ITK/Examples/Registration/ImageRegistrationMethod

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This example registers two synthetic images. A white circle is created in the center of the fixed image (with a black background). A white ellipse is created as the moving image and offset from the center of the image. A rigid translation-only transform is then optimized to bring the ellipse to the circle.

ImageRegistrationMethod.cxx

<source lang="cpp">

  1. include "itkCastImageFilter.h"
  2. include "itkEllipseSpatialObject.h"
  3. include "itkImage.h"
  4. include "itkImageRegistrationMethod.h"
  5. include "itkLinearInterpolateImageFunction.h"
  6. include "itkImageFileReader.h"
  7. include "itkImageFileWriter.h"
  8. include "itkMeanSquaresImageToImageMetric.h"
  9. include "itkRegularStepGradientDescentOptimizer.h"
  10. include "itkResampleImageFilter.h"
  11. include "itkRescaleIntensityImageFilter.h"
  12. include "itkSpatialObjectToImageFilter.h"
  13. include "itkTranslationTransform.h"

const unsigned int Dimension = 2; typedef unsigned char PixelType;

typedef itk::Image< PixelType, Dimension > ImageType;

void CreateEllipseImage(ImageType::Pointer image); void CreateSphereImage(ImageType::Pointer image);

int main(int, char *[] ) { 

 //  The transform that will map the fixed image into the moving image.
 typedef itk::TranslationTransform< double, Dimension > TransformType;
 
 //  An optimizer is required to explore the parameter space of the transform
 //  in search of optimal values of the metric.
 typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
 
 //  The metric will compare how well the two images match each other. Metric
 //  types are usually parameterized by the image types as it can be seen in
 //  the following type declaration.
 typedef itk::MeanSquaresImageToImageMetric< 
     ImageType,
     ImageType >    MetricType;
 
 //  Finally, the type of the interpolator is declared. The interpolator will
 //  evaluate the intensities of the moving image at non-grid positions.
 typedef itk:: LinearInterpolateImageFunction<
     ImageType,
     double          >    InterpolatorType;
 
 //  The registration method type is instantiated using the types of the
 //  fixed and moving images. This class is responsible for interconnecting
 //  all the components that we have described so far.
 typedef itk::ImageRegistrationMethod<
     ImageType,
     ImageType >    RegistrationType;

 // Create components
 MetricType::Pointer         metric        = MetricType::New();
 TransformType::Pointer      transform     = TransformType::New();
 OptimizerType::Pointer      optimizer     = OptimizerType::New();
 InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
 RegistrationType::Pointer   registration  = RegistrationType::New();

 // Each component is now connected to the instance of the registration method.
 registration->SetMetric(        metric        );
 registration->SetOptimizer(     optimizer     );
 registration->SetTransform(     transform     );
 registration->SetInterpolator(  interpolator  );

 // Get the two images
 ImageType::Pointer  fixedImage  = ImageType::New();
 ImageType::Pointer movingImage = ImageType::New();

 CreateSphereImage(fixedImage);
 CreateEllipseImage(movingImage);

 // Write the two synthetic inputs
 typedef itk::ImageFileWriter< ImageType >  WriterType;
 
 WriterType::Pointer      fixedWriter =  WriterType::New();
 fixedWriter->SetFileName("fixed.png");
 fixedWriter->SetInput( fixedImage);
 fixedWriter->Update();

 WriterType::Pointer      movingWriter =  WriterType::New();
 movingWriter->SetFileName("moving.png");
 movingWriter->SetInput( movingImage);
 movingWriter->Update();
 
 // Set the registration inputs
 registration->SetFixedImage(fixedImage);
 registration->SetMovingImage(movingImage);
 
 registration->SetFixedImageRegion(
   fixedImage->GetLargestPossibleRegion() );

 //  Initialize the transform
 typedef RegistrationType::ParametersType ParametersType;
 ParametersType initialParameters( transform->GetNumberOfParameters() );

 initialParameters[0] = 0.0;  // Initial offset along X
 initialParameters[1] = 0.0;  // Initial offset along Y

 registration->SetInitialTransformParameters( initialParameters );

 optimizer->SetMaximumStepLength( 4.00 );
 optimizer->SetMinimumStepLength( 0.01 );

 // Set a stopping criterion
 optimizer->SetNumberOfIterations( 200 );

 // Connect an observer
 //CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
 //optimizer->AddObserver( itk::IterationEvent(), observer );

 try
 {
   registration->Update();
 }
 catch( itk::ExceptionObject & err )
 {
   std::cerr << "ExceptionObject caught !" << std::endl;
   std::cerr << err << std::endl;
   return EXIT_FAILURE;
 }

 //  The result of the registration process is an array of parameters that
 //  defines the spatial transformation in an unique way. This final result is
 //  obtained using the \code{GetLastTransformParameters()} method.
             
 ParametersType finalParameters = registration->GetLastTransformParameters();

 //  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.
             
 const double TranslationAlongX = finalParameters[0];
 const double TranslationAlongY = finalParameters[1];

 //  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 maximum step length has been set too small, which
 //  is undesirable since it results in long computational times.
             
 const unsigned int numberOfIterations = optimizer->GetCurrentIteration();

 //  The value of the image metric corresponding to the last set of parameters
 //  can be obtained with the \code{GetValue()} method of the optimizer.
             
 const double bestValue = optimizer->GetValue();

 // 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;

 //  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.
 //  This is easily done with the \doxygen{ResampleImageFilter}. Please
 //  refer to 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.
             
 typedef itk::ResampleImageFilter<
     ImageType,
     ImageType >    ResampleFilterType;

 //  A resampling filter is created and the moving image is connected as  its input.

