ITK  5.4.0 Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration1.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceShifted13x17y.png}
// OUTPUTS: {ImageRegistration1Output.png}
// OUTPUTS: {ImageRegistration1DifferenceAfter.png}
// OUTPUTS: {ImageRegistration1DifferenceBefore.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the image registration framework in
// Insight. It should be read as a Hello World'' for ITK registration.
// Instead of means to an end, this example should be read as a basic
// introduction to the elements typically involved when solving a problem
// of image registration.
//
// \index{itk::Image!Instantiation}
//
// A registration method requires the following set of components: two input
// images, a transform, a metric and an optimizer. Some of these components
// are parameterized by the image type for which the registration is intended.
// The following header files provide declarations of common types used for
// these components.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
using OptimizerPointer = const OptimizerType *;
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
auto 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 [differenceImageAfter]";
std::cerr << "[differenceImageBefore] [useEstimator]" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// The type of each registration component should
// be instantiated first. We start by selecting the image
// dimension and the types to be used for representing image pixels.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 2;
using PixelType = float;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The types of the input images are instantiated by the following lines.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The transform that will map the fixed image space into the moving image
// space is defined below.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// An optimizer is required to explore the parameter space of the transform
// in search of optimal values of the metric.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The metric will compare how well the two images match each other. Metric
// types are usually templated over the image types as seen in
// the following type declaration.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MetricType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The registration method type is instantiated using the types of the
// fixed and moving images as well as the output transform type. This class
// is responsible for interconnecting all the components that we have
// described so far.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using RegistrationType = itk::
ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Each one of the registration components is created using its
// \code{New()} method and is assigned to its respective
// \doxygen{SmartPointer}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto metric = MetricType::New();
auto optimizer = OptimizerType::New();
auto registration = RegistrationType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Each component is now connected to the instance of the registration
// method.
//
// \index{itk::RegistrationMethodv4!SetMetric()}
// \index{itk::RegistrationMethodv4!SetOptimizer()}
// \index{itk::RegistrationMethodv4!SetFixedImage()}
// \index{itk::RegistrationMethodv4!SetMovingImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this example the transform object does not need to be created and
// passed to the registration method like above since the registration
// filter will instantiate an internal transform object using the transform
// type that is passed to it as a template parameter.
//
// Metric needs an interpolator to evaluate the intensities of the fixed
// and moving images at non-grid positions. The types of fixed and moving
// interpolators are declared here.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FixedLinearInterpolatorType =
using MovingLinearInterpolatorType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then, fixed and moving interpolators are created and passed to the
// metric. Since linear interpolators are used as default, we could skip
// the following step in this example.
//
// \index{itk::MeanSquaresImageToImageMetricv4!SetFixedInterpolator()}
// \index{itk::MeanSquaresImageToImageMetricv4!SetMovingInterpolator()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto fixedInterpolator = FixedLinearInterpolatorType::New();
auto movingInterpolator = MovingLinearInterpolatorType::New();
metric->SetFixedInterpolator(fixedInterpolator);
metric->SetMovingInterpolator(movingInterpolator);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this example, the fixed and moving images are read from files. This
// requires the \doxygen{ImageRegistrationMethodv4} to acquire its inputs
// from the output of the readers.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the registration process should be initialized. ITKv4 registration
// framework provides initial transforms for both fixed and moving images.
// These transforms can be used to setup an initial known correction of the
// misalignment between the virtual domain and fixed/moving image spaces.
// In this particular case, a translation transform is being used for
// initialization of the moving image space.
// The array of parameters for the initial moving transform is simply
// composed of the translation values along each dimension. Setting the
// values of the parameters to zero initializes the transform to an
// \emph{Identity} transform. Note that the array constructor requires the
// number of elements to be passed as an argument.
//
// \index{itk::TranslationTransform!GetNumberOfParameters()}
// \index{itk::RegistrationMethodv4!SetMovingInitialTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto movingInitialTransform = TransformType::New();
TransformType::ParametersType initialParameters(
movingInitialTransform->GetNumberOfParameters());
initialParameters[0] = 0.0; // Initial offset in mm along X
initialParameters[1] = 0.0; // Initial offset in mm along Y
movingInitialTransform->SetParameters(initialParameters);
registration->SetMovingInitialTransform(movingInitialTransform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the registration filter this moving initial transform will be added
// to a composite transform that already includes an instantiation of the
// output optimizable transform; then, the resultant composite transform
// will be used by the optimizer to evaluate the metric values at each
// iteration.
