ITK/Examples/Registration/ImageRegistrationMethod: Difference between revisions

From KitwarePublic
< ITK‎ | Examples
Jump to navigationJump to search
No edit summary
(Deprecated content that is moved to sphinx)
 
(One intermediate revision by one other user not shown)
Line 1: Line 1:
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.
{{warning|1=The media wiki content on this page is no longer maintained.  The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases.  In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.}}
 
==ImageRegistrationMethod.cxx==
<source lang="cpp">
#include "itkCastImageFilter.h"
#include "itkEllipseSpatialObject.h"
#include "itkImage.h"
#include "itkImageRegistrationMethod.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkResampleImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkSpatialObjectToImageFilter.h"
#include "itkTranslationTransform.h"
 
const    unsigned int    Dimension = 2;
typedef  unsigned char          PixelType;
 
typedef itk::Image< PixelType, Dimension >  ImageType;
 
static void CreateEllipseImage(ImageType::Pointer image);
static 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>
 
{{ITKCMakeLists|ImageRegistrationMethod}}

Latest revision as of 14:43, 7 June 2019

Warning: The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.