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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkCannySegmentationLevelSetImageFilter.h
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkCannySegmentationLevelSetImageFilter_h
#define __itkCannySegmentationLevelSetImageFilter_h
#include "itkSegmentationLevelSetImageFilter.h"
#include "itkCannySegmentationLevelSetFunction.h"
namespace itk {
/** \class CannySegmentationLevelSetImageFilter
* \brief Segments structures in images based on image features derived from
* pseudo-canny-edges.
*
* \par IMPORTANT
* The SegmentationLevelSetImageFilter class and the
* CannySegmentationLevelSetFunction 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 designed to lock onto edges as detected by a Canny
* filter.
*
* \par
* The CannySegmentationLevelSetImageFilter can be a tool for refining an
* existing segmentation, or it can be used to try to segment a region by
* itself. Like all other level-set based segmentation filters (see
* SegmentationLevelSetImageFilter), it works by first constructing a scalar
* speed term and a vector advection field based on edge features in the
* image. The level set front is then moved according to these two terms
* with the addition of a third curvature term to contol the smoothness of
* the solution.
*
* \par
* The speed term is constructed as the Danielsson distance transform of the
* Canny edge image, as calculated by the CannyEdgeDetectionImageFilter.
* This scalar speed can be tuned in and out of the final evolution equation
* by setting the PropagationScaling parameter (a value of 0 removes the
* speed term).
*
* \par
* The advection field term is constructed by minimizing Danielsson distance
* squared. i.e. \f$ \mbox{min} \int D^2 \Rightarrow D \nabla D \f$. This
* term moves the level set down the gradient of the distance transform.
*
* \par
* In practice, you may set the speed (propagation) term to zero if your
* initialization is already close to the edge you are interested in. If you
* are trying to segment a region by seeding with a small surface (blob,
* sphere) then you will likely want to add speed (propagation) to the
* equation so that the levelsets can expand along zero gradients. The
* relative influence of these two terms are controlled by the
* SetPropagationScaling and SetAdvectionScaling parameters.
*
*
* \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.
*
* \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
* There are five parameters important for controlling the behavior of this
* filter.
* \par
* (1) Threshold. Sets the thresholding value of the Canny edge detection.
* See CannyEdgeDetectionImageFilter for more information.
* \par
* (2) Variance. Controls the smoothing paramter of the gaussian filtering
* done during Canny edge detection.
* \par
* (3) CurvatureScaling. Controls the degree to which curvature influences
* the evolution of the level set. Higher values relative to Propagation and
* Advection scalings will yield a smoother surface.
* \par
* (4) PropagationScaling. Scales the propagation (speed) term of the level
* set equation. Set this term to zero to allow the level set to flow _only_
* down the gradient of the distance transform.
* \par
* (5) AdvectionScaling. Scales influence of the advection field relative to
* curvature and propagation terms.
*
* \sa SegmentationLevelSetImageFilter
* \sa CannySegmentationLevelSetFunction,
* \sa SparseFieldLevelSetImageFilter */
template <class TInputImage,
class TFeatureImage,
class TOutputPixelType = float>
class ITK_EXPORT CannySegmentationLevelSetImageFilter
: public SegmentationLevelSetImageFilter<TInputImage, TFeatureImage, TOutputPixelType >
{
public:
/** Standard class typedefs */
typedef CannySegmentationLevelSetImageFilter 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;
typedef typename Superclass::VectorImageType VectorImageType;
typedef typename Superclass::SpeedImageType SpeedImageType;
/** Type of the segmentation function */
typedef ::itk::CannySegmentationLevelSetFunction<OutputImageType,
FeatureImageType> CannyFunctionType;
typedef typename CannyFunctionType::ScalarValueType ScalarValueType;
/** Run-time type information (and related methods). */
itkTypeMacro(CannySegmentationLevelSetImageFilter, SegmentationLevelSetImageFilter);
/** Method for creation through the object factory */
itkNewMacro(Self);
/** Set the Threshold parameter of the CannyEdgeDetectionImageFilter
* used by the underlying level set function. */
void SetThreshold(ScalarValueType v)
{ this->m_CannyFunction->SetThreshold(v); }
ScalarValueType GetThreshold() const
{ return this->m_CannyFunction->GetThreshold(); }
/** Set the Variance parameter of the CannyEdgeDetectionImageFilter
* used by the underlying level set function. */
void SetVariance(double v)
{ this->m_CannyFunction->SetVariance(v); }
double GetVariance() const
{ return this->m_CannyFunction->GetVariance(); }
/** Get the Canny image that was used to create the speed and
advection images */
OutputImageType *GetCannyImage(void)
{ return this->m_CannyFunction->GetCannyImage(); }
#ifdef ITK_USE_CONCEPT_CHECKING
/** Begin concept checking */
itkConceptMacro(OutputHasNumericTraitsCheck,
(Concept::HasNumericTraits<TOutputPixelType>));
/** End concept checking */
#endif
protected:
~CannySegmentationLevelSetImageFilter() {}
CannySegmentationLevelSetImageFilter();
private:
CannySegmentationLevelSetImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
typename CannyFunctionType::Pointer m_CannyFunction;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkCannySegmentationLevelSetImageFilter.txx"
#endif
#endif
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