libinsighttoolkit3-dev 3.20.1-1 / usr / include / InsightToolkit / Review / itkDiscreteGradientMagnitudeGaussianImageFunction.h

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

  Program:   Insight Segmentation & Registration Toolkit
  Module:    itkDiscreteGradientMagnitudeGaussianImageFunction.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 __itkDiscreteGradientMagnitudeGaussianImageFunction_h
#define __itkDiscreteGradientMagnitudeGaussianImageFunction_h

#include "itkNeighborhoodOperatorImageFunction.h"
#include "itkImageFunction.h"
#include "itkGaussianOperator.h"
#include "itkGaussianDerivativeOperator.h"

namespace itk
{

/**
 * \class DiscreteGradientMagnitudeGaussianImageFunction
 * \brief Compute the discrete gradient magnitude gaussian of an the image
 *        at a specific location in space, i.e. point, index or continuous
 *        index. This class computes a single derivative given the order in
 *        each direction (by default zero).
 * This class is templated over the input image type.
 *
 * The Initialize() method must be called after setting the parameters and before
 * evaluating the function.
 *
 * \author Ivan Macia, VICOMTech, Spain, http://www.vicomtech.es
 *
 * This implementation was taken from the Insight Journal paper:
 * http://hdl.handle.net/1926/1290
 *
 * \sa NeighborhoodOperator
 * \sa ImageFunction
 */
template <class TInputImage,class TOutput=double>
class ITK_EXPORT DiscreteGradientMagnitudeGaussianImageFunction :
  public ImageFunction< TInputImage, TOutput, TOutput >
{
public:

  /**Standard "Self" typedef */
  typedef DiscreteGradientMagnitudeGaussianImageFunction   Self;

  /** Standard "Superclass" typedef */
  typedef ImageFunction<TInputImage, TOutput, TOutput>  Superclass;

  /** Smart pointer typedef support */
  typedef SmartPointer<Self>        Pointer;
  typedef SmartPointer<const Self>  ConstPointer;

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

  /** Run-time type information (and related methods) */
  itkTypeMacro( DiscreteGradientMagnitudeGaussianImageFunction, ImageFunction );

  /** Image dependent types */
  typedef typename Superclass::InputImageType       InputImageType;
  typedef typename Superclass::InputPixelType       InputPixelType;
  typedef typename Superclass::IndexType            IndexType;
  typedef typename Superclass::IndexValueType       IndexValueType;
  typedef typename Superclass::ContinuousIndexType  ContinuousIndexType;
  typedef typename Superclass::PointType            PointType;

  /** Dimension of the underlying image */
  itkStaticConstMacro(ImageDimension2, unsigned int,
                      InputImageType::ImageDimension);

  /** Output type */
  typedef typename Superclass::OutputType     OutputType;

  /** Arrays for native types */
  typedef FixedArray<double,itkGetStaticConstMacro(ImageDimension2)>        VarianceArrayType;
  typedef FixedArray<unsigned int,itkGetStaticConstMacro(ImageDimension2)>  OrderArrayType;

  typedef itk::GaussianDerivativeOperator<TOutput,
    itkGetStaticConstMacro(ImageDimension2)>    GaussianDerivativeOperatorType;

  /** Array to store gaussian derivative operators one for each dimension */
  typedef FixedArray<GaussianDerivativeOperatorType,
    2*itkGetStaticConstMacro(ImageDimension2)>            GaussianDerivativeOperatorArrayType;

  /** Precomputed N-dimensional derivative kernel */
  typedef Neighborhood<TOutput,itkGetStaticConstMacro(ImageDimension2)>  KernelType;

  /** Array to store precomputed N-dimensional kernels for the gradient components */
  typedef FixedArray<KernelType,itkGetStaticConstMacro(ImageDimension2)>      KernelArrayType;

  /** Image function that performs convolution with the neighborhood operator */
  typedef NeighborhoodOperatorImageFunction
    <InputImageType, TOutput>                           OperatorImageFunctionType;
  typedef typename OperatorImageFunctionType::Pointer   OperatorImageFunctionPointer;

  /** Interpolation modes */
  enum InterpolationModeType { NearestNeighbourInterpolation, LinearInterpolation };

public:

  /** Evalutate the  in the given dimension at specified point */
  virtual OutputType Evaluate(const PointType& point) const;

  /** Evaluate the function at specified Index position */
  virtual OutputType EvaluateAtIndex( const IndexType & index ) const;

  /** Evaluate the function at specified ContinousIndex position */
  virtual OutputType EvaluateAtContinuousIndex(
    const ContinuousIndexType & index ) const;

  /** Set/Get the variance for the discrete Gaussian kernel.
   * Sets the variance for individual dimensions. The default is 0.0 in each dimension.
   * If UseImageSpacing is true, the units are the physical units of your image.
   * If UseImageSpacing is false then the units are pixels */
  itkSetMacro(Variance, VarianceArrayType);
  itkGetConstMacro(Variance, const VarianceArrayType);
  itkSetVectorMacro( Variance, double, VarianceArrayType::Length );