//
// Despite this, the fixed initial transform does not contribute to the
// optimization process. It is only used to access the fixed image from the
// virtual image space where the metric evaluation happens.
//
// Virtual images are a new concept added to the ITKv4 registration
// framework, which potentially lets us to do the registration process in a
// physical domain totally different from the fixed and moving image
// domains. In fact, the region over which metric evaluation is performed
// is called virtual image domain. This domain defines the resolution at
// which the evaluation is performed, as well as the physical coordinate
// system.
//
// The virtual reference domain is taken from the virtual image''
// buffered region, and the input images should be accessed from this
// reference space using the fixed and moving initial transforms.
//
// The legacy intuitive registration framework can be considered as a
// special case where the virtual domain is the same as the fixed image
// domain. As this case practically happens in most of the real life
// applications, the virtual image is set to be the same as the fixed image
// by default. However, the user can define the virtual domain differently
// than the fixed image domain by calling either \code{SetVirtualDomain} or
// \code{SetVirtualDomainFromImage}.
//
// In this example, like the most examples of this chapter, the virtual
// image is considered the same as the fixed image. Since the registration
// process happens in the fixed image physical domain, the fixed initial
// transform maintains its default value of identity and does not need to
// be set.
//
// However, a Hello World!'' example should show all the basics, so
// all the registration components are explicitly set here.
//
// In the next section of this chapter, you will get a better understanding
// from behind the scenes of the registration process when the initial
// fixed transform is not identity.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto identityTransform = TransformType::New();
identityTransform->SetIdentity();
registration->SetFixedInitialTransform(identityTransform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that the above process shows only one way of initializing the
// registration configuration. Another option is to initialize the output
// optimizable transform directly. In this approach, a transform object is
// created, initialized, and then passed to the registration method via
// \code{SetInitialTransform()}. This approach is shown in
// section~\ref{sec:RigidRegistrationIn2D}.
//
// At this point the registration method is ready for execution. The
// optimizer is the component that drives the execution of the
// registration. However, the ImageRegistrationMethodv4 class
// orchestrates the ensemble to make sure that everything is in place
// before control is passed to the optimizer.
//
// It is usually desirable to fine tune the parameters of the optimizer.
// Each optimizer has particular parameters that must be interpreted in the
// context of the optimization strategy it implements. The optimizer used
// in this example is a variant of gradient descent that attempts to
// prevent it from taking steps that are too large. At each iteration, this
// optimizer will take a step along the direction of the
// \doxygen{ImageToImageMetricv4} derivative. Each time the direction of
// the derivative abruptly changes, the optimizer assumes that a local
// extrema has been passed and reacts by reducing the step length by a
// relaxation factor. The reducing factor should have a value between 0
// and 1. This factor is set to 0.5 by default, and it can be changed to a
// different value via \code{SetRelaxationFactor()}. Also, the default
// value for the initial step length is 1, and this value can be changed
// manually with the method \code{SetLearningRate()}.
//
// In addition to manual settings, the initial step size can also be
// estimated automatically, either at each iteration or only at the first
// iteration, by assigning a ScalesEstimator (as will be seen in later
// examples).
//
// After several reductions of the step length, the optimizer may be moving
// in a very restricted area of the transform parameter space. By the
// method \code{SetMinimumStepLength()}, the user can define how small the
// step length should be to consider convergence to have been reached. This
// is equivalent to defining the precision with which the final transform
// should be known. User can also set some other stop criteria manually
// like maximum number of iterations.
//
// In other gradient descent-based optimizers of the ITKv4 framework, such
// criteria are set via \code{SetMinimumConvergenceValue()} which is
// computed based on the results of the last few iterations. The number of
// iterations involved in computations are defined by the convergence
// window size via \code{SetConvergenceWindowSize()} which is shown in
// later examples of this chapter.
