1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
|
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkGaussianDerivativeSpatialFunction_hxx
#define itkGaussianDerivativeSpatialFunction_hxx
#include <cmath>
#include "itkMath.h"
#include "itkGaussianDerivativeSpatialFunction.h"
namespace itk
{
template< typename TOutput, unsigned int VImageDimension, typename TInput >
GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >
::GaussianDerivativeSpatialFunction()
{
m_Mean = ArrayType::Filled(0.0);
m_Sigma = ArrayType::Filled(1.0);
m_Scale = 1.0;
m_Normalized = false;
m_Direction = 0;
}
template< typename TOutput, unsigned int VImageDimension, typename TInput >
GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >
::~GaussianDerivativeSpatialFunction()
{}
template< typename TOutput, unsigned int VImageDimension, typename TInput >
typename GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >::OutputType
GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >
::Evaluate(const TInput & position) const
{
// Normalizing the Gaussian is important for statistical applications
// but is generally not desirable for creating images because of the
// very small numbers involved (would need to use doubles)
double prefixDenom;
if ( m_Normalized )
{
prefixDenom = m_Sigma[m_Direction] * m_Sigma[m_Direction];
for ( unsigned int i = 0; i < VImageDimension; i++ )
{
prefixDenom *= m_Sigma[i];
}
prefixDenom *= 2 * std::pow(2 * itk::Math::pi, VImageDimension / 2.0);
}
else
{
prefixDenom = 1.0;
}
double suffixExp = 0;
for ( unsigned int i = 0; i < VImageDimension; i++ )
{
suffixExp += ( position[m_Direction] - m_Mean[m_Direction] )
* ( position[m_Direction] - m_Mean[m_Direction] )
/ ( 2 * m_Sigma[m_Direction] * m_Sigma[m_Direction] );
}
double value = -2 * ( position[m_Direction] - m_Mean[m_Direction] ) * m_Scale * ( 1 / prefixDenom ) * std::exp(
-1 * suffixExp);
return static_cast<TOutput>(value);
}
/** Evaluate the function at a given position and return a vector */
template< typename TOutput, unsigned int VImageDimension, typename TInput >
typename GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >::VectorType
GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >
::EvaluateVector(const TInput & position) const
{
VectorType gradient;
for ( unsigned int i = 0; i < VImageDimension; i++ )
{
m_Direction = i;
gradient[i] = this->Evaluate(position);
}
return gradient;
}
template< typename TOutput, unsigned int VImageDimension, typename TInput >
void
GaussianDerivativeSpatialFunction< TOutput, VImageDimension, TInput >
::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Sigma: " << m_Sigma << std::endl;
os << indent << "Mean: " << m_Mean << std::endl;
os << indent << "Scale: " << m_Scale << std::endl;
os << indent << "Normalized?: " << m_Normalized << std::endl;
os << indent << "Direction: " << m_Direction << std::endl;
}
} // end namespace itk
#endif
|
