| 1 |
|
/*========================================================================= |
| 2 |
|
|
| 3 |
|
Program: Insight Segmentation & Registration Toolkit |
| 4 |
|
Module: $RCSfile: itkGaussianDerivativeSpatialFunction.txx.html,v $ |
| 5 |
|
Language: C++ |
| 6 |
|
Date: $Date: 2006/01/17 19:15:36 $ |
| 7 |
|
Version: $Revision: 1.4 $ |
| 8 |
|
|
| 9 |
|
Copyright (c) Insight Software Consortium. All rights reserved. |
| 10 |
|
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. |
| 11 |
|
|
| 12 |
|
This software is distributed WITHOUT ANY WARRANTY; without even |
| 13 |
|
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR |
| 14 |
|
PURPOSE. See the above copyright notices for more information. |
| 15 |
|
|
| 16 |
|
=========================================================================*/ |
| 17 |
|
#ifndef __itkGaussianDerivativeSpatialFunction_txx |
| 18 |
|
#define __itkGaussianDerivativeSpatialFunction_txx |
| 19 |
|
|
| 20 |
|
#include <math.h> |
| 21 |
|
#include "itkGaussianDerivativeSpatialFunction.h" |
| 22 |
|
|
| 23 |
|
namespace itk |
| 24 |
|
{ |
| 25 |
|
|
| 26 |
|
template <typename TOutput, unsigned int VImageDimension, typename TInput> |
| 27 |
|
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput> |
| 28 |
|
::GaussianDerivativeSpatialFunction() |
| 29 |
|
{ |
| 30 |
|
m_Mean = ArrayType::Filled(0.0); |
| 31 |
|
m_Sigma = ArrayType::Filled(1.0); |
| 32 |
|
m_Scale = 1.0; |
| 33 |
|
m_Normalized = false; |
| 34 |
|
m_Direction = 0; |
| 35 |
|
} |
| 36 |
|
|
| 37 |
|
template <typename TOutput, unsigned int VImageDimension, typename TInput> |
| 38 |
|
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput> |
| 39 |
|
::~GaussianDerivativeSpatialFunction() |
| 40 |
|
{ |
| 41 |
|
|
| 42 |
|
} |
| 43 |
|
|
| 44 |
|
template <typename TOutput, unsigned int VImageDimension, typename TInput> |
| 45 |
LEN |
typename GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::OutputType |
| 46 |
|
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput> |
| 47 |
|
::Evaluate(const TInput& position) const |
| 48 |
|
{ |
| 49 |
|
// Normalizing the Gaussian is important for statistical applications |
| 50 |
|
// but is generally not desirable for creating images because of the |
| 51 |
|
// very small numbers involved (would need to use doubles) |
| 52 |
|
double prefixDenom; |
| 53 |
|
|
| 54 |
|
if (m_Normalized) |
| 55 |
|
{ |
| 56 |
|
prefixDenom = m_Sigma[0]*m_Sigma[0]*m_Sigma[0]; |
| 57 |
|
|
| 58 |
|
for(unsigned int i = 1; i < VImageDimension; i++) |
| 59 |
|
{ |
| 60 |
|
prefixDenom *= m_Sigma[i]*m_Sigma[i]*m_Sigma[i]; |
| 61 |
|
} |
| 62 |
|
|
| 63 |
|
prefixDenom *= 2 * 3.1415927; |
| 64 |
|
} |
| 65 |
|
else |
| 66 |
|
{ |
| 67 |
|
prefixDenom = 1.0; |
| 68 |
|
} |
| 69 |
|
|
| 70 |
|
double suffixExp = 0; |
| 71 |
|
|
| 72 |
|
for(unsigned int i = 0; i < VImageDimension; i++) |
| 73 |
|
{ |
| 74 |
LEN |
suffixExp += (position[m_Direction] - m_Mean[m_Direction])*(position[m_Direction] - m_Mean[m_Direction]) / (2 * m_Sigma[m_Direction] * m_Sigma[m_Direction]); |
| 75 |
|
} |
| 76 |
|
|
| 77 |
|
|
| 78 |
LEN |
double value = -2*(position[m_Direction] - m_Mean[m_Direction])*m_Scale * (1 / prefixDenom) * exp(-1 * suffixExp); |
| 79 |
|
|
| 80 |
|
return (TOutput) value; |
| 81 |
|
} |
| 82 |
|
|
| 83 |
|
|
| 84 |
|
/** Evaluate the function at a given position and return a vector */ |
| 85 |
|
template <typename TOutput, unsigned int VImageDimension, typename TInput> |
| 86 |
LEN |
typename GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::VectorType |
| 87 |
|
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput> |
| 88 |
|
::EvaluateVector(const TInput& position) const |
| 89 |
|
{ |
| 90 |
|
VectorType gradient; |
| 91 |
|
for(unsigned int i = 0; i < VImageDimension; i++) |
| 92 |
|
{ |
| 93 |
|
m_Direction = i; |
| 94 |
|
gradient[i]=this->Evaluate(position); |
| 95 |
|
} |
| 96 |
|
return gradient; |
| 97 |
|
} |
| 98 |
|
|
| 99 |
|
template <typename TOutput, unsigned int VImageDimension, typename TInput> |
| 100 |
|
void |
| 101 |
|
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput> |
| 102 |
|
::PrintSelf(std::ostream& os, Indent indent) const |
| 103 |
|
{ |
| 104 |
|
Superclass::PrintSelf(os,indent); |
| 105 |
|
|
| 106 |
|
os << indent << "Sigma: " << m_Sigma << std::endl; |
| 107 |
|
os << indent << "Mean: " << m_Mean << std::endl; |
| 108 |
|
os << indent << "Scale: " << m_Scale << std::endl; |
| 109 |
|
os << indent << "Normalized?: " << m_Normalized << std::endl; |
| 110 |
|
os << indent << "Direction: " << m_Direction << std::endl; |
| 111 |
|
} |
| 112 |
|
|
| 113 |
|
|
| 114 |
|
} // end namespace itk |
| 115 |
|
|
| 116 |
|
#endif |
| 117 |
|
|
