ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv4/ImageRegistration6.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceR10X13Y17.png}
// OUTPUTS: {ImageRegistration6Output.png}
// OUTPUTS: {ImageRegistration6DifferenceBefore.png}
// OUTPUTS: {ImageRegistration6DifferenceAfter.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{CenteredRigid2DTransform}
// for performing registration. The example code is for the most part
// identical to the one presented in Section~\ref{sec:RigidRegistrationIn2D}.
// Even though this current example is done in $2D$, the class
// \doxygen{CenteredTransformInitializer} is quite generic and could be used
// in other dimensions. The objective of the initializer class is to simplify
// the computation of the center of rotation and the translation required to
// initialize certain transforms such as the
// CenteredRigid2DTransform. The initializer accepts two images and
// a transform as inputs. The images are considered to be the fixed and
// moving images of the registration problem, while the transform is the one
// used to register the images.
//
// The CenteredRigid2DTransform supports two modes of operation. In the first
// mode, the centers of the images are computed as space coordinates using the
// image origin, size and spacing. The center of the fixed image is assigned as
// the rotational center of the transform while the vector going from the fixed
// image center to the moving image center is passed as the initial translation
// of the transform. In the second mode, the image centers are not computed
// geometrically but by using the moments of the intensity gray levels. The
// center of mass of each image is computed using the helper class
// \doxygen{ImageMomentsCalculator}. The center of mass of the fixed image is
// passed as the rotational center of the transform while the vector going from
// the fixed image center of mass to the moving image center of mass is passed
// as the initial translation of the transform. This second mode of operation
// is quite convenient when the anatomical structures of interest are not
// centered in the image. In such cases the alignment of the centers of mass
// provides a better rough initial registration than the simple use of the
// geometrical centers. The validity of the initial registration should be
// questioned when the two images are acquired in different imaging modalities.
// In those cases, the center of mass of intensities in one modality does not
// necessarily match the center of mass of intensities in the other imaging
// modality.
//
// \index{itk::CenteredRigid2DTransform}
// \index{itk::ImageMomentsCalculator}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers in this example.
//
//
// 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) ITK_OVERRIDE
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event) ITK_OVERRIDE
{
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::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 type is instantiated using the code below. The only
// template parameter of this class is the representation type of the
// space coordinates.
//
// \index{itk::CenteredRigid2DTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
FixedImageType,
MovingImageType > MetricType;
FixedImageType,
MovingImageType > 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
//
// Like the previous section, a direct initialization method is used here.
// The transform object is constructed below. This transform will
// be initialized, and its initial parameters will be considered as
// the parameters to be used when the registration process begins.
//
// \index{itk::CenteredRigid2DTransform!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input images are taken from readers. It is not necessary to
// explicitly call \code{Update()} on the readers since the
// CenteredTransformInitializer class will do it as part of its
// initialization. The following code instantiates the initializer. This
// class is templated over the fixed and moving images type as well as the
// transform type. An initializer is then constructed by calling the
// \code{New()} method and assigning the result to a
// \doxygen{SmartPointer}.
//
// \index{itk::CenteredRigid2DTransform!Instantiation}
// \index{itk::CenteredRigid2DTransform!New()}
// \index{itk::CenteredRigid2DTransform!SmartPointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType,
FixedImageType,
MovingImageType > TransformInitializerType;
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The initializer is now connected to the transform and to the fixed and
// moving images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initializer->SetTransform( transform );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The use of the geometrical centers is selected by calling
// \code{GeometryOn()} while the use of center of mass is selected by
// calling \code{MomentsOn()}. Below we select the center of mass mode.
//
// \index{CenteredTransformInitializer!MomentsOn()}
// \index{CenteredTransformInitializer!GeometryOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initializer->MomentsOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the computation of the center and translation is triggered by
// the \code{InitializeTransform()} method. The resulting values will be
// passed directly to the transform.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The remaining parameters of the transform are initialized as before.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetAngle( 0.0 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the initialized transform object will be set to the registration method,
// and the starting point of the registration is defined by its initial parameters.
