ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/ImageRegistration2.cxx
/*=========================================================================
*
*
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1SliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceShifted13x17y.png}
// OUTPUTS: {ImageRegistration2Output.png}
// OUTPUTS: {ImageRegistration2CheckerboardBefore.png}
// OUTPUTS: {ImageRegistration2CheckerboardAfter.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The following simple example illustrates how multiple imaging modalities can
// be registered using the ITK registration framework. The first difference
// between this and previous examples is the use of the
// \doxygen{MutualInformationImageToImageMetric} as the cost-function to be
// optimized. The second difference is the use of the
// \doxygen{GradientDescentOptimizer}. Due to the stochastic nature of the
// metric computation, the values are too noisy to work successfully with the
// \doxygen{RegularStepGradientDescentOptimizer}. Therefore, we will use the
// simpler GradientDescentOptimizer with a user defined learning rate. The
// following headers declare the basic components of this registration method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// One way to simplify the computation of the mutual information is
// to normalize the statistical distribution of the two input images. The
// \doxygen{NormalizeImageFilter} is the perfect tool for this task.
// It rescales the intensities of the input images in order to produce an
// output image with zero mean and unit variance. This filter has been
// discussed in Section \ref{sec:CastingImageFilters}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Additionally, low-pass filtering of the images to be registered will also
// increase robustness against noise. In this example, we will use the
// \doxygen{DiscreteGaussianImageFilter} for that purpose. The
// characteristics of this filter have been discussed in Section
// \ref{sec:BlurringFilters}.
//
// 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 = 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 ";
std::cerr << "[checkerBoardBefore] [checkerBoardAfter]" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// The moving and fixed images types should be instantiated first.
//
// Software Guide : EndLatex
//
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
typedef unsigned short PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// It is convenient to work with an internal image type because mutual
// information will perform better on images with a normalized statistical
// distribution. The fixed and moving images will be normalized and
// converted to this internal type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef float InternalPixelType;
typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The rest of the image registration components are instantiated as
// illustrated in Section \ref{sec:IntroductionImageRegistration} with
// the use of the \code{InternalImageType}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InternalImageType,
double > InterpolatorType;
InternalImageType,
InternalImageType > RegistrationType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The mutual information metric type is instantiated using the image
// types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InternalImageType,
InternalImageType > 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 a number of parameters to be selected, including
// the standard deviation of the Gaussian kernel for the fixed image
// density estimate, the standard deviation of the kernel for the moving
// image density and the number of samples use to compute the densities
// and entropy values. Details on the concepts behind the computation of
// the metric can be found in Section
// \ref{sec:MutualInformationMetric}. Experience has
// shown that a kernel standard deviation of $0.4$ works well for images
// which have been normalized to a mean of zero and unit variance. We
// will follow this empirical rule in this example.
//
// \index{itk::Mutual\-Information\-Image\-To\-Image\-Metric!SetFixedImageStandardDeviation()}
// \index{itk::Mutual\-Information\-Image\-To\-Image\-Metric!SetMovingImageStandardDeviation()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->SetFixedImageStandardDeviation( 0.4 );
metric->SetMovingImageStandardDeviation( 0.4 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The normalization filters are instantiated using the fixed and moving
// image types as input and the internal image type as output.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FixedImageType,
InternalImageType
> FixedNormalizeFilterType;
MovingImageType,
InternalImageType
> MovingNormalizeFilterType;
FixedNormalizeFilterType::Pointer fixedNormalizer =
FixedNormalizeFilterType::New();
MovingNormalizeFilterType::Pointer movingNormalizer =
MovingNormalizeFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The blurring filters are declared using the internal image type as both
// the input and output types. In this example, we will set the variance
// for both blurring filters to $2.0$.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InternalImageType,
InternalImageType
> GaussianFilterType;
GaussianFilterType::Pointer fixedSmoother = GaussianFilterType::New();
GaussianFilterType::Pointer movingSmoother = GaussianFilterType::New();
fixedSmoother->SetVariance( 2.0 );
movingSmoother->SetVariance( 2.0 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of the readers becomes the input to the normalization
// filters. The output of the normalization filters is connected as
// input to the blurring filters. The input to the registration method
// is taken from the blurring filters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
fixedSmoother->SetInput( fixedNormalizer->GetOutput() );
movingSmoother->SetInput( movingNormalizer->GetOutput() );
registration->SetFixedImage( fixedSmoother->GetOutput() );
registration->SetMovingImage( movingSmoother->GetOutput() );
// Software Guide : EndCodeSnippet
fixedNormalizer->Update();
FixedImageType::RegionType fixedImageRegion =
fixedNormalizer->GetOutput()->GetBufferedRegion();
registration->SetFixedImageRegion( fixedImageRegion );
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
//
// We should now define the number of spatial samples to be considered in
// the metric computation. Note that we were forced to postpone this setting
// until we had done the preprocessing of the images because the number of
// samples is usually defined as a fraction of the total number of pixels in
// the fixed image.
