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Insight Segmentation and Registration Toolkit
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RegistrationITKv4/DeformableRegistration12.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 the
// \doxygen{BSplineTransform} class for performing
// registration of two $2D$ images. The example code is for the most
// part identical to the code presented in
// Section~\ref{sec:DeformableRegistration8}. The major difference is
// that this example we set the image dimension to 2.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSBOptimizerv4}
//
//
// Software Guide : EndLatex
#include "
itkImageRegistrationMethodv4.h
"
#include "
itkMattesMutualInformationImageToImageMetricv4.h
"
#include "
itkTimeProbesCollectorBase.h
"
#include "
itkMemoryProbesCollectorBase.h
"
// Software Guide : BeginLatex
//
// The following are the most relevant headers to this example.
//
// \index{itk::BSplineTransform!header}
// \index{itk::LBFGSBOptimizerv4!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "
itkBSplineTransform.h
"
#include "
itkLBFGSBOptimizerv4