//
// If the \code{InPlaceOn()} method is called, this initialized transform will be the output transform
// object or grafted'' to the output. Otherwise, this InitialTransform'' will be deep-copied or
// cloned'' to the output.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform( transform );
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
typedef OptimizerType::ScalesType OptimizerScalesType;
OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = translationScale;
optimizerScales[2] = translationScale;
optimizerScales[3] = translationScale;
optimizerScales[4] = translationScale;
optimizer->SetScales( optimizerScales );
optimizer->SetLearningRate( 0.1 );
optimizer->SetMinimumStepLength( 0.001 );
optimizer->SetNumberOfIterations( 200 );
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
// 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;
}
// Software Guide : BeginLatex
//
// Since the registration filter has \code{InPlace} set, the transform object
// is grafted to the output and is updated by the registration method.
//
// Software Guide : EndLatex
TransformType::ParametersType finalParameters = transform->GetParameters();
const double finalAngle = finalParameters[0];
const double finalRotationCenterX = finalParameters[1];
const double finalRotationCenterY = finalParameters[2];
const double finalTranslationX = finalParameters[3];
const double finalTranslationY = finalParameters[4];
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 << "Result = " << 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{BrainProtonDensitySliceR10X13Y17.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first
// image by $10$ degrees and shifting it $13mm$ in $X$ and $17mm$ in
// $Y$. Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5}. The registration takes $22$
// iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [0.17429, 111.172, 131.563, 12.4582, 16.0724]
// \end{verbatim}
// \end{center}
//
// These parameters are interpreted as
//
// \begin{itemize}
// \item Angle = $0.17429$ radians
// \item Center = $( 111.172 , 131.563 )$ millimeters
// \item Translation = $( 12.4582 , 16.0724 )$ millimeters
// \end{itemize}
//
// Note that the reported translation is not the translation of $(13,17)$
// that might be expected. The reason is that the five parameters of the
// CenteredRigid2DTransform are redundant. The actual movement
// in space is described by only $3$ parameters. This means that there are
// infinite combinations of rotation center and translations that will
// represent the same actual movement in space. It is more illustrative in
// this case to take a look at the actual rotation matrix and offset
// resulting from the five parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::MatrixType matrix = transform->GetMatrix();
TransformType::OffsetType offset = transform->GetOffset();
std::cout << "Matrix = " << std::endl << matrix << std::endl;
std::cout << "Offset = " << std::endl << offset << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Which produces the following output.
//
// \begin{verbatim}
// Matrix =
// 0.98485 -0.173409
// 0.173409 0.98485
//
// Offset =
// [36.9567, -1.21272]
// \end{verbatim}
//
// This output illustrates how counter-intuitive the mix of center of
// rotation and translations can be. Figure
// \ref{fig:TranslationAndRotationCenter} will clarify this situation. The
// figure shows the original image on the left. A rotation of $10^{\circ}$
// around the center of the image is shown in the middle. The same rotation
// performed around the origin of coordinates is shown on the right. It can
// be seen here that changing the center of rotation introduces additional
// translations.
//
// Let's analyze what happens to the center of the image that we just
// registered. Under the point of view of rotating $10^{\circ}$ around the
// center and then applying a translation of $(13mm,17mm)$. The image has
// a size of $(221 \times 257)$ pixels and unit spacing. Hence its center
// has coordinates $(110.5,128.5)$. Since the rotation is done around this
// point, the center behaves as the fixed point of the transformation and
// remains unchanged. Then with the $(13mm,17mm)$ translation it is mapped
// to $(123.5,145.5)$ which becomes its final position.
