ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv4/ImageRegistration12.cxx
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates the use of \code{SpatialObject}s as masks for selecting the
// pixels that should contribute to the computation of Image Metrics. This
// example is almost identical to ImageRegistration6 with the exception that
// the \code{SpatialObject} masks are created and passed to the image metric.
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The most important header in this example is the one corresponding to the
// \doxygen{ImageMaskSpatialObject} class.
//
// \index{itk::ImageMaskSpatialObject!header}
//
// 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:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
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 < 5 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile fixedImageMaskFile";
std::cerr << " outputImagefile [differenceOutputfile] ";
std::cerr << " [differenceBeforeRegistration] "<< std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = float;
using FixedImageType = itk::Image< PixelType, Dimension >;
using MovingImageType = itk::Image< PixelType, Dimension >;
using TransformType = itk::Euler2DTransform< double >;
FixedImageType,
MovingImageType >;
using RegistrationType = itk::ImageRegistrationMethodv4<
FixedImageType,
MovingImageType,
TransformType >;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
TransformType::Pointer transform = TransformType::New();
registration->SetInitialTransform( transform );
registration->InPlaceOn();
using FixedImageReaderType = itk::ImageFileReader< FixedImageType >;
using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
movingImageReader->SetFileName( argv[2] );
registration->SetFixedImage( fixedImageReader->GetOutput() );
registration->SetMovingImage( movingImageReader->GetOutput() );
fixedImageReader->Update();
using TransformInitializerType = itk::CenteredTransformInitializer<
TransformType,
FixedImageType,
MovingImageType >;
TransformInitializerType::Pointer initializer = TransformInitializerType::New();
initializer->SetTransform( transform );
initializer->SetFixedImage( fixedImageReader->GetOutput() );
initializer->SetMovingImage( movingImageReader->GetOutput() );
initializer->MomentsOn();
initializer->InitializeTransform();
transform->SetAngle( 0.0 );
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = translationScale;
optimizerScales[2] = translationScale;
optimizer->SetScales( optimizerScales );
optimizer->SetLearningRate( 0.1 );
optimizer->SetMinimumStepLength( 0.001 );
optimizer->SetNumberOfIterations( 200 );
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
// Software Guide : BeginLatex
//
// Here we instantiate the type of the \doxygen{ImageMaskSpatialObject}
// using the same dimension of the images to be registered.
//
// \index{itk::ImageMaskSpatialObject!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then we use the type for creating the spatial object mask that will
// restrict the registration to a reduced region of the image.
//
// \index{itk::ImageMaskSpatialObject!New}
// \index{itk::ImageMaskSpatialObject!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MaskType::Pointer spatialObjectMask = MaskType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The mask in this case is read from a binary file using the
// \code{ImageFileReader} instantiated for an \code{unsigned char} pixel
// type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MaskReaderType = itk::ImageFileReader< ImageMaskType >;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The reader is constructed and a filename is passed to it.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MaskReaderType::Pointer maskReader = MaskReaderType::New();
maskReader->SetFileName( argv[3] );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As usual, the reader is triggered by invoking its \code{Update()} method.
// Since this may eventually throw an exception, the call must be placed in
// a \code{try/catch} block. Note that a full fledged application will place
// this \code{try/catch} block at a much higher level, probably under the
// control of the GUI.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
maskReader->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of the mask reader is connected as input to the
// \code{ImageMaskSpatialObject}.
//
// \index{itk::ImageMaskSpatialObject!SetImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
spatialObjectMask->SetImage( maskReader->GetOutput() );
spatialObjectMask->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the spatial object mask is passed to the image metric.
//
// \index{itk::ImageToImageMetricv4!SetFixedImageMask()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->SetFixedImageMask( spatialObjectMask );
// Software Guide : EndCodeSnippet
// One level registration process without shrinking and smoothing.
//
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 );
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;
}
OptimizerType::ParametersType finalParameters =
transform->GetParameters();
const double finalAngle = finalParameters[0];
const double finalTranslationX = finalParameters[1];
const double finalTranslationY = finalParameters[2];
const double rotationCenterX = registration->GetOutput()->Get()->GetFixedParameters()[0];
const double rotationCenterY = registration->GetOutput()->Get()->GetFixedParameters()[1];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Print out results
//
const double finalAngleInDegrees = finalAngle * 45.0 / std::atan(1.0);
std::cout << "Result = " << std::endl;
std::cout << " Angle (radians) " << finalAngle << std::endl;
std::cout << " Angle (degrees) " << finalAngleInDegrees << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Fixed Center X = " << rotationCenterX << std::endl;
std::cout << " Fixed Center Y = " << rotationCenterY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images provided in
// \code{Examples/Data}, for example:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceR10X13Y17.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first
// image by $10$ degrees and shifting it $13mm$ in $X$ and $17mm$ in
// $Y$. Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5}.
//
// The registration converges after $20$ iterations and produces the
// following results:
//
// \begin{verbatim}
//
// Angle (radians) 0.174712
// Angle (degrees) 10.0103
// Translation X = 12.4521
// Translation Y = 16.0765
//
// \end{verbatim}
//
// These values are a very close match to the true misalignments
// introduced in the moving image.
//
// Now we resample the moving image using the transform resulting from the
// registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::MatrixType matrix = transform->GetMatrix();
TransformType::OffsetType offset = transform->GetOffset();
std::cout << "Matrix = " << std::endl << matrix << std::endl;
std::cout << "Offset = " << std::endl << offset << std::endl;
// Software Guide : EndCodeSnippet
using ResampleFilterType = itk::ResampleImageFilter<
MovingImageType,
FixedImageType >;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( finalTransform );
resample->SetInput( movingImageReader->GetOutput() );
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( 100 );
using OutputPixelType = unsigned char;
using CastFilterType = itk::CastImageFilter<
FixedImageType,
OutputImageType >;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[4] );
caster->SetInput( resample->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
using DifferenceFilterType = itk::SquaredDifferenceImageFilter<
FixedImageType,
FixedImageType,
OutputImageType >;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput( difference->GetOutput() );
// Compute the difference image between the
// fixed and resampled moving image.
if( argc >= 6 )
{
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( resample->GetOutput() );
writer2->SetFileName( argv[5] );
writer2->Update();
}
// Compute the difference image between the
// fixed and moving image before registration.
if( argc >= 7 )
{
writer2->SetFileName( argv[6] );
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( movingImageReader->GetOutput() );
writer2->Update();
}
return EXIT_SUCCESS;
}