ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/ImageRegistration4.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1SliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceShifted13x17y.png}
// OUTPUTS: {ImageRegistration4Output.png}
// ARGUMENTS: 100
// OUTPUTS: {ImageRegistration4CheckerboardBefore.png}
// OUTPUTS: {ImageRegistration4CheckerboardAfter.png}
// ARGUMENTS: 24
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// In this example, we will solve a simple multi-modality problem using another
// Mattes~\emph{et. al}~\cite{Mattes2003}. One of the main differences between
// \doxygen{MattesMutualInformationImageToImageMetric} and
// \doxygen{MutualInformationImageToImageMetric} is that only one spatial
// sample set is used for the whole registration process instead of using new
// samples every iteration. The use of a single sample set results in a much
// smoother cost function and hence allows the use of more intelligent
// optimizers. In this example, we will use the
// RegularStepGradientDescentOptimizer. Another noticeable difference is that
// pre-normalization of the images is not necessary as the metric rescales
// internally when building up the discrete density functions. Other
// differences between the two mutual information implementations are described
// in detail in Section \ref{sec:MutualInformationMetric}.
//
// First, we include the header files of the components used in this example.
//
// \index{itk::ImageRegistrationMethod!Multi-Modality}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// The following section of code implements a Command observer
// used to 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 = 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 [defaultPixelValue]" << std::endl;
std::cerr << "[checkerBoardAfter] [checkerBoardBefore]" << std::endl;
std::cerr << "[numberOfBins] [numberOfSamples]";
std::cerr << "[useExplicitPDFderivatives ] " << std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 2;
typedef unsigned short PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
MovingImageType,
double > InterpolatorType;
FixedImageType,
MovingImageType > RegistrationType;
// Software Guide : BeginLatex
//
// In this example the image types and all registration components,
// except the metric, are declared as in Section
// \ref{sec:IntroductionImageRegistration}.
// The Mattes mutual information metric type is
// instantiated using the image types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FixedImageType,
MovingImageType > MetricType;
// Software Guide : EndCodeSnippet
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetInterpolator( interpolator );
// Software Guide : BeginLatex
//
// The metric is created using the \code{New()} method and then
// connected to the registration object.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MetricType::Pointer metric = MetricType::New();
registration->SetMetric( metric );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The metric requires two parameters to be selected: the number of bins
// used to compute the entropy and the number of spatial samples used to
// compute the density estimates. In typical application 50 histogram bins
// are sufficient. Note however, that the number of bins may have dramatic
// effects on the optimizer's behavior. The number of spatial samples to be
// used depends on the content of the image. If the images are smooth and do
// not contain much detail, then using approximately $1$ percent of the
// pixels will do. On the other hand, if the images are detailed, it may be
// necessary to use a much higher proportion, such as $20$ percent.
//
// \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfHistogramBins()}
// \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfSpatialSamples()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
unsigned int numberOfBins = 24;
unsigned int numberOfSamples = 10000;
// Software Guide : EndCodeSnippet
if( argc > 7 )
{
numberOfBins = atoi( argv[7] );
}
if( argc > 8 )
{
numberOfSamples = atoi( argv[8] );
}
// Software Guide : BeginCodeSnippet
metric->SetNumberOfHistogramBins( numberOfBins );
metric->SetNumberOfSpatialSamples( numberOfSamples );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// One mechanism for bringing the Metric to its limit is to disable the
// sampling and use all the pixels present in the FixedImageRegion. This can
// be done with the \code{UseAllPixelsOn()} method. You may want to try this
// option only while you are fine tuning all other parameters of your
// registration. We don't use this method in this current example though.
//
// \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!UseAllPixelsOn()}
//
// Software Guide : EndLatex
if( argc > 9 )
{
// Define whether to calculate the metric derivative by explicitly
// computing the derivatives of the joint PDF with respect to the Transform
// parameters, or doing it by progressively accumulating contributions from
// each bin in the joint PDF.
metric->SetUseExplicitPDFDerivatives( atoi( argv[9] ) );
}
registration->SetFixedImageRegion(
typedef RegistrationType::ParametersType ParametersType;
ParametersType initialParameters( transform->GetNumberOfParameters() );
initialParameters[0] = 0.0; // Initial offset in mm along X
initialParameters[1] = 0.0; // Initial offset in mm along Y
registration->SetInitialTransformParameters( initialParameters );
// Software Guide : BeginLatex
//
// Another significant difference in the metric is that it computes the
// negative mutual information and hence we need to minimize the cost
// function in this case. In this example we will use the same optimization
// parameters as in Section \ref{sec:IntroductionImageRegistration}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->MinimizeOn();
optimizer->SetMaximumStepLength( 2.00 );
optimizer->SetMinimumStepLength( 0.001 );
optimizer->SetNumberOfIterations( 200 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Whenever the regular step gradient descent optimizer encounters that the
// direction of movement has changed in the parametric space, it reduces the
// size of the step length. The rate at which the step length is reduced is
// controlled by a relaxation factor. The default value of the factor is
// $0.5$. This value, however may prove to be inadequate for noisy metrics
// since they tend to induce very erratic movements on the optimizers and
// therefore result in many directional changes. In those
// conditions, the optimizer will rapidly shrink the step length while it is
// still too far from the location of the extrema in the cost function. In
// this example we set the relaxation factor to a number higher than the
// default in order to prevent the premature shrinkage of the step length.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetRelaxationFactor( 0.8 );
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
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;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[0];
double TranslationAlongY = finalParameters[1];
// For stability reasons it may be desirable to round up the values of translation
//
unsigned int numberOfIterations = optimizer->GetCurrentIteration();
double bestValue = optimizer->GetValue();
// Print out results
//
std::cout << std::endl;
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;
std::cout << " Stop Condition = " << optimizer->GetStopCondition() << std::endl;
// Software Guide : BeginLatex
//
// This example is executed using the same multi-modality images as the one
// in section~\ref{sec:MultiModalityRegistrationViolaWells} The registration
// converges after $59$ iterations and produces the following results:
//
// \begin{verbatim}
// Translation X = 13.0283
// Translation Y = 17.007
// \end{verbatim}
//
// These values are a very close match to the true misalignment introduced in
// the moving image.
