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/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    itkCurvesLevelSetFunction.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 __itkCurvesLevelSetFunction_h
#define __itkCurvesLevelSetFunction_h

#include "itkSegmentationLevelSetFunction.h"

namespace itk {

/** \class CurvesLevelSetFunction
 *
 * \brief This function is used in CurvesLevelSetImageFilter to
 * segment structures in images based on user supplied edge potential map.
 *
 * \par CurvesLevelSetFunction is a subclass of the generic LevelSetFunction.
 * It is useful for segmentations based on a user supplied edge potential map which
 * has values close to zero in regions near edges (or high image gradient) and values
 * close to one in regions with relatively constant intensity. Typically, the edge
 * potential map is a function of the 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 In this function both the propagation term \f$ P(\mathbf{x}) \f$
 * and the curvature spatial modifier term $\f$ Z(\mathbf{x}) \f$ are taken directly
 * from the edge potential image. The edge potential image is set via the
 * SetFeatureImage() method. An advection term \f$ A(\mathbf{x}) \f$ is constructed
 * from the negative gradient of the edge potential image. This term behaves like
 * a doublet attracting the contour to the edges.
 *
 * \par This implementation is based on:
 *  L. Lorigo, O. Faugeras, W.E.L. Grimson, R. Keriven, R. Kikinis, A. Nabavi,
 *  and C.-F. Westin, Curves: Curve evolution for vessel segmentation.
 *  Medical Image Analysis, 5:195-206, 2001.
 *
 * \sa LevelSetFunction
 * \sa SegmentationLevelSetImageFunction
 * \sa GeodesicActiveContourLevelSetImageFilter
 *
 * \ingroup FiniteDifferenceFunctions
 */
template <class TImageType, class TFeatureImageType = TImageType>
class ITK_EXPORT CurvesLevelSetFunction
  : public SegmentationLevelSetFunction<TImageType, TFeatureImageType>
{
public:
  /** Standard class typedefs. */
  typedef CurvesLevelSetFunction                   Self;
  typedef SegmentationLevelSetFunction<TImageType> Superclass;
  typedef LevelSetFunction<TImageType>             SuperSuperclass;
  typedef SmartPointer<Self>                       Pointer;
  typedef SmartPointer<const Self>                 ConstPointer;
  typedef TFeatureImageType                        FeatureImageType;

  /** Method for creation through the object factory. */
  itkNewMacro(Self);

  /** Run-time type information (and related methods) */
  itkTypeMacro( CurvesLevelSetFunction, SegmentationLevelSetFunction );

  /** Extract some parameters from the superclass. */
  typedef typename SuperSuperclass::PixelType        PixelType;
  typedef typename Superclass::ImageType             ImageType;
  typedef typename Superclass::NeighborhoodType      NeighborhoodType;
  typedef typename Superclass::ScalarValueType       ScalarValueType;
  typedef typename Superclass::FeatureScalarType     FeatureScalarType;
  typedef typename Superclass::RadiusType            RadiusType;
  typedef typename SuperSuperclass::FloatOffsetType  FloatOffsetType;
  typedef typename SuperSuperclass::GlobalDataStruct GlobalDataStruct;
  typedef typename Superclass::VectorImageType       VectorImageType;

  /** Extract some parameters from the superclass. */
  itkStaticConstMacro(ImageDimension, unsigned int,
                      Superclass::ImageDimension);

  /** Compute speed image from feature image. */
  virtual void CalculateSpeedImage();

  /** Compute the advection field from feature image. */
  virtual void CalculateAdvectionImage();

  /** The curvature speed is same as the propagation speed. */
  virtual ScalarValueType CurvatureSpeed(const NeighborhoodType & neighborhood,
                                         const FloatOffsetType & offset, GlobalDataStruct *gd ) const
  { return PropagationSpeed( neighborhood, offset, gd ); }

  /** Set/Get the sigma for the Gaussian kernel used to compute the gradient
   * of the feature image needed for the advection term of the equation. */
  void SetDerivativeSigma( const double v )
    { m_DerivativeSigma = v; }
  double GetDerivativeSigma()
    { return m_DerivativeSigma; }

  virtual void Initialize(const RadiusType &r);
  
protected:
  CurvesLevelSetFunction()
    {
    //Curvature term is the minimal curvature.
    this->UseMinimalCurvatureOn();
    this->SetAdvectionWeight( NumericTraits<ScalarValueType>::One );
    this->SetPropagationWeight( NumericTraits<ScalarValueType>::One );
    this->SetCurvatureWeight( NumericTraits<ScalarValueType>::One );
    
    m_DerivativeSigma = 1.0;
    }
  virtual ~CurvesLevelSetFunction() {}

  CurvesLevelSetFunction(const Self&); //purposely not implemented
  void operator=(const Self&); //purposely not implemented
  
  void PrintSelf(std::ostream& os, Indent indent) const
    {
    Superclass::PrintSelf(os, indent );
    os << indent << "DerivativeSigma: " << m_DerivativeSigma << std::endl;
    }

private:
  
  /** Slices for the ND neighborhood. */
  std::slice x_slice[ImageDimension];
  
  /** The offset of the center pixel in the neighborhood. */
  ::size_t m_Center;
  
  /** Stride length along the y-dimension. */
  ::size_t m_xStride[ImageDimension];
  
  
  double m_DerivativeSigma;
  
};
  
} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkCurvesLevelSetFunction.txx"
#endif

#endif