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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkGradientNDAnisotropicDiffusionFunction.txx,v $
Language: C++
Date: $Date: 2008-01-18 20:07:32 $
Version: $Revision: 1.13 $
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 __itkGradientNDAnisotropicDiffusionFunction_txx_
#define __itkGradientNDAnisotropicDiffusionFunction_txx_
#include "itkNumericTraits.h"
namespace itk {
template<class TImage>
double GradientNDAnisotropicDiffusionFunction<TImage>
::m_MIN_NORM = 1.0e-10;
template<class TImage>
GradientNDAnisotropicDiffusionFunction<TImage>
::GradientNDAnisotropicDiffusionFunction()
{
unsigned int i, j;
RadiusType r;
for (i = 0; i < ImageDimension; ++i)
{
r[i] = 1;
}
this->SetRadius(r);
// Dummy neighborhood used to set up the slices.
Neighborhood<PixelType, ImageDimension> it;
it.SetRadius(r);
// Slice the neighborhood
m_Center = it.Size() / 2;
for (i = 0; i< ImageDimension; ++i)
{ m_Stride[i] = it.GetStride(i); }
for (i = 0; i< ImageDimension; ++i)
{ x_slice[i] = std::slice( m_Center - m_Stride[i], 3, m_Stride[i]); }
for (i = 0; i< ImageDimension; ++i)
{
for (j = 0; j < ImageDimension; ++j)
{
// For taking derivatives in the i direction that are offset one
// pixel in the j direction.
xa_slice[i][j]
= std::slice((m_Center + m_Stride[j])-m_Stride[i], 3, m_Stride[i]);
xd_slice[i][j]
= std::slice((m_Center - m_Stride[j])-m_Stride[i], 3, m_Stride[i]);
}
}
// Allocate the derivative operator.
dx_op.SetDirection(0); // Not relevant, will be applied in a slice-based
// fashion.
dx_op.SetOrder(1);
dx_op.CreateDirectional();
}
template<class TImage>
typename GradientNDAnisotropicDiffusionFunction<TImage>::PixelType
GradientNDAnisotropicDiffusionFunction<TImage>
::ComputeUpdate(const NeighborhoodType &it, void *,
const FloatOffsetType&)
{
unsigned int i, j;
double accum;
double accum_d;
double Cx;
double Cxd;
// PixelType is scalar in this context
PixelRealType delta;
PixelRealType dx_forward;
PixelRealType dx_backward;
PixelRealType dx[ImageDimension];
PixelRealType dx_aug;
PixelRealType dx_dim;
delta = NumericTraits<PixelRealType>::Zero;
// Calculate the centralized derivatives for each dimension.
for (i = 0; i < ImageDimension; i++)
{ dx[i] = (it.GetPixel(m_Center + m_Stride[i])-it.GetPixel(m_Center - m_Stride[i]))/2.0f; }
for (i = 0; i < ImageDimension; i++)
{
// ``Half'' directional derivatives
dx_forward = it.GetPixel(m_Center + m_Stride[i])
- it.GetPixel(m_Center);
dx_backward = it.GetPixel(m_Center)
- it.GetPixel(m_Center - m_Stride[i]);
// Calculate the conductance terms. Conductance varies with each
// dimension because the gradient magnitude approximation is different
// along each dimension.
accum = 0.0;
accum_d = 0.0;
for (j = 0; j < ImageDimension; j++)
{
if (j != i)
{
dx_aug = (it.GetPixel(m_Center + m_Stride[i] + m_Stride[j]) -
it.GetPixel(m_Center + m_Stride[i] - m_Stride[j]) ) / 2.0f;
dx_dim = (it.GetPixel(m_Center - m_Stride[i] + m_Stride[j]) -
it.GetPixel(m_Center - m_Stride[i] - m_Stride[j]) ) /2.0f;
accum += 0.25f * vnl_math_sqr( dx[j] + dx_aug );
accum_d += 0.25f * vnl_math_sqr( dx[j] + dx_dim );
}
}
if (m_K == 0.0)
{
Cx = 0.0;
Cxd = 0.0;
}
else
{
Cx = vcl_exp(( vnl_math_sqr( dx_forward ) + accum) / m_K );
Cxd= vcl_exp(( vnl_math_sqr( dx_backward) + accum_d)/ m_K );
}
// Conductance modified first order derivatives.
dx_forward = dx_forward * Cx;
dx_backward = dx_backward * Cxd;
// Conductance modified second order derivative.
delta += dx_forward - dx_backward;
}
return static_cast<PixelType>(delta);
}
} // end namespace itk
#endif
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