 ResampleFilterType::Pointer resampler = ResampleFilterType::New();
 resampler->SetInput( movingImage);
 
 //  The Transform that is produced as output of the Registration method is
 //  also passed as input to the resampling filter. Note the use of the
 //  methods \code{GetOutput()} and \code{Get()}. This combination is needed
 //  here because the registration method acts as a filter whose output is a
 //  transform decorated in the form of a \doxygen{DataObject}. For details in
 //  this construction you may want to read the documentation of the
 //  \doxygen{DataObjectDecorator}.

 resampler->SetTransform( registration->GetOutput()->Get() );

 //  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.  

 resampler->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
 resampler->SetOutputOrigin(  fixedImage->GetOrigin() );
 resampler->SetOutputSpacing( fixedImage->GetSpacing() );
 resampler->SetOutputDirection( fixedImage->GetDirection() );
 resampler->SetDefaultPixelValue( 100 );
 
 //  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.
                 
 typedef unsigned char OutputPixelType;
 typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
 typedef itk::CastImageFilter<
     ImageType,
     ImageType > CastFilterType;
 
 WriterType::Pointer      writer =  WriterType::New();
 CastFilterType::Pointer  caster =  CastFilterType::New();
 writer->SetFileName("output.png");

 caster->SetInput( resampler->GetOutput() );
 writer->SetInput( caster->GetOutput()   );
 writer->Update();

 /*
 //  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.
                     
                     
 typedef itk::SubtractImageFilter<
     FixedImageType,
     FixedImageType,
     FixedImageType > DifferenceFilterType;

 DifferenceFilterType::Pointer difference = DifferenceFilterType::New();

 difference->SetInput1( fixedImageReader->GetOutput() );
 difference->SetInput2( resampler->GetOutput() );
 */
 

 return EXIT_SUCCESS;

}

void CreateEllipseImage(ImageType::Pointer image) { 

 typedef itk::EllipseSpatialObject< Dimension >   EllipseType;

 typedef itk::SpatialObjectToImageFilter<
   EllipseType, ImageType >   SpatialObjectToImageFilterType;

 SpatialObjectToImageFilterType::Pointer imageFilter =
   SpatialObjectToImageFilterType::New();

 ImageType::SizeType size;
 size[ 0 ] =  100;
 size[ 1 ] =  100;

 imageFilter->SetSize( size );

 ImageType::SpacingType spacing;
 spacing.Fill(1);
 imageFilter->SetSpacing(spacing);

 EllipseType::Pointer ellipse    = EllipseType::New();
 EllipseType::ArrayType radiusArray;
 radiusArray[0] = 10;
 radiusArray[1] = 20;
 ellipse->SetRadius(radiusArray);

 typedef EllipseType::TransformType                 TransformType;
 TransformType::Pointer transform = TransformType::New();
 transform->SetIdentity();

 TransformType::OutputVectorType  translation;
 TransformType::CenterType        center;

 translation[ 0 ] =  65;
 translation[ 1 ] =  45;
 transform->Translate( translation, false );

 ellipse->SetObjectToParentTransform( transform );

 imageFilter->SetInput(ellipse);

 ellipse->SetDefaultInsideValue(255);
 ellipse->SetDefaultOutsideValue(0);
 imageFilter->SetUseObjectValue( true );
 imageFilter->SetOutsideValue( 0 );

 imageFilter->Update();

 image->Graft(imageFilter->GetOutput());

}

void CreateSphereImage(ImageType::Pointer image) { 

typedef itk::EllipseSpatialObject< Dimension >   EllipseType;

 typedef itk::SpatialObjectToImageFilter<
   EllipseType, ImageType >   SpatialObjectToImageFilterType;

 SpatialObjectToImageFilterType::Pointer imageFilter =
   SpatialObjectToImageFilterType::New();

 ImageType::SizeType size;
 size[ 0 ] =  100;
 size[ 1 ] =  100;

 imageFilter->SetSize( size );

 ImageType::SpacingType spacing;
 spacing.Fill(1);
 imageFilter->SetSpacing(spacing);

 EllipseType::Pointer ellipse    = EllipseType::New();
 EllipseType::ArrayType radiusArray;
 radiusArray[0] = 10;
 radiusArray[1] = 10;
 ellipse->SetRadius(radiusArray);

 typedef EllipseType::TransformType                 TransformType;
 TransformType::Pointer transform = TransformType::New();
 transform->SetIdentity();

 TransformType::OutputVectorType  translation;
 TransformType::CenterType        center;

 translation[ 0 ] =  50;
 translation[ 1 ] =  50;
 transform->Translate( translation, false );

 ellipse->SetObjectToParentTransform( transform );

 imageFilter->SetInput(ellipse);

 ellipse->SetDefaultInsideValue(255);
 ellipse->SetDefaultOutsideValue(0);
 imageFilter->SetUseObjectValue( true );
 imageFilter->SetOutsideValue( 0 );

 imageFilter->Update();

 image->Graft(imageFilter->GetOutput());

}

</source>

CMakeLists.txt

<source lang="cmake"> cmake_minimum_required(VERSION 2.6)

PROJECT(Registration)

FIND_PACKAGE(ITK REQUIRED) INCLUDE(${ITK_USE_FILE})

ADD_EXECUTABLE(Registration Registration.cpp) TARGET_LINK_LIBRARIES(Registration ITKIO ITKNumerics)


</source>

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