  /** Convenience method for setting the variance for all dimensions */
  virtual void SetVariance( double variance )
    {
    m_Variance.Fill( variance );
    this->Modified();
    }

  /** Convenience method for setting the variance through the standard deviation */
  void SetSigma( const double sigma )
    {
    SetVariance( sigma * sigma );
    }

  /** Set/Get the desired maximum error of the gaussian approximation.  Maximum
   * error is the difference between the area under the discrete Gaussian curve
   * and the area under the continuous Gaussian. Maximum error affects the
   * Gaussian operator size. The value is clamped between 0.00001 and
   * 0.99999. */
  itkSetClampMacro( MaximumError, double, 0.00001, 0.99999 );
  itkGetConstMacro( MaximumError, double );

  /** Set/Get the flag for calculating scale-space normalized derivatives.
    * Normalized derivatives are obtained multiplying by the scale
    * parameter t. */
  itkSetMacro( NormalizeAcrossScale, bool );
  itkGetConstMacro( NormalizeAcrossScale, bool );
  itkBooleanMacro( NormalizeAcrossScale );

  /** Set/Get the flag for using image spacing when calculating derivatives. */
  itkSetMacro( UseImageSpacing, bool );
  itkGetConstMacro( UseImageSpacing, bool );
  itkBooleanMacro( UseImageSpacing );

  /** Set/Get a limit for growth of the kernel. Small maximum error values with
   *  large variances will yield very large kernel sizes. This value can be
   *  used to truncate a kernel in such instances. A warning will be given on
   *  truncation of the kernel. */
  itkSetMacro( MaximumKernelWidth, unsigned int );
  itkGetConstMacro( MaximumKernelWidth, unsigned int );

  /** Set/Get the interpolation mode. */
  itkSetMacro( InterpolationMode, InterpolationModeType );
  itkGetConstMacro( InterpolationMode, InterpolationModeType );

  /** Set the input image.
   * \warning this method caches BufferedRegion information.
   * If the BufferedRegion has changed, user must call
   * SetInputImage again to update cached values. */
  virtual void SetInputImage( const InputImageType * ptr );

  /** Initialize the Gaussian kernel. Call this method before evaluating the function.
   * This method MUST be called after any changes to function parameters. */
  virtual void Initialize( ) { RecomputeGaussianKernel(); }

protected:

  DiscreteGradientMagnitudeGaussianImageFunction();
  DiscreteGradientMagnitudeGaussianImageFunction( const Self& ){};

  ~DiscreteGradientMagnitudeGaussianImageFunction(){};

  void operator=( const Self& ){};
  void PrintSelf(std::ostream& os, Indent indent) const;

  void RecomputeGaussianKernel();
 // void RecomputeContinuousGaussianKernel(
   //        const double* offset) const;

private:

  /** Desired variance of the discrete Gaussian function */
  VarianceArrayType m_Variance;

  /** Difference between the areas under the curves of the continuous and
   * discrete Gaussian functions */
  double m_MaximumError;

  /** Maximum kernel size allowed.  This value is used to truncate a kernel
   *  that has grown too large.  A warning is given when the specified maximum
   *  error causes the kernel to exceed this size */
  unsigned int m_MaximumKernelWidth;

  /** Array of derivative operators, one for each dimension and order.
    * First N zero-rder operators are stored, then N first-order making
    * 2*N operators altogether where N=ImageDimension */
  GaussianDerivativeOperatorArrayType   m_OperatorArray;

  /** Array of N-dimensional kernels used to calculate gradient components */
  KernelArrayType  m_KernelArray;

  /** OperatorImageFunction */
  OperatorImageFunctionPointer  m_OperatorImageFunction;

  /** Flag for scale-space normalization of derivatives */
  bool m_NormalizeAcrossScale;

  /** Flag to indicate whether to use image spacing */
  bool m_UseImageSpacing;

  /** Interpolation mode */
  InterpolationModeType  m_InterpolationMode;

};

} // namespace itk

// Define instantiation macro for this template.
#define ITK_TEMPLATE_DiscreteGradientMagnitudeGaussianImageFunction(_, EXPORT, x, y) namespace itk { \
  _(2(class EXPORT DiscreteGradientMagnitudeGaussianImageFunction< ITK_TEMPLATE_2 x >)) \
  namespace Templates { typedef DiscreteGradientMagnitudeGaussianImageFunction< ITK_TEMPLATE_2 x > \
                        DiscreteGradientMagnitudeGaussianImageFunction##y; } \
  }

#if ITK_TEMPLATE_EXPLICIT
# include "Templates/itkDiscreteGradientMagnitudeGaussianImageFunction+-.h"
#endif

#if ITK_TEMPLATE_TXX
# include "itkDiscreteGradientMagnitudeGaussianImageFunction.txx"
#endif

#endif