//
// The matrix and offset that we obtained at the end of the registration
// indicate that this should be equivalent to a rotation of $10^{\circ}$
// around the origin, followed by a translation of $(36.95,-1.21)$. Let's
// compute this in detail. First the rotation of the image center by
// $10^{\circ}$ around the origin will move the point to
// $(86.52,147.97)$. Now, applying a translation of $(36.95,-1.21)$ maps
// this point to $(123.47,146.76)$, which is close to the result of our
// previous computation.
//
// It is unlikely that we could have chosen these translations as the
// initial guess, since we tend to think about images in a coordinate
// system whose origin is in the center of the image.
//
// \begin{figure}
// \center
// \includegraphics[width=\textwidth]{TranslationAndRotationCenter}
// \itkcaption[Effect of changing the center of rotation]{Effect of changing
// the center of rotation.}
// \label{fig:TranslationAndRotationCenter}
// \end{figure}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// You may be wondering why the actual movement is represented by three
// parameters when we take the trouble of using five. In particular, why
// use a $5$-dimensional optimizer space instead of a $3$-dimensional
// one? The answer is that by using five parameters we have a much simpler
// way of initializing the transform with the rotation matrix and
// offset. Using the minimum three parameters it is not obvious how to
// determine what the initial rotation and translations should be.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
// \itkcaption[CenteredTransformInitializer input images]{Fixed and moving
// images provided as input to the registration method using
// CenteredTransformInitializer.}
// \label{fig:FixedMovingImageRegistration6}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration6Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration6DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration6DifferenceAfter}
// \itkcaption[CenteredTransformInitializer output images]{Resampled moving
// image (left). Differences between fixed and moving images, before
// registration (center) and after registration (right) with the
// CenteredTransformInitializer.}
// \label{fig:ImageRegistration6Outputs}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration6Outputs} shows the output of the
// registration. The image on the right of this figure shows the differences
// between the fixed image and the resampled moving image after registration.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration6TraceMetric}
// \includegraphics[height=0.32\textwidth]{ImageRegistration6TraceAngle}
// \includegraphics[height=0.32\textwidth]{ImageRegistration6TraceTranslations}
// \itkcaption[CenteredTransformInitializer output plots]{Plots of the Metric,
// rotation angle, center of rotation and translations during the
// registration using CenteredTransformInitializer.}
// \label{fig:ImageRegistration6Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration6Plots} plots the output parameters of
// the registration process. It includes the metric values at every
// iteration, the angle values at every iteration, and the values of the
// translation components as the registration progresses. Note that this is
// the complementary translation as used in the transform, not the actual
// total translation that is used in the transform offset. We could modify
// the observer to print the total offset instead of printing the array of
// parameters. Let's call that an exercise for the reader!
//
// Software Guide : EndLatex
MovingImageType,
FixedImageType > ResampleFilterType;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transform );
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( 100 );
typedef unsigned char OutputPixelType;
FixedImageType,
OutputImageType > CastFilterType;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( resample->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
// Now compute the difference between the images
// before and after registration.
//
typedef itk::Image< float, Dimension > DifferenceImageType;
FixedImageType,
FixedImageType,
DifferenceImageType > DifferenceFilterType;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
typedef unsigned char OutputPixelType;
DifferenceImageType,
intensityRescaler->SetOutputMinimum( 0 );
intensityRescaler->SetOutputMaximum( 255 );
difference->SetInput2( resample->GetOutput() );
resample->SetDefaultPixelValue( 1 );
intensityRescaler->SetInput( difference->GetOutput() );
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput( intensityRescaler->GetOutput() );
try
{
// Compute the difference image between the
// fixed and moving image after registration.
if( argc > 5 )
{
writer2->SetFileName( argv[5] );
writer2->Update();
}
// Compute the difference image between the
// fixed and resampled moving image after registration.
TransformType::Pointer identityTransform = TransformType::New();
identityTransform->SetIdentity();
resample->SetTransform( identityTransform );
if( argc > 4 )
{
writer2->SetFileName( argv[4] );
writer2->Update();
}
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Error while writing difference images" << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}