//
// Also note that unlike the previous versions, ITKv4 optimizers do not
// have a
// maximize/minimize'' option to modify the effect of the metric
// derivatives. Each assigned metric is assumed to return a parameter
// derivative result that "improves" the optimization.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetLearningRate(4);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetRelaxationFactor(0.5);
// Software Guide : EndCodeSnippet
bool useEstimator = false;
if (argc > 6)
{
useEstimator = std::stoi(argv[6]) != 0;
}
if (useEstimator)
{
using ScalesEstimatorType =
auto scalesEstimator = ScalesEstimatorType::New();
scalesEstimator->SetMetric(metric);
scalesEstimator->SetTransformForward(true);
optimizer->SetScalesEstimator(scalesEstimator);
optimizer->SetDoEstimateLearningRateOnce(true);
}
// Software Guide : BeginLatex
//
// In case the optimizer never succeeds reaching the desired
// precision tolerance, it is prudent to establish a limit on the number of
// iterations to be performed. This maximum number is defined with the
// method \code{SetNumberOfIterations()}.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetNumberOfIterations(200);
// Software Guide : EndCodeSnippet
// Connect an observer
auto observer = CommandIterationUpdate::New();
// Software Guide : BeginLatex
//
// ITKv4 facilitates a multi-level registration framework whereby each
// stage is different in the resolution of its virtual space and the
// smoothness of the fixed and moving images. These criteria need to be
// defined before registration starts. Otherwise, the default values will
// be used. In this example, we run a simple registration in one level with
// no space shrinking or smoothing on the input data.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr 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);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The registration process is triggered by an invocation of the
// \code{Update()} method. If something goes wrong during the
// initialization or execution of the registration an exception will be
// thrown. We should therefore place the \code{Update()} method
// inside a \code{try/catch} block as illustrated in the following lines.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In a real life application, you may attempt to recover from the error by
// taking more effective actions in the catch block. Here we are simply
// printing out a message and then terminating the execution of the
// program.
//
//
// The result of the registration process is obtained using the
// \code{GetTransform()} method that returns a constant pointer to the
// output transform.
//
// \index{itk::ImageRegistrationMethodv4!GetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::ConstPointer transform = registration->GetTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the case of the \doxygen{TranslationTransform}, there is a
// straightforward interpretation of the parameters. Each element of the
// array corresponds to a translation along one spatial dimension.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::ParametersType finalParameters = transform->GetParameters();
const double TranslationAlongX = finalParameters[0];
const double TranslationAlongY = finalParameters[1];
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The optimizer can be queried for the actual number of iterations
// performed to reach convergence. The \code{GetCurrentIteration()}
// method returns this value. A large number of iterations may be an
// indication that the learning rate has been set too small, which
// is undesirable since it results in long computational times.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The value of the image metric corresponding to the last set of
// parameters can be obtained with the \code{GetValue()} method of the
// optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const double bestValue = optimizer->GetValue();
// Software Guide : EndCodeSnippet
// Print out results
//
std::cout << "Result = " << std::endl;
std::cout << " Translation X = " << TranslationAlongX << std::endl;
std::cout << " Translation Y = " << TranslationAlongY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over two of the images provided in
// \code{Examples/Data}:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceShifted13x17y.png}
// \end{itemize}
//
// The second image is the result of intentionally translating the first
// image by $(13,17)$ millimeters. Both images have unit-spacing and
// are shown in Figure \ref{fig:FixedMovingImageRegistration1}. The
// registration takes 20 iterations and the resulting transform parameters
// are:
//
// \begin{verbatim}
// Translation X = 13.0012
// Translation Y = 16.9999
// \end{verbatim}
//
// As expected, these values match quite well the misalignment that we
// intentionally introduced in the moving image.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceShifted13x17y}
// \itkcaption[Fixed and Moving images in registration framework]{Fixed and
// Moving image provided as input to the registration method.}
// \label{fig:FixedMovingImageRegistration1}
// \end{figure}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// It is common, as the last step of a registration task, to use the
// resulting transform to map the moving image into the fixed image space.
//
// Before the mapping process, notice that we have not used the direct
// initialization of the output transform in this example, so the
// parameters of the moving initial transform are not reflected in the
// output parameters of the registration filter. Hence, a composite
// transform is needed to concatenate both initial and output transforms
// together.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using CompositeTransformType = itk::CompositeTransform<double, Dimension>;
auto outputCompositeTransform = CompositeTransformType::New();
registration->GetModifiableTransform());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the mapping process is easily done with the
// Section~\ref{sec:ResampleImageFilter} for details on the use of this
// filter. First, a ResampleImageFilter type is instantiated using the
// image types. It is convenient to use the fixed image type as the output
// type since it is likely that the transformed moving image will be
// compared with the fixed image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ResampleFilterType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// A resampling filter is created and the moving image is connected as
// its input.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto resampler = ResampleFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The created output composite transform is also passed as input to the
// resampling filter.
