forked from InsightSoftwareConsortium/ITK
-
Notifications
You must be signed in to change notification settings - Fork 0
/
ImageRegistration7.cxx
566 lines (452 loc) · 19.3 KB
/
ImageRegistration7.cxx
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
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
/*=========================================================================
*
* 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.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceR10X13Y17S12.png}
// OUTPUTS: {ImageRegistration7Output.png}
// OUTPUTS: {ImageRegistration7DifferenceBefore.png}
// OUTPUTS: {ImageRegistration7DifferenceAfter.png}
// ARGUMENTS: 1.0 1.0 0.0
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{CenteredSimilarity2DTransform}
// class for performing registration in $2D$. The of example code is for
// the most part identical to the code presented in Section
// \ref{sec:InitializingRegistrationWithMoments}. The main difference is the
// use of \doxygen{CenteredSimilarity2DTransform} here rather than the
// \doxygen{CenteredRigid2DTransform} class.
//
// A similarity transform can be seen as a composition of rotations,
// translations and uniform scaling. It preserves angles and map lines into
// lines. This transform is implemented in the toolkit as deriving from a rigid
// $2D$ transform and with a scale parameter added.
//
// When using this transform, attention should be paid to the fact that scaling
// and translations are not independent. In the same way that rotations can
// locally be seen as translations, scaling also result in local displacements.
// Scaling is performed in general with respect to the origin of coordinates.
// However, we already saw how ambiguous that could be in the case of
// rotations. For this reason, this transform also allows users to setup a
// specific center. This center is use both for rotation and scaling.
//
//
// \index{itk::CenteredSimilarity2DTransform}
//
// Software Guide : EndLatex
#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkCenteredTransformInitializer.h"
// Software Guide : BeginLatex
//
// In addition to the headers included in previous examples, here the
// following header must be included.
//
// \index{itk::CenteredSimilarity2DTransform!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkCenteredSimilarity2DTransform.h"
// Software Guide : EndCodeSnippet
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkIdentityTransform.h"
// The following section of code implements a Command observer
// that will monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
typedef CommandIterationUpdate Self;
typedef itk::Command Superclass;
typedef itk::SmartPointer<Self> Pointer;
itkNewMacro( Self );
protected:
CommandIterationUpdate() {};
public:
typedef itk::RegularStepGradientDescentOptimizer OptimizerType;
typedef const OptimizerType * OptimizerPointer;
void Execute(itk::Object *caller, const itk::EventObject & event)
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event)
{
OptimizerPointer optimizer = static_cast< OptimizerPointer >( object );
if( ! itk::IterationEvent().CheckEvent( &event ) )
{
return;
}
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << optimizer->GetValue() << " ";
std::cout << optimizer->GetCurrentPosition() << std::endl;
}
};
int main( int argc, char *argv[] )
{
if( argc < 4 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile ";
std::cerr << " outputImagefile [differenceBeforeRegistration] ";
std::cerr << " [differenceAfterRegistration] ";
std::cerr << " [steplength] ";
std::cerr << " [initialScaling] [initialAngle] ";
std::cerr << std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 2;
typedef float PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
// Software Guide : BeginLatex
//
// The Transform class is instantiated using the code below. The only
// template parameter of this class is the representation type of the
// space coordinates.
//
// \index{itk::CenteredSimilarity2DTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::CenteredSimilarity2DTransform< double > TransformType;
// Software Guide : EndCodeSnippet
typedef itk::RegularStepGradientDescentOptimizer OptimizerType;
typedef itk::MeanSquaresImageToImageMetric< FixedImageType, MovingImageType >
MetricType;
typedef itk:: LinearInterpolateImageFunction< MovingImageType, double >
InterpolatorType;
typedef itk::ImageRegistrationMethod< FixedImageType, MovingImageType >
RegistrationType;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetInterpolator( interpolator );
// Software Guide : BeginLatex
//
// The transform object is constructed below and passed to the registration
// method.
