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
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
|
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkDiffusionTensor3DReconstructionImageFilterTest.cxx
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.
=========================================================================*/
#include "itkDiffusionTensor3DReconstructionImageFilter.h"
#include "itkImageRegionIteratorWithIndex.h"
#include <iostream>
int itkDiffusionTensor3DReconstructionImageFilterTest(int, char*[])
{
typedef short int ReferencePixelType;
typedef short int GradientPixelType;
typedef double TensorPrecisionType;
typedef itk::DiffusionTensor3DReconstructionImageFilter<
ReferencePixelType, GradientPixelType, TensorPrecisionType >
TensorReconstructionImageFilterType;
typedef TensorReconstructionImageFilterType::GradientImageType GradientImageType;
TensorReconstructionImageFilterType::Pointer tensorReconstructionFilter =
TensorReconstructionImageFilterType::New();
// Create a reference image
//
typedef TensorReconstructionImageFilterType::ReferenceImageType ReferenceImageType;
ReferenceImageType::Pointer referenceImage = ReferenceImageType::New();
typedef ReferenceImageType::RegionType ReferenceRegionType;
typedef ReferenceRegionType::IndexType ReferenceIndexType;
typedef ReferenceRegionType::SizeType ReferenceSizeType;
ReferenceSizeType sizeReferenceImage = {{ 4, 4, 4 }};
ReferenceIndexType indexReferenceImage = {{ 0, 0, 0 }};
ReferenceRegionType regionReferenceImage;
regionReferenceImage.SetSize( sizeReferenceImage );
regionReferenceImage.SetIndex( indexReferenceImage);
referenceImage->SetRegions( regionReferenceImage );
referenceImage->Allocate();
referenceImage->FillBuffer( 100 );
const unsigned int numberOfGradientImages = 6;
// Assign gradient directions
//
double gradientDirections[6][3] =
{
{-1.000000, 0.000000 , 0.000000},
{-0.166000, 0.986000 , 0.000000},
{0.110000 , 0.664000 , 0.740000},
{-0.901000, -0.419000 , -0.110000},
{0.169000 , -0.601000 , 0.781000},
{0.815000 , -0.386000 , 0.433000}
};
// Create gradient images
//
typedef GradientImageType::Pointer GradientImagePointer;
typedef TensorReconstructionImageFilterType::GradientImageType GradientImageType;
typedef GradientImageType::RegionType GradientRegionType;
typedef GradientRegionType::IndexType GradientIndexType;
typedef GradientRegionType::SizeType GradientSizeType;
typedef ReferenceRegionType::IndexType ReferenceIndexType;
for( unsigned int i=0; i < numberOfGradientImages; i++ )
{
GradientImageType::Pointer gradientImage = GradientImageType::New();
GradientSizeType sizeGradientImage = {{ 4, 4, 4 }};
GradientIndexType indexGradientImage = {{ 0, 0, 0 }};
GradientRegionType regionGradientImage;
regionGradientImage.SetSize( sizeGradientImage );
regionGradientImage.SetIndex( indexGradientImage);
gradientImage->SetRegions( regionGradientImage );
gradientImage->Allocate();
itk::ImageRegionIteratorWithIndex< GradientImageType > git(
gradientImage, regionGradientImage );
git.GoToBegin();
while( !git.IsAtEnd() )
{
GradientPixelType fancyGradientValue =
static_cast< short int >((i+1) * (i+1) * (i+1));
git.Set( fancyGradientValue );
++git;
}
TensorReconstructionImageFilterType::GradientDirectionType gradientDirection;
gradientDirection[0] = gradientDirections[i][0];
gradientDirection[1] = gradientDirections[i][1];
gradientDirection[2] = gradientDirections[i][2];
tensorReconstructionFilter->AddGradientImage( gradientDirection, gradientImage );
std::cout << "Gradient directions: " << gradientDirection << std::endl;
}
tensorReconstructionFilter->SetReferenceImage( referenceImage );
// TODO: remove this when netlib is made thread safe
tensorReconstructionFilter->SetNumberOfThreads( 1 );
// Also see if vnl_svd is thread safe now...
std::cout << std::endl << "This filter is using " <<
tensorReconstructionFilter->GetNumberOfThreads() << " threads " << std::endl;
tensorReconstructionFilter->Update();
typedef TensorReconstructionImageFilterType::TensorImageType TensorImageType;
TensorImageType::Pointer tensorImage = tensorReconstructionFilter->GetOutput();
typedef TensorImageType::IndexType TensorImageIndexType;
TensorImageIndexType tensorImageIndex = {{3,3,3}};
GradientIndexType gradientImageIndex = {{3,3,3}};
ReferenceIndexType referenceImageIndex = {{3,3,3}};
std::cout << std::endl << "Pixels at index: " << tensorImageIndex << std::endl;
std::cout << "Reference pixel "
<< referenceImage->GetPixel( referenceImageIndex ) << std::endl;
for( unsigned int i=0; i < numberOfGradientImages; i++ )
{
std::cout << "Gradient image " << i << " pixel : "
<< static_cast< GradientImageType * >( const_cast< GradientImageType * >(
tensorReconstructionFilter->GetInput(i+1)))->GetPixel(gradientImageIndex)
<< std::endl;
}
double expectedResult[3][3] =
{
{4.60517, -2.6698, -8.4079},
{-2.6698, 1.56783, 0.900034},
{-8.4079, 0.900034, 2.62504}
};
std::cout << std::endl << "Reconstructed tensor : " << std::endl;
bool passed = true;
double precision = 0.0001;
for( unsigned int i = 0; i<3; i++ )
{
std::cout << "\t";
for( unsigned int j = 0; j<3; j++ )
{
std::cout << tensorImage->GetPixel(tensorImageIndex)(i,j) << " ";
if( (vnl_math_abs(tensorImage->GetPixel(tensorImageIndex)(i,j) - expectedResult[i][j])) > precision )
{
passed = false;
}
}
std::cout << std::endl;
}
if( !passed )
{
std::cout << "[FAILED]" << std::endl;
std::cout << "Expected tensor : " << std::endl;
for( unsigned int i = 0; i<3; i++ )
{
std::cout << "\t";
for( unsigned int j = 0; j<3; j++ )
{
std::cout << expectedResult[i][j] << " ";
}
std::cout << std::endl;
}
return EXIT_FAILURE;
}
std::cout << "[PASSED]" << std::endl;
return EXIT_SUCCESS;
}
|
