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
|
/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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.
*
*=========================================================================*/
#include "itkImageRegistrationMethod.h"
#include "itkTranslationTransform.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkTextOutput.h"
/**
* This program test one instantiation of the itk::ImageRegistrationMethod class
*
* This file tests initialization errors.
*/
int
itkImageRegistrationMethodTest(int, char *[])
{
itk::OutputWindow::SetInstance(itk::TextOutput::New().GetPointer());
bool pass;
constexpr unsigned int dimension = 3;
// Fixed Image Type
using FixedImageType = itk::Image<float, dimension>;
// Moving Image Type
using MovingImageType = itk::Image<char, dimension>;
// Transform Type
using TransformType = itk::TranslationTransform<double, dimension>;
// Optimizer Type
using OptimizerType = itk::RegularStepGradientDescentOptimizer;
// Metric Type
using MetricType = itk::MeanSquaresImageToImageMetric<FixedImageType, MovingImageType>;
// Interpolation technique
using InterpolatorType = itk::LinearInterpolateImageFunction<MovingImageType, double>;
// Registration Method
using RegistrationType = itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;
auto metric = MetricType::New();
auto transform = TransformType::New();
auto optimizer = OptimizerType::New();
auto fixedImage = FixedImageType::New();
auto movingImage = MovingImageType::New();
auto interpolator = InterpolatorType::New();
auto registration = RegistrationType::New();
FixedImageType::SizeType size;
size.Fill(4); // the size of image have to be at least 4 in each dimension to
// compute gradient image inside the metric.
FixedImageType::RegionType region(size);
fixedImage->SetRegions(region);
fixedImage->Allocate();
fixedImage->FillBuffer(3.0);
movingImage->SetRegions(region);
movingImage->Allocate();
movingImage->FillBuffer(4);
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
registration->SetTransform(transform);
registration->SetFixedImage(fixedImage);
registration->SetMovingImage(movingImage);
registration->SetInterpolator(interpolator);
// Exercise Get methods
std::cout << "metric: " << registration->GetMetric() << std::endl;
std::cout << "optimizer: " << registration->GetOptimizer() << std::endl;
std::cout << "transform: " << registration->GetTransform() << std::endl;
std::cout << "fixed image: " << registration->GetFixedImage() << std::endl;
std::cout << "moving image: " << registration->GetMovingImage() << std::endl;
std::cout << "interpolator: " << registration->GetInterpolator() << std::endl;
std::cout << "initial parameters: ";
std::cout << registration->GetInitialTransformParameters() << std::endl;
using ParametersType = RegistrationType::ParametersType;
ParametersType initialParameters(transform->GetNumberOfParameters());
initialParameters.Fill(0);
ParametersType badParameters(2);
badParameters.Fill(5);
registration->SetInitialTransformParameters(initialParameters);
std::cout << registration;
/****************************************************
* Test out initialization errors
****************************************************/
#define TEST_INITIALIZATION_ERROR(ComponentName, badComponent, goodComponent) \
registration->Set##ComponentName(badComponent); \
try \
{ \
pass = false; \
registration->Update(); \
} \
catch (const itk::ExceptionObject & err) \
{ \
std::cout << "Caught expected ExceptionObject" << std::endl; \
std::cout << err << std::endl; \
pass = true; \
} \
registration->Set##ComponentName(goodComponent); \
\
if (!pass) \
{ \
std::cout << "Test failed." << std::endl; \
return EXIT_FAILURE; \
} \
ITK_MACROEND_NOOP_STATEMENT
TEST_INITIALIZATION_ERROR(InitialTransformParameters, badParameters, initialParameters);
TEST_INITIALIZATION_ERROR(Metric, nullptr, metric);
TEST_INITIALIZATION_ERROR(Optimizer, nullptr, optimizer);
TEST_INITIALIZATION_ERROR(Transform, nullptr, transform);
TEST_INITIALIZATION_ERROR(FixedImage, nullptr, fixedImage);
TEST_INITIALIZATION_ERROR(MovingImage, nullptr, movingImage);
TEST_INITIALIZATION_ERROR(Interpolator, nullptr, interpolator);
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
|
