ITK  4.6.0
Insight Segmentation and Registration Toolkit
RegistrationITKv4/ImageRegistration7.cxx
/*=========================================================================
*
* 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 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 \left(\isotropic\right) 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
// 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
// Software Guide : EndCodeSnippet
// 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;
itkNewMacro( Self );
protected:
CommandIterationUpdate() {};
public:
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 =
dynamic_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
// Software Guide : EndCodeSnippet
typedef itk::MeanSquaresImageToImageMetricv4< FixedImageType,
MovingImageType > MetricType;
typedef itk::ImageRegistrationMethodv4< FixedImageType,
MovingImageType,
TransformType > RegistrationType;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
// Software Guide : BeginLatex
//
// As before, the transform object is constructed and is initialized before it
// is passed to the registration filter.
//
// \index{itk::CenteredSimilarity2DTransform!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
// 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() );
// 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
//
// Now the initialized transform object will be set to the registration method,
// and its initial parameters are used to initialize the registration process.
//
// Also, by calling the \code{InPlaceOn()}, this initialized transform will be the output transform
// object or "grafted" to the output of the registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform( transform );
registration->InPlaceOn();
// 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 ordinary parameters of the optimization method. In this
// case we are using a
// \doxygen{RegularStepGradientDescentOptimizerv4}. Below defines the
// optimization parameters like initial learning rate (step length), minimal
// step length and number of iterations. The 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->SetLearningRate( 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 );
// One level registration process without shrinking and smoothing.
//
const unsigned int numberOfLevels = 1;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize( 1 );
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize( 1 );
smoothingSigmasPerLevel[0] = 0;
registration->SetNumberOfLevels ( numberOfLevels );
registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );
registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );
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;
}
TransformType::ParametersType finalParameters =
transform->GetParameters();
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 / vnl_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 $60$
// iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [0.833193, -0.174514, 111.025, 131.92, -12.7267, -12.757]
// \end{verbatim}
// \end{center}
//
// That are interpreted as
//
// \begin{itemize}
// \item Scale factor = $0.833193$
// \item Angle = $-0.174514$ radians
// \item Center = $( 111.025 , 131.92 )$ millimeters
// \item Translation = $( -12.7267 , -12.757 )$ 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;
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetTransform( transform );
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;
CastFilterType;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( resampler->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
FixedImageType,
FixedImageType,
FixedImageType > DifferenceFilterType;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
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;
}