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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkVectorGradientNDAnisotropicDiffusionFunction.txx,v $
Language: C++
Date: $Date: 2003-09-10 14:28:59 $
Version: $Revision: 1.11 $
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 __itkVectorGradientNDAnisotropicDiffusionFunction_txx_
#define __itkVectorGradientNDAnisotropicDiffusionFunction_txx_
namespace itk {
template<class TImage>
double VectorGradientNDAnisotropicDiffusionFunction<TImage>
::m_MIN_NORM = 1.0e-10;
template<class TImage>
VectorGradientNDAnisotropicDiffusionFunction<TImage>
::VectorGradientNDAnisotropicDiffusionFunction()
{
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 relelevant, we'll apply in a slice-based
// fashion
dx_op.SetOrder(1);
dx_op.CreateDirectional();
}
template<class TImage>
typename VectorGradientNDAnisotropicDiffusionFunction<TImage>::PixelType
VectorGradientNDAnisotropicDiffusionFunction<TImage>
::ComputeUpdate(const NeighborhoodType &it, void *,
const FloatOffsetType&)
{
unsigned int i, j, k;
PixelType delta;
double GradMag;
double GradMag_d;
double Cx[ImageDimension];
double Cxd[ImageDimension];
// Remember: PixelType is a Vector of length VectorDimension.
PixelType dx_forward[ImageDimension];
PixelType dx_backward[ImageDimension];
PixelType dx[ImageDimension];
PixelType dx_aug;
PixelType dx_dim;
// Calculate the directional and centralized derivatives.
for (i = 0; i < ImageDimension; i++)
{
dx_forward[i] = it.GetPixel(m_Center + m_Stride[i])
- it.GetPixel(m_Center);
dx_backward[i]= it.GetPixel(m_Center)
- it.GetPixel(m_Center - m_Stride[i]);
dx[i] = m_InnerProduct(x_slice[i], it, dx_op);
}
// Calculate the conductance term for each dimension.
for (i = 0; i < ImageDimension; i++)
{
// Calculate gradient magnitude approximation in this
// dimension linked (summed) across the vector components.
GradMag = 0.0;
GradMag_d = 0.0;
for (k =0; k < VectorDimension; k++)
{
GradMag += vnl_math_sqr( dx_forward[i][k] );
GradMag_d += vnl_math_sqr( dx_backward[i][k] );
for (j = 0; j < ImageDimension; j++)
{
if ( j != i)
{
dx_aug = m_InnerProduct(xa_slice[j][i], it, dx_op);
dx_dim = m_InnerProduct(xd_slice[j][i], it, dx_op);
GradMag += 0.25f * vnl_math_sqr( dx[j][k]+dx_aug[k] );
GradMag_d += 0.25f * vnl_math_sqr( dx[j][k]+dx_dim[k] );
}
}
}
if (m_K == 0.0)
{
Cx[i] = 0.0;
Cxd[i] = 0.0;
}
else
{
Cx[i] = ::exp( GradMag / m_K );
Cxd[i] = ::exp( GradMag_d / m_K );
}
}
// Compute update value
for (k = 0; k < VectorDimension; k++)
{
delta[k] = NumericTraits<ScalarValueType>::Zero;
for (i = 0; i < ImageDimension; ++i)
{
dx_forward[i][k] *= Cx[i];
dx_backward[i][k] *= Cxd[i];
delta[k] += dx_forward[i][k] - dx_backward[i][k];
}
}
return delta;
}
} // end namespace itk
#endif
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