Overridden from Superclass to handle the case when PropagationScaling is zero
* and CurvatureScaling is non-zero.*/
void
GenerateData() override;
private:
ShapeDetectionFunctionPointer m_ShapeDetectionFunction{};
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkShapeDetectionLevelSetImageFilter.hxx"
#endif
#endif
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