//
// \index{itk::ImageRegistrationMethod!Resampling image}
// \index{itk::ImageRegistrationMethod!Pipeline}
// \index{itk::ImageRegistrationMethod!DataObjectDecorator}
// \index{itk::ImageRegistrationMethod!GetOutput()}
// \index{itk::DataObjectDecorator!Use in Registration}
// \index{itk::DataObjectDecorator!Get()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
resampler->SetTransform(outputCompositeTransform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As described in Section \ref{sec:ResampleImageFilter}, the
// ResampleImageFilter requires additional parameters to be specified, in
// particular, the spacing, origin and size of the output image. The
// default pixel value is also set to a distinct gray level in order to
// highlight the regions that are mapped outside of the moving image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resampler->SetOutputOrigin(fixedImage->GetOrigin());
resampler->SetOutputSpacing(fixedImage->GetSpacing());
resampler->SetOutputDirection(fixedImage->GetDirection());
resampler->SetDefaultPixelValue(100);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration1Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration1DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration1DifferenceAfter}
// \itkcaption[HelloWorld registration output images]{Mapped moving image
// and its difference with the fixed image before and after registration}
// \label{fig:ImageRegistration1Output}
// \end{figure}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The output of the filter is passed to a writer that will store the
// image in a file. An \doxygen{CastImageFilter} is used to convert the
// pixel type of the resampled image to the final type used by the
// writer. The cast and writer filters are instantiated below.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using CastFilterType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filters are created by invoking their \code{New()}
// method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto writer = WriterType::New();
auto caster = CastFilterType::New();
// Software Guide : EndCodeSnippet
writer->SetFileName(argv[3]);
// Software Guide : BeginLatex
//
// The filters are connected together and the \code{Update()} method of the
// writer is invoked in order to trigger the execution of the pipeline.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
caster->SetInput(resampler->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=\textwidth]{ImageRegistration1Pipeline}
// \itkcaption[Pipeline structure of the registration example]{Pipeline
// structure of the registration example.}
// \label{fig:ImageRegistration1Pipeline}
// \end{figure}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The fixed image and the transformed moving image can easily be compared
// using the \doxygen{SubtractImageFilter}. This pixel-wise filter computes
// the difference between homologous pixels of its two input images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using DifferenceFilterType =
auto difference = DifferenceFilterType::New();
difference->SetInput2(resampler->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that the use of subtraction as a method for comparing the images is
// appropriate here because we chose to represent the images using a pixel
// type \code{float}. A different filter would have been used if the pixel
// type of the images were any of the \code{unsigned} integer types.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Since the differences between the two images may correspond to very low
// values of intensity, we rescale those intensities with a
// \doxygen{RescaleIntensityImageFilter} in order to make them more
// visible. This rescaling will also make it possible to visualize the
// negative values even if we save the difference image in a file format
// that only supports unsigned pixel values\footnote{This is the case of
// PNG, BMP, JPEG and TIFF among other common file formats.}. We also
// reduce the \code{DefaultPixelValue} to 1'' in order to prevent that
// value from absorbing the dynamic range of the differences between the
// two images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
intensityRescaler->SetInput(difference->GetOutput());
intensityRescaler->SetOutputMinimum(0);
intensityRescaler->SetOutputMaximum(255);
resampler->SetDefaultPixelValue(1);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Its output can be passed to another writer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
auto writer2 = WriterType::New();
writer2->SetInput(intensityRescaler->GetOutput());
// Software Guide : EndCodeSnippet
if (argc > 4)
{
writer2->SetFileName(argv[4]);
writer2->Update();
}
// Software Guide : BeginLatex
//
// For the purpose of comparison, the difference between the fixed image
// and the moving image before registration can also be computed by simply
// setting the transform to an identity transform. Note that the resampling
// is still necessary because the moving image does not necessarily have
// the same spacing, origin and number of pixels as the fixed image.