//
// \index{itk::CenteredSimilarity2DTransform!New()}
// \index{itk::CenteredSimilarity2DTransform!Pointer}
// \index{itk::RegistrationMethod!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
registration->SetTransform( transform );
// Software Guide : EndCodeSnippet
typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
movingImageReader->SetFileName( argv[2] );
registration->SetFixedImage( fixedImageReader->GetOutput() );
registration->SetMovingImage( movingImageReader->GetOutput() );
fixedImageReader->Update();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion() );
// Software Guide : BeginLatex
//
// In this example, we again use the helper class
// \doxygen{CenteredTransformInitializer} to compute a reasonable
// value for the initial center of rotation and the translation.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::CenteredTransformInitializer<
TransformType, FixedImageType,
MovingImageType > TransformInitializerType;
TransformInitializerType::Pointer initializer
= TransformInitializerType::New();
initializer->SetTransform( transform );
initializer->SetFixedImage( fixedImageReader->GetOutput() );
initializer->SetMovingImage( movingImageReader->GetOutput() );
initializer->MomentsOn();
initializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The remaining parameters of the transform are initialized below.
//
// \index{itk::CenteredSimilarity2DTransform!SetScale()}
// \index{itk::CenteredSimilarity2DTransform!SetAngle()}
//
// Software Guide : EndLatex
double initialScale = 1.0;
if( argc > 7 )
{
initialScale = atof( argv[7] );
}
double initialAngle = 0.0;
if( argc > 8 )
{
initialAngle = atof( argv[8] );
}
// Software Guide : BeginCodeSnippet
transform->SetScale( initialScale );
transform->SetAngle( initialAngle );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We now pass the parameter of the current transform as the initial
// parameters to be used when the registration process starts.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransformParameters( transform->GetParameters() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Keeping in mind that the scale of units in scaling, rotation and
// translation are quite different, we take advantage of the scaling
// functionality provided by the optimizers. We know that the first element
// of the parameters array corresponds to the scale factor, the second
// corresponds to the angle, third and forth are the center of rotation and
// fifth and sixth are the remaining translation. We use henceforth small
// factors in the scales associated with translations and the rotation
// center.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef OptimizerType::ScalesType OptimizerScalesType;
OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
const double translationScale = 1.0 / 100.0;
optimizerScales[0] = 10.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = translationScale;
optimizerScales[3] = translationScale;
optimizerScales[4] = translationScale;
optimizerScales[5] = translationScale;
optimizer->SetScales( optimizerScales );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We set also the normal parameters of the optimization method. In this
// case we are using A
// \doxygen{RegularStepGradientDescentOptimizer}. Below, we define the
// optimization parameters like initial step length, minimal step length
// and number of iterations. These last two act as stopping criteria for
// the optimization.
//
// Software Guide : EndLatex
double steplength = 1.0;
if( argc > 6 )
{
steplength = atof( argv[6] );
}
// Software Guide : BeginCodeSnippet
optimizer->SetMaximumStepLength( steplength );
optimizer->SetMinimumStepLength( 0.0001 );
optimizer->SetNumberOfIterations( 500 );
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
OptimizerType::ParametersType finalParameters =
registration->GetLastTransformParameters();
const double finalScale = finalParameters[0];
const double finalAngle = finalParameters[1];
const double finalRotationCenterX = finalParameters[2];
const double finalRotationCenterY = finalParameters[3];
const double finalTranslationX = finalParameters[4];
const double finalTranslationY = finalParameters[5];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Print out results
//
const double finalAngleInDegrees = finalAngle * 180.0 / itk::Math::pi;
std::cout << std::endl;
std::cout << "Result = " << std::endl;
std::cout << " Scale = " << finalScale << std::endl;
std::cout << " Angle (radians) " << finalAngle << std::endl;
std::cout << " Angle (degrees) " << finalAngleInDegrees << std::endl;
std::cout << " Center X = " << finalRotationCenterX << std::endl;
std::cout << " Center Y = " << finalRotationCenterY << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images provided in
// \code{Examples/Data}, for example:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceR10X13Y17S12.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first image
// by $10$ degrees, scaling by $1/1.2$ and then translating by $(-13,-17)$.
// Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration7}. The registration takes $16$
// iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [0.833222, -0.174521, 111.437, 131.741, -12.8272, -12.7862]
// \end{verbatim}
// \end{center}
//
// That are interpreted as
//
// \begin{itemize}
// \item Scale factor = $0.833222$
// \item Angle = $0.174521$ radians
// \item Center = $( 111.437 , 131.741 )$ millimeters
// \item Translation = $( -12.8272 , -12.7862 )$ millimeters
// \end{itemize}
//
//
// These values approximate the misalignment intentionally introduced into
// the moving image. Since $10$ degrees is about $0.174532$ radians.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17S12}
// \itkcaption[Fixed and Moving image registered with
// CenteredSimilarity2DTransform]{Fixed and Moving image provided as input to the
// registration method using the Similarity2D transform.}
// \label{fig:FixedMovingImageRegistration7}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration7Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration7DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration7DifferenceAfter}
// \itkcaption[Output of the CenteredSimilarity2DTransform registration]{Resampled
// moving image (left). Differences between fixed and
// moving images, before (center) and after (right) registration with the
// Similarity2D transform.}
// \label{fig:ImageRegistration7Outputs}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration7Outputs} shows the output of the
// registration. The right image shows the squared magnitude of pixel
// differences between the fixed image and the resampled moving image.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceMetric}
// \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceAngle}
// \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceScale}
// \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceTranslations}
// \itkcaption[CenteredSimilarity2DTransform registration plots]{Plots of the Metric,
// rotation angle and translations during
// the registration using
// Similarity2D transform.}
// \label{fig:ImageRegistration7Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration7Plots} shows the plots of the main
// output parameters of the registration process. The metric values at every
// iteration are shown on the top. The angle values are shown in the plot at
// left while the translation components of the registration are presented
// in the plot at right.
//
// Software Guide : EndLatex
typedef itk::ResampleImageFilter< MovingImageType,
FixedImageType > ResampleFilterType;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetTransform( finalTransform );
resampler->SetInput( movingImageReader->GetOutput() );
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resampler->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resampler->SetOutputOrigin( fixedImage->GetOrigin() );
resampler->SetOutputSpacing( fixedImage->GetSpacing() );
resampler->SetOutputDirection( fixedImage->GetDirection() );
resampler->SetDefaultPixelValue( 100 );
typedef unsigned char OutputPixelType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::CastImageFilter< FixedImageType, OutputImageType >
CastFilterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( resampler->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
typedef itk::SubtractImageFilter<
FixedImageType,
FixedImageType,
FixedImageType > DifferenceFilterType;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
typedef itk::RescaleIntensityImageFilter<
FixedImageType,
OutputImageType > RescalerType;
RescalerType::Pointer intensityRescaler = RescalerType::New();
intensityRescaler->SetInput( difference->GetOutput() );
intensityRescaler->SetOutputMinimum( 0 );
intensityRescaler->SetOutputMaximum( 255 );
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( resampler->GetOutput() );
resampler->SetDefaultPixelValue( 1 );
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput( intensityRescaler->GetOutput() );
// Compute the difference image between the
// fixed and resampled moving image.
if( argc > 5 )
{
writer2->SetFileName( argv[5] );
writer2->Update();
}
typedef itk::IdentityTransform< double, Dimension > IdentityTransformType;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
// Compute the difference image between the
// fixed and moving image before registration.
if( argc > 4 )
{
resampler->SetTransform( identity );
writer2->SetFileName( argv[4] );
writer2->Update();
}
return EXIT_SUCCESS;
}