//
// The number of spatial samples can usually be as low as $1\%$ of the total
// number of pixels in the fixed image. Increasing the number of samples
// improves the smoothness of the metric from one iteration to another and
// therefore helps when this metric is used in conjunction with optimizers
// that rely of the continuity of the metric values. The trade-off, of
// course, is that a larger number of samples result in longer computation
// times per every evaluation of the metric.
//
// It has been demonstrated empirically that the number of samples is not a
// critical parameter for the registration process. When you start fine
// tuning your own registration process, you should start using high values
// of number of samples, for example in the range of $20\%$ to $50\%$ of the
// number of pixels in the fixed image. Once you have succeeded to register
// your images you can then reduce the number of samples progressively until
// you find a good compromise on the time it takes to compute one evaluation
// of the Metric. Note that it is not useful to have very fast evaluations
// of the Metric if the noise in their values results in more iterations
// being required by the optimizer to converge. You must then study the
// behavior of the metric values as the iterations progress, just as
// illustrated in section~\ref{sec:MonitoringImageRegistration}.
//
// \index{itk::Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfSpatialSamples()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int numberOfPixels = fixedImageRegion.GetNumberOfPixels();
const unsigned int numberOfSamples =
static_cast< unsigned int >( numberOfPixels * 0.01 );
metric->SetNumberOfSpatialSamples( numberOfSamples );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Since larger values of mutual information indicate better matches than
// smaller values, we need to maximize the cost function in this example.
// By default the GradientDescentOptimizer class is set to minimize the
// value of the cost-function. It is therefore necessary to modify its
// default behavior by invoking the \code{MaximizeOn()} method.
// Additionally, we need to define the optimizer's step size using the
// \code{SetLearningRate()} method.
//
// \index{itk::Image\-Registration\-Method!Maximize vs Minimize}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetLearningRate( 15.0 );
optimizer->SetNumberOfIterations( 200 );
optimizer->MaximizeOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that large values of the learning rate will make the optimizer
// unstable. Small values, on the other hand, may result in the optimizer
// needing too many iterations in order to walk to the extrema of the cost
// function. The easy way of fine tuning this parameter is to start with
// small values, probably in the range of $\{5.0,10.0\}$. Once the other
// registration parameters have been tuned for producing convergence, you
// may want to revisit the learning rate and start increasing its value until
// you observe that the optimization becomes unstable. The ideal value for
// this parameter is the one that results in a minimum number of iterations
// while still keeping a stable path on the parametric space of the
// optimization. Keep in mind that this parameter is a multiplicative factor
// applied on the gradient of the Metric. Therefore, its effect on the
// optimizer step length is proportional to the Metric values themselves.
// Metrics with large values will require you to use smaller values for the
// learning rate in order to maintain a similar optimizer behavior.
//
// Software Guide : EndLatex
// 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::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[0];
double TranslationAlongY = finalParameters[1];
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 << " Numb. Samples = " << numberOfSamples << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over two of the images provided in
// \code{Examples/Data}:
//
// \begin{itemize}
// \item \code{BrainT1SliceBorder20.png}
// \item \code{BrainProtonDensitySliceShifted13x17y.png}
// \end{itemize}
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainT1SliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceShifted13x17y}
// \itkcaption[Multi-Modality Registration Inputs]{A T1 MRI (fixed image) and a proton
// density MRI (moving image) are provided as input to the registration method.}
// \label{fig:FixedMovingImageRegistration2}
// \end{figure}
//
// The second image is the result of intentionally translating the image
// \code{Brain\-Proton\-Density\-Slice\-Border20.png} by $(13,17)$
// millimeters. Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration2}. The registration is stopped at
// 200 iterations and produces as result the parameters:
//
// \begin{verbatim}
// Translation X = 12.9147
// Translation Y = 17.0871
// \end{verbatim}
// These values are approximately within one tenth of a pixel from 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 );
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();
// 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() );
// Before registration
TransformType::Pointer identityTransform = TransformType::New();
identityTransform->SetIdentity();
resample->SetTransform( identityTransform );
if( argc > 4 )
{
writer->SetFileName( argv[4] );
writer->Update();
}
// After registration
resample->SetTransform( finalTransform );
if( argc > 5 )
{
writer->SetFileName( argv[5] );
writer->Update();
}