// Therefore a pixel-by-pixel operation cannot in general be performed. The
// resampling process with an identity transform will ensure that we have a
// representation of the moving image in the grid of the fixed image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
resampler->SetTransform(identityTransform);
// Software Guide : EndCodeSnippet
if (argc > 5)
{
writer2->SetFileName(argv[5]);
writer2->Update();
}
// Software Guide : BeginLatex
//
// The complete pipeline structure of the current example is presented in
// Figure~\ref{fig:ImageRegistration1Pipeline}. The components of the
// registration method are depicted as well. Figure
// \ref{fig:ImageRegistration1Output} (left) shows the result of resampling
// the moving image in order to map it onto the fixed image space. The top
// and right borders of the image appear in the gray level selected with
// the \code{SetDefaultPixelValue()} in the ResampleImageFilter. The center
// image shows the difference between the fixed image and the original
// moving image (i.e. the difference before the registration is
// performed). The right image shows the difference between the fixed image
// and the transformed moving image (i.e. after the registration has
// been performed). Both difference images have been rescaled in intensity
// in order to highlight those pixels where differences exist. Note that
// the final registration is still off by a fraction of a pixel, which
// causes bands around edges of anatomical structures to appear in the
// difference image. A perfect registration would have produced a null
// difference image.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[height=0.44\textwidth]{ImageRegistration1TraceTranslations}
// \includegraphics[height=0.44\textwidth]{ImageRegistration1TraceMetric}
// \itkcaption[Trace of translations and metrics during registration]{The
// sequence of translations and metric values at each iteration of the
// optimizer.} \label{fig:ImageRegistration1Trace} \end{figure}
//
// It is always useful to keep in mind that registration is essentially an
// optimization problem. Figure \ref{fig:ImageRegistration1Trace} helps to
// reinforce this notion by showing the trace of translations and values of
// the image metric at each iteration of the optimizer. It can be seen from
// the top figure that the step length is reduced progressively as the
// optimizer gets closer to the metric extrema. The bottom plot clearly
// shows how the metric value decreases as the optimization advances. The
// log plot helps to highlight the normal oscillations of the optimizer
// around the extrema value.
//
// In this section, we used a very simple example to introduce the basic
// components of a registration process in ITKv4. However, studying this
// example alone is not enough to start using the
// \doxygen{ImageRegistrationMethodv4}. In order to choose the best
// registration practice for a specific application, knowledge of other
// registration method instantiations and their capabilities are required.
// For example, direct initialization of the output optimizable transform
// is shown in section~\ref{sec:RigidRegistrationIn2D}. This method can
// simplify the registration process in many cases. Also, multi-resolution
// and multistage registration approaches are illustrated in
// sections~\ref{sec:MultiResolutionRegistration} and
// ~\ref{sec:MultiStageRegistration}.
// These examples illustrate the flexibility in the usage of ITKv4
// registration method framework that can help to provide faster and more
// reliable registration processes.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
Pointer
SmartPointer< Self > Pointer
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:100
ConstPointer
SmartPointer< const Self > ConstPointer
itk::CompositeTransform
This class contains a list of transforms and concatenates them by composition.
Definition: itkCompositeTransform.h:87
itk::SmartPointer< Self >
itkCastImageFilter.h
itkImageRegistrationMethodv4.h
itkMeanSquaresImageToImageMetricv4.h
itkTranslationTransform.h
Data source that reads image data from a single file.
itk::LinearInterpolateImageFunction
Linearly interpolate an image at specified positions.
Definition: itkLinearInterpolateImageFunction.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:88
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:42
itk::TranslationTransform
Translation transformation of a vector space (e.g. space coordinates)
Definition: itkTranslationTransform.h:43
itkSubtractImageFilter.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::SubtractImageFilter
Pixel-wise subtraction of two images.
Definition: itkSubtractImageFilter.h:68
itkImageFileWriter.h
itk::MeanSquaresImageToImageMetricv4
Class implementing a mean squares metric.
Definition: itkMeanSquaresImageToImageMetricv4.h:46
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:90
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:61
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:147
itk::RegistrationParameterScalesFromPhysicalShift
Registration helper class for estimating scales of transform parameters a step sizes,...
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition: itkEventObject.h:57
New
static Pointer New()