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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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 itkThresholdSegmentationLevelSetImageFilter_h
#define itkThresholdSegmentationLevelSetImageFilter_h
#include "itkSegmentationLevelSetImageFilter.h"
#include "itkThresholdSegmentationLevelSetFunction.h"
namespace itk
{
/** \class ThresholdSegmentationLevelSetImageFilter
* \brief Segments structures in images based on intensity values.
*
* \par IMPORTANT
* The SegmentationLevelSetImageFilter class and the
* ThresholdSegmentationLevelSetFunction class contain additional information necessary
* to the full understanding of how to use this filter.
*
* \par OVERVIEW
* This class is a level set method segmentation filter. It constructs a
* speed function which is close to zero at the upper and lower bounds of an
* intensity window, effectively locking the propagating front onto those
* edges. Elsewhere, the front will propagate quickly.
*
* \par INPUTS
* This filter requires two inputs. The first input is a seed
* image. This seed image must contain an isosurface that you want to use as the
* seed for your segmentation. It can be a binary, graylevel, or floating
* point image. The only requirement is that it contain a closed isosurface
* that you will identify as the seed by setting the IsosurfaceValue parameter
* of the filter. For a binary image you will want to set your isosurface
* value halfway between your on and off values (i.e. for 0's and 1's, use an
* isosurface value of 0.5).
*
* \par
* The second input is the feature image. This is the image from which the
* speed function will be calculated. For most applications, this is the
* image that you want to segment. The desired isosurface in your seed image
* should lie within the region of your feature image that you are trying to
* segment. Note that this filter does no preprocessing of the feature image
* before thresholding.
*
* \par
* See SegmentationLevelSetImageFilter for more information on Inputs.
*
* \par OUTPUTS
* The filter outputs a single, scalar, real-valued image.
* Positive values in the output image are inside the segmentated region
* and negative values in the image are outside of the inside region. The
* zero crossings of the image correspond to the position of the level set
* front.
*
* \par
* See SparseFieldLevelSetImageFilter and
* SegmentationLevelSetImageFilter for more information.
*
* \par PARAMETERS
* In addition to parameters described in SegmentationLevelSetImageFilter,
* this filter adds the UpperThreshold and LowerThreshold. See
* ThresholdSegmentationLevelSetFunction for a description of how these values
* affect the segmentation.
*
* \sa SegmentationLevelSetImageFilter
* \sa ThresholdSegmentationLevelSetFunction,
* \sa SparseFieldLevelSetImageFilter
* \ingroup ITKLevelSets
*/
template< typename TInputImage,
typename TFeatureImage,
typename TOutputPixelType = float >
class ITK_TEMPLATE_EXPORT ThresholdSegmentationLevelSetImageFilter:
public SegmentationLevelSetImageFilter< TInputImage, TFeatureImage, TOutputPixelType >
{
public:
/** Standard class typedefs */
typedef ThresholdSegmentationLevelSetImageFilter Self;
typedef SegmentationLevelSetImageFilter< TInputImage, TFeatureImage, TOutputPixelType >
Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Inherited typedef from the superclass. */
typedef typename Superclass::ValueType ValueType;
typedef typename Superclass::OutputImageType OutputImageType;
typedef typename Superclass::FeatureImageType FeatureImageType;
/** Type of the segmentation function */
typedef ThresholdSegmentationLevelSetFunction< OutputImageType,
FeatureImageType > ThresholdFunctionType;
typedef typename ThresholdFunctionType::Pointer ThresholdFunctionPointer;
/** Run-time type information (and related methods). */
itkTypeMacro(ThresholdSegmentationLevelSetImageFilter, SegmentationLevelSetImageFilter);
/** Method for creation through the object factory */
itkNewMacro(Self);
/** Get/Set the threshold values that will be used to calculate the speed
function. */
void SetUpperThreshold(ValueType v)
{
this->m_ThresholdFunction->SetUpperThreshold(v);
this->Modified();
}
void SetLowerThreshold(ValueType v)
{
this->m_ThresholdFunction->SetLowerThreshold(v);
this->Modified();
}
ValueType GetUpperThreshold() const
{
return m_ThresholdFunction->GetUpperThreshold();
}
ValueType GetLowerThreshold() const
{
return m_ThresholdFunction->GetLowerThreshold();
}
/** Set/Get the weight applied to the edge (Laplacian) attractor in the speed
* term function. Zero will turn this term off. */
void SetEdgeWeight(ValueType v)
{
this->m_ThresholdFunction->SetEdgeWeight(v);
this->Modified();
}
ValueType GetEdgeWeight() const
{
return m_ThresholdFunction->GetEdgeWeight();
}
/** Anisotropic diffusion is applied to the FeatureImage before calculating
* the Laplacian (edge) term. This method sets/gets the number of diffusion
* iterations. */
void SetSmoothingIterations(int v)
{
this->m_ThresholdFunction->SetSmoothingIterations(v);
this->Modified();
}
int GetSmoothingIterations() const
{
return m_ThresholdFunction->GetSmoothingIterations();
}
/** Anisotropic diffusion is applied to the FeatureImage before calculating
* the Laplacian (edge) term. This method sets/gets the diffusion time
* step. */
void SetSmoothingTimeStep(ValueType v)
{
this->m_ThresholdFunction->SetSmoothingTimeStep(v);
this->Modified();
}
ValueType GetSmoothingTimeStep() const
{
return m_ThresholdFunction->GetSmoothingTimeStep();
}
/** Anisotropic diffusion is applied to the FeatureImage before calculatign
* the Laplacian (edge) term. This method sets/gets the smoothing
* conductance. */
void SetSmoothingConductance(ValueType v)
{
this->m_ThresholdFunction->SetSmoothingConductance(v);
this->Modified();
}
ValueType GetSmoothingConductance() const
{
return m_ThresholdFunction->GetSmoothingConductance();
}
protected:
~ThresholdSegmentationLevelSetImageFilter() ITK_OVERRIDE {}
ThresholdSegmentationLevelSetImageFilter();
virtual void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
ITK_DISALLOW_COPY_AND_ASSIGN(ThresholdSegmentationLevelSetImageFilter);
private:
ThresholdFunctionPointer m_ThresholdFunction;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkThresholdSegmentationLevelSetImageFilter.hxx"
#endif
#endif
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