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration2Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration2CheckerboardBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration2CheckerboardAfter}
// \itkcaption[Multi-Modality Registration outputs]{Mapped moving image (left)
// and composition of fixed and moving images before (center) and after
// (right) registration.}
// \label{fig:ImageRegistration2Output}
// \end{figure}
//
// The moving image after resampling is presented on the left
// side of Figure \ref{fig:ImageRegistration2Output}. The center and right
// figures present a checkerboard composite of the fixed and
// moving images before and after registration.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{ImageRegistration2TraceTranslations}
// \includegraphics[width=0.44\textwidth]{ImageRegistration2TraceTranslations2}
// \itkcaption[Multi-Modality Registration plot of translations]{Sequence of
// translations during the registration process. On the left are iterations 0 to
// 200. On the right are iterations 150 to 200.}
// \label{fig:ImageRegistration2TraceTranslations}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration2TraceTranslations} shows the sequence
// of translations followed by the optimizer as it searched the parameter
// space. The left plot shows iterations $0$ to $200$ while the right
// figure zooms into iterations $150$ to $200$. The area covered by the
// right figure has been highlighted by a rectangle in the left image. It
// can be seen that after a certain number of iterations the optimizer
// oscillates within one or two pixels of the true solution. At this
// point it is clear that more iterations will not help. Instead it is
// time to modify some of the parameters of the registration process, for
// example, reducing the learning rate of the optimizer and continuing the
// registration so that smaller steps are taken.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{ImageRegistration2TraceMetric}
// \includegraphics[width=0.44\textwidth]{ImageRegistration2TraceMetric2}
// \itkcaption[Multi-Modality Registration plot of metrics]{The sequence of metric
// values produced during the registration process. On the left are
// iterations 0 to 200. On the right are iterations 150 to 200.}
// \label{fig:ImageRegistration2TraceMetric}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration2TraceMetric} shows the sequence of
// metric values computed as the optimizer searched the parameter space.
// The left plot shows values when iterations are extended from $0$ to
// $200$ while the right figure zooms into iterations $150$ to $200$. The
// fluctuations in the metric value are due to the stochastic nature in
// which the measure is computed. At each call of \code{GetValue()}, two
// new sets of intensity samples are randomly taken from the image to
// compute the density and entropy estimates. Even with the fluctuations,
// the measure initially increases overall with the number of iterations.
// After about 150 iterations, the metric value merely oscillates without further
// noticeable convergence. The trace plots in Figure
// \ref{fig:ImageRegistration2TraceMetric} highlight one of the
// difficulties associated with this particular metric: the stochastic
// oscillations make it difficult to determine convergence and limit the
// use of more sophisticated optimization methods. As explained above,
// the reduction of the learning rate as the registration progresses is
// very important in order to get precise results.
//
// This example shows the importance of tracking the evolution of the
// registration method in order to obtain insight into the characteristics
// of the particular problem at hand and the components being used. The
// behavior revealed by these plots usually helps to identify possible
// improvements in the setup of the registration parameters.
//
// The plots in Figures~\ref{fig:ImageRegistration2TraceTranslations}
// and~\ref{fig:ImageRegistration2TraceMetric} were generated using
// Gnuplot\footnote{\url{http://www.gnuplot.info/}}. The scripts used for
// this purpose are available in the \code{InsightDocuments} CVS module
// under the directory
//
// ~\code{InsightDocuments/SoftwareGuide/Art}.
//
// Data for the plots was taken directly from the output that the
// Command/Observer in this example prints out to the console. The output
// was processed with the UNIX editor
// \code{sed}\footnote{\url{http://www.gnu.org/software/sed/sed.html}} in
// order to remove commas and brackets that were confusing for Gnuplot's
// parser. Both the shell script for running \code{sed} and for running
// {Gnuplot} are available in the directory indicated above. You may find
// useful to run them in order to verify the results presented here, and to
// eventually modify them for profiling your own registrations.
//
// \index{Open Science}
//
// Open Science is not just an abstract concept. Open Science is something
// to be practiced every day with the simple gesture of sharing information
// with your peers, and by providing all the tools that they need for
// replicating the results that you are reporting. In Open Science, the only
// bad results are those that can not be
// replicated\footnote{\url{http://science.creativecommons.org/}}. Science
// is dead when people blindly trust authorities~\footnote{For example:
// Reviewers of Scientific Journals.} instead of verifying their statements
// by performing their own experiments ~\cite{Popper1971,Popper2002}.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}