ITK  4.8.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv4/DeformableRegistration4.cxx
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*
* 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.
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// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{BSplineTransform}
// class for performing registration of two $2D$ images in an ITKv4
// registration framework. Due to the large number of parameters of
// the BSpline transform, we will use a \doxygen{LBFGSOptimizerv4}
// instead of a simple steepest descent or a conjugate gradient
// descent optimizer.
//
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSOptimizerv4}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers to this example.
//
// \index{itk::BSplineTransform!header}
// \index{itk::LBFGSOptimizerv4!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The parameter space of the \code{BSplineTransform} is composed by
// the set of all the deformations associated with the nodes of the BSpline
// grid. This large number of parameters makes it possible to represent a wide
// variety of deformations, at the cost of requiring a
// significant amount of computation time.
//
// \index{itk::BSplineTransform!header}
//
// Software Guide : EndLatex
// NOTE: the LBFGSOptimizerv4 does not invoke events
int main( int argc, char *argv[] )
{
if( argc < 4 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile outputImagefile ";
std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
std::cerr << " [deformationField] ";
return EXIT_FAILURE;
}
const unsigned int ImageDimension = 2;
typedef float PixelType;
typedef itk::Image< PixelType, ImageDimension > MovingImageType;
// Software Guide : BeginLatex
//
// We instantiate now the type of the \code{BSplineTransform} using
// as template parameters the type for coordinates representation, the
// dimension of the space, and the order of the BSpline.
//
// \index{BSplineTransform!New}
// \index{BSplineTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int SpaceDimension = ImageDimension;
const unsigned int SplineOrder = 3;
typedef double CoordinateRepType;
CoordinateRepType,
SpaceDimension,
SplineOrder > TransformType;
// Software Guide : EndCodeSnippet
typedef itk::LBFGSOptimizerv4 OptimizerType;
FixedImageType,
MovingImageType > MetricType;
FixedImageType,
MovingImageType > RegistrationType;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
movingImageReader->SetFileName( argv[2] );
fixedImageReader->Update();
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
// Software Guide : BeginLatex
//
// The transform object is constructed below.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Fixed parameters of the BSpline transform should be defined
// before the registration. These parameters define origin,
// dimension, direction and mesh size of the transform grid
// and are set based on specifications of the fixed image space
// lattice. We can use \doxygen{BSplineTransformInitializer} to
// initialize fixed parameters of a BSpline transform.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType,
FixedImageType> InitializerType;
InitializerType::Pointer transformInitializer = InitializerType::New();
unsigned int numberOfGridNodesInOneDimension = 8;
TransformType::MeshSizeType meshSize;
meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
transformInitializer->SetTransform( transform );
transformInitializer->SetImage( fixedImage );
transformInitializer->SetTransformDomainMeshSize( meshSize );
transformInitializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// After setting the fixed parameters of the transform, we set the
// initial transform to be an identity transform. It is like setting
// all the transform parameters to zero in created parameter space.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetIdentity();
// Software Guide : EndCodeSnippet
std::cout << "Initial Parameters = " << std::endl;
std::cout << transform->GetParameters() << std::endl;
// Software Guide : BeginLatex
//
// Then, the initialized transform is connected to the registration
// object and is set to be optimized directly during the registration
// process.
//
// Calling \code{InPlaceOn()} means that the current initialized transform
// will optimized directly and is grafted to the output, so it can be
// considered as the output transform object. Otherwise, the initial transform
// will be copied or ``cloned'' to the output transform object, and the copied
// object will be optimized during the registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform( transform );
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImageReader->GetOutput() );
// Software Guide : BeginLatex
//
// The \doxygen{RegistrationParameterScalesFromPhysicalShift} class
// is used to estimate the parameters scales before we set the optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ScalesEstimatorType;
ScalesEstimatorType::Pointer scalesEstimator = ScalesEstimatorType::New();
scalesEstimator->SetMetric( metric );
scalesEstimator->SetTransformForward( true );
scalesEstimator->SetSmallParameterVariation( 1.0 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the scale estimator is passed to the \doxygen{LBFGSOptimizerv4},
// and we set other parameters of the optimizer as well.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetGradientConvergenceTolerance( 5e-2 );
optimizer->SetLineSearchAccuracy( 1.2 );
optimizer->SetDefaultStepLength( 1.5 );
optimizer->TraceOn();
optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );
optimizer->SetScalesEstimator( scalesEstimator );
// Software Guide : EndCodeSnippet
// A single level registration process is run using
// the shrink factor 1 and smoothing sigma 0.
//
const 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 );
// Add time and memory probes
std::cout << std::endl << "Starting Registration" << std::endl;
try
{
memorymeter.Start( "Registration" );
chronometer.Start( "Registration" );
registration->Update();
chronometer.Stop( "Registration" );
memorymeter.Stop( "Registration" );
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;
}
// Report the time and memory taken by the registration
chronometer.Report( std::cout );
memorymeter.Report( std::cout );
// Software Guide : BeginLatex
//
// Let's execute this example using the rat lung images from the previous examples.
//
// \begin{itemize}
// \item \code{RatLungSlice1.mha}
// \item \code{RatLungSlice2.mha}
// \end{itemize}
//
// The \emph{transform} object is updated during the registration process
// and is passed to the resampler to map the moving image space onto the
// fixed image space.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerType::ParametersType finalParameters = transform->GetParameters();
// Software Guide : EndCodeSnippet
std::cout << "Last Transform Parameters" << std::endl;
std::cout << finalParameters << std::endl;
MovingImageType,
FixedImageType > ResampleFilterType;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transform );
resample->SetInput( movingImageReader->GetOutput() );
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() );
try
{
writer->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
FixedImageType,
FixedImageType,
OutputImageType > DifferenceFilterType;
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 >= 5 )
{
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( resample->GetOutput() );
writer2->SetFileName( argv[4] );
try
{
writer2->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
// Compute the difference image between the
// fixed and moving image before registration.
if( argc >= 6 )
{
writer2->SetFileName( argv[5] );
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( movingImageReader->GetOutput() );
try
{
writer2->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
// Generate the explicit deformation field resulting from
// the registration.
typedef itk::Vector< float, ImageDimension > VectorPixelType;
typedef itk::Image< VectorPixelType, ImageDimension > DisplacementFieldImageType;
DisplacementFieldImageType,
CoordinateRepType > DisplacementFieldGeneratorType;
DisplacementFieldGeneratorType::Pointer dispfieldGenerator =
DisplacementFieldGeneratorType::New();
dispfieldGenerator->UseReferenceImageOn();
dispfieldGenerator->SetReferenceImage( fixedImage );
dispfieldGenerator->SetTransform( transform );
try
{
dispfieldGenerator->Update();
}
catch ( itk::ExceptionObject & err )
{
std::cerr << "Exception detected while generating deformation field";
std::cerr << " : " << err << std::endl;
return EXIT_FAILURE;
}
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput( dispfieldGenerator->GetOutput() );
if( argc >= 7 )
{
fieldWriter->SetFileName( argv[6] );
try
{
fieldWriter->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception thrown " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
}
return EXIT_SUCCESS;
}