//
// Software Guide : EndLatex
MovingImageType,
FixedImageType > ResampleFilterType;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( finalTransform );
PixelType defaultPixelValue = 100;
if( argc > 4 )
{
defaultPixelValue = atoi( argv[4] );
}
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( defaultPixelValue );
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();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration4Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration4CheckerboardBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration4CheckerboardAfter}
// \itkcaption[MattesMutualInformationImageToImageMetric output images]{The mapped
// moving image (left) and the composition of fixed and moving images before
// (center) and after (right) registration with Mattes mutual information.}
// \label{fig:ImageRegistration4Output}
// \end{figure}
//
// The result of resampling the moving image is presented on the left of
// Figure \ref{fig:ImageRegistration4Output}. The center and right parts of
// the figure present a checkerboard composite of the fixed and moving
// images before and after registration respectively.
//
// Software Guide : EndLatex
//
// Generate checkerboards before and after registration
//
typedef itk::CheckerBoardImageFilter< FixedImageType > CheckerBoardFilterType;
CheckerBoardFilterType::Pointer checker = CheckerBoardFilterType::New();
checker->SetInput1( fixedImage );
checker->SetInput2( resample->GetOutput() );
caster->SetInput( checker->GetOutput() );
writer->SetInput( caster->GetOutput() );
resample->SetDefaultPixelValue( 0 );
// Before registration
TransformType::Pointer identityTransform = TransformType::New();
identityTransform->SetIdentity();
resample->SetTransform( identityTransform );
if( argc > 5 )
{
writer->SetFileName( argv[5] );
writer->Update();
}
// After registration
resample->SetTransform( finalTransform );
if( argc > 6 )
{
writer->SetFileName( argv[6] );
writer->Update();
}
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{ImageRegistration4TraceTranslations}
// \includegraphics[width=0.44\textwidth]{ImageRegistration4TraceTranslations2}
// \includegraphics[width=0.6\textwidth]{ImageRegistration4TraceMetric}
// \itkcaption[MattesMutualInformationImageToImageMetric output plots]{Sequence
// of translations and metric values at each iteration of the optimizer.}
// \label{fig:ImageRegistration4TraceTranslations}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration4TraceTranslations} (upper-left) shows
// the sequence of translations followed by the optimizer as it searched the
// parameter space. The upper-right figure presents a closer look at the
// convergence basin for the last iterations of the optimizer. The bottom of
// the same figure shows the sequence of metric values computed as the
// optimizer searched the parameter space. Comparing these trace plots with
// Figures \ref{fig:ImageRegistration2TraceTranslations} and
// \ref{fig:ImageRegistration2TraceMetric}, we can see that the measures
// produced by MattesMutualInformationImageToImageMetric are smoother than
// those of the MutualInformationImageToImageMetric. This smoothness allows
// the use of more sophisticated optimizers such as the
// onto the optimal value.
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// You must note however that there are a number of non-trivial issues
// involved in the fine tuning of parameters for the optimization. For
// example, the number of bins used in the estimation of Mutual Information
// has a dramatic effect on the performance of the optimizer. In order to
// illustrate this effect, this same example has been executed using a range
// of different values for the number of bins, from $10$ to $30$. If you
// repeat this experiment, you will notice that depending on the number of
// bins used, the optimizer's path may get trapped early on in local minima.
// Figure \ref{fig:ImageRegistration4TraceTranslationsNumberOfBins} shows the
// multiple paths that the optimizer took in the parametric space of the
// transform as a result of different selections on the number of bins used
// by the Mattes Mutual Information metric. Note that many of the paths die
// in local minima instead of reaching the extrema value on the upper right
// corner.
//
// \begin{figure}
// \center
// \includegraphics[width=0.8\textwidth]{ImageRegistration4TraceTranslationsNumberOfBins}
// \itkcaption[MattesMutualInformationImageToImageMetric number of
// bins]{Sensitivity of the optimization path to the number of Bins used for
// estimating the value of Mutual Information with Mattes et al. approach.}
// \label{fig:ImageRegistration4TraceTranslationsNumberOfBins}
// \end{figure}
//
// Effects such as the one illustrated here highlight how useless is to
// compare different algorithms based on a non-exhaustive search of their
// parameter setting. It is quite difficult to be able to claim that a
// particular selection of parameters represent the best combination for
// running a particular algorithm. Therefore, when comparing the performance
// of two or more different algorithms, we are faced with the challenge of
// proving that none of the algorithms involved in the comparison is being
// run with a sub-optimal set of parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The plots in Figures~\ref{fig:ImageRegistration4TraceTranslations}
// and~\ref{fig:ImageRegistration4TraceTranslationsNumberOfBins} were
// generated using Gnuplot. The scripts used for this purpose are available
// in the \code{InsightDocuments} CVS module under the directory
//
// ~\code{InsightDocuments/SoftwareGuide/Art}.
//
// The use of these scripts was similar to what was described at the end of
// section~\ref{sec:MultiModalityRegistrationViolaWells}.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}