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

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

  Program:   Insight Segmentation & Registration Toolkit
  Module:    itkDiscreteGradientMagnitudeGaussianImageFunction.txx
  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_txx
#define __itkDiscreteGradientMagnitudeGaussianImageFunction_txx

#include "itkDiscreteGradientMagnitudeGaussianImageFunction.h"
#include "itkNeighborhoodOperatorImageFilter.h"

namespace itk
{

/** Set the Input Image */
template <class TInputImage, class TOutput>
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::DiscreteGradientMagnitudeGaussianImageFunction() :
  m_MaximumError( 0.005 ),
  m_MaximumKernelWidth( 30 ),
  m_NormalizeAcrossScale( true ),
  m_UseImageSpacing( true ),
  m_InterpolationMode( NearestNeighbourInterpolation )
{
  m_Variance.Fill( 1.0 );
  m_OperatorImageFunction = OperatorImageFunctionType::New();
}


/** Print self method */
template <class TInputImage, class TOutput>
void
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::PrintSelf(std::ostream& os, Indent indent) const
{
  this->Superclass::PrintSelf(os,indent);
  os << indent << "UseImageSpacing: " << m_UseImageSpacing << std::endl;
  os << indent << "NormalizeAcrossScale: " << m_NormalizeAcrossScale << std::endl;
  os << indent << "Variance: " << m_Variance << std::endl;
  os << indent << "MaximumError: " << m_MaximumError << std::endl;
  os << indent << "MaximumKernelWidth: " << m_MaximumKernelWidth << std::endl;
  os << indent << "InterpolationMode: " << m_InterpolationMode << std::endl;
  os << indent << "OperatorArray: " << m_OperatorArray << std::endl;
  os << indent << "KernelArray: " << m_KernelArray << std::endl;
  os << indent << "OperatorImageFunction: " << m_OperatorImageFunction << std::endl;
}


/** Set the input image */
template <class TInputImage, class TOutput>
void
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::SetInputImage( const InputImageType * ptr )
{
  Superclass::SetInputImage(ptr);
  m_OperatorImageFunction->SetInputImage(ptr);
}


/** Recompute the gaussian kernel used to evaluate indexes
 *  This should use a fastest Derivative Gaussian operator */
template <class TInputImage, class TOutput>
void
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::RecomputeGaussianKernel()
{
  /** Create 2*N operators (N=ImageDimension) where the
   * first N are zero-order and the second N are first-order */

  unsigned int idx;
  unsigned int maxRadius = 0;

  for(unsigned int direction=0; direction <
    itkGetStaticConstMacro(ImageDimension2); direction++ )
    {
    for( unsigned int order=0; order <= 1; ++order )
      {
      idx = itkGetStaticConstMacro(ImageDimension2)*order + direction;
      m_OperatorArray[idx].SetDirection( direction );
      m_OperatorArray[idx].SetMaximumKernelWidth( m_MaximumKernelWidth );
      m_OperatorArray[idx].SetMaximumError( m_MaximumError );

      if( ( m_UseImageSpacing == true ) && ( this->GetInputImage() ) )
        {
        if ( this->GetInputImage()->GetSpacing()[direction] == 0.0)
          {
          itkExceptionMacro(<< "Pixel spacing cannot be zero");
          }
        else
          {
        m_OperatorArray[idx].SetSpacing(this->GetInputImage()->GetSpacing()[direction]);
          }
        }

      // GaussianDerivativeOperator modifies the variance when setting image spacing
      m_OperatorArray[idx].SetVariance( m_Variance[direction] );
      m_OperatorArray[idx].SetOrder(order);
      m_OperatorArray[idx].SetNormalizeAcrossScale( m_NormalizeAcrossScale );
      m_OperatorArray[idx].CreateDirectional();

      // Check for maximum radius
      for( unsigned int i=0; i<itkGetStaticConstMacro(ImageDimension2); ++i )
        {
        if( m_OperatorArray[idx].GetRadius()[i] > maxRadius )
          maxRadius = m_OperatorArray[idx].GetRadius()[i];
        }
      }
    }

  // Now precompute the N-dimensional kernel. This fastest as we don't have to perform
  // N convolutions for each point we calculate but only one.

  typedef itk::Image<TOutput,itkGetStaticConstMacro(ImageDimension2)>  KernelImageType;
  typename KernelImageType::Pointer kernelImage = KernelImageType::New();

  typedef typename KernelImageType::RegionType RegionType;
  RegionType region;

  typename RegionType::SizeType size;
  size.Fill( 4 * maxRadius + 1 );
  region.SetSize( size );

  kernelImage->SetRegions( region );
  kernelImage->Allocate();
  kernelImage->FillBuffer( itk::NumericTraits<TOutput>::Zero );

  // Initially the kernel image will be an impulse at the center
  typename KernelImageType::IndexType centerIndex;
  centerIndex.Fill( 2 * maxRadius ); // include also boundaries

  // Create an image region to be used later that does not include boundaries
  RegionType kernelRegion;
  size.Fill( 2 * maxRadius + 1 );
  typename RegionType::IndexType origin;
  origin.Fill( maxRadius );
  kernelRegion.SetSize( size );
  kernelRegion.SetIndex( origin );

  // Now create an image filter to perform sucessive convolutions
  typedef itk::NeighborhoodOperatorImageFilter<KernelImageType,KernelImageType>
    NeighborhoodFilterType;
  typename NeighborhoodFilterType::Pointer convolutionFilter = NeighborhoodFilterType::New();

  unsigned int opidx; // current operator index in m_OperatorArray

  for( unsigned int i=0; i<itkGetStaticConstMacro(ImageDimension2); ++i )
    {
      // Reset kernel image
    kernelImage->FillBuffer( itk::NumericTraits<TOutput>::Zero );
    kernelImage->SetPixel( centerIndex, itk::NumericTraits<TOutput>::One );

    for( unsigned int direction = 0; direction<itkGetStaticConstMacro(ImageDimension2); ++direction )
      {
      opidx = ( direction == i ? itkGetStaticConstMacro(ImageDimension2) + direction : direction );
      convolutionFilter->SetInput( kernelImage );
      convolutionFilter->SetOperator( m_OperatorArray[opidx] );
      convolutionFilter->Update();
      kernelImage = convolutionFilter->GetOutput();
      kernelImage->DisconnectPipeline();
      }

    // Set the size of the current kernel
    m_KernelArray[i].SetRadius( maxRadius );

    // Copy kernel image to neighborhood. Do not copy boundaries.
    ImageRegionConstIterator<KernelImageType> it( kernelImage, kernelRegion );
    it.GoToBegin();
    idx = 0;

    while( !it.IsAtEnd() )
      {
      m_KernelArray[i][idx] = it.Get();
      ++idx;
      ++it;
      }
    }
}


/** Evaluate the function at the specifed index */
template <class TInputImage, class TOutput>
typename DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>::OutputType
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::EvaluateAtIndex(const IndexType& index) const
{
  OutputType gradientMagnitude = itk::NumericTraits<OutputType>::Zero;
  OutputType temp;

  for( unsigned int i=0; i<m_KernelArray.Size(); ++i )
    {
    m_OperatorImageFunction->SetOperator( m_KernelArray[i] );
    temp = m_OperatorImageFunction->EvaluateAtIndex( index );
    if( m_UseImageSpacing )
      {
      gradientMagnitude += vnl_math_sqr( temp / this->GetInputImage()->GetSpacing()[i] );
      }
    else
      {
      gradientMagnitude += vnl_math_sqr( temp );
      } 
    }

  gradientMagnitude = vcl_sqrt( gradientMagnitude );
  return gradientMagnitude;
}


/** Evaluate the function at the specifed point */
template <class TInputImage, class TOutput>
typename DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>::OutputType
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::Evaluate(const PointType& point) const
{
  if( m_InterpolationMode == NearestNeighbourInterpolation )
    {
    IndexType index;
    this->ConvertPointToNearestIndex( point , index );
    return this->EvaluateAtIndex ( index );
    }
  else
    {
    ContinuousIndexType cindex;
#if ( ITK_VERSION_MAJOR < 3 ) || ( ITK_VERSION_MAJOR == 3 && ITK_VERSION_MINOR < 6 )
    this->ConvertPointToContinousIndex( point, cindex );
#else
    this->ConvertPointToContinuousIndex( point, cindex );
#endif
    return this->EvaluateAtContinuousIndex( cindex );
    }
}


/** Evaluate the function at specified ContinousIndex position.*/
template <class TInputImage, class TOutput>
typename DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>::OutputType
DiscreteGradientMagnitudeGaussianImageFunction<TInputImage,TOutput>
::EvaluateAtContinuousIndex(const ContinuousIndexType & cindex ) const
{
  if( m_InterpolationMode == NearestNeighbourInterpolation )
    {
    IndexType index;
    this->ConvertContinuousIndexToNearestIndex( cindex, index  );
    return this->EvaluateAtIndex( index );
    }
  else
    {
    unsigned int dim;  // index over dimension
    unsigned long neighbors = 1 << ImageDimension2;

    // Compute base index = closet index below point
    // Compute distance from point to base index
    IndexType baseIndex;
    double distance[ImageDimension2];

    for( dim = 0; dim < ImageDimension2; dim++ )
      {
      baseIndex[dim] = Math::Floor<IndexValueType>( cindex[dim] );
      distance[dim] = cindex[dim] - static_cast< double >( baseIndex[dim] );
      }

    // Interpolated value is the weighted sum of each of the surrounding
    // neighbors. The weight for each neighbor is the fraction overlap
    // of the neighbor pixel with respect to a pixel centered on point.
    TOutput value = NumericTraits<TOutput>::Zero;
    TOutput totalOverlap = NumericTraits<TOutput>::Zero;

    for( unsigned int counter = 0; counter < neighbors; counter++ )
      {
      double overlap = 1.0;          // fraction overlap
      unsigned int upper = counter;  // each bit indicates upper/lower neighbour
      IndexType neighIndex;

      // get neighbor index and overlap fraction
      for( dim = 0; dim < ImageDimension2; dim++ )
        {
        if ( upper & 1 )
          {
          neighIndex[dim] = baseIndex[dim] + 1;
          overlap *= distance[dim];
          }
        else
          {
          neighIndex[dim] = baseIndex[dim];
          overlap *= 1.0 - distance[dim];
          }
        upper >>= 1;
        }

      // get neighbor value only if overlap is not zero
      if( overlap )
        {
        value += overlap * static_cast<TOutput>( this->EvaluateAtIndex( neighIndex ) );
        totalOverlap += overlap;
        }

      if( totalOverlap == 1.0 )
        {
        // finished
        break;
        }
      }
    return ( static_cast<OutputType>( value ) );
    }
}

} // end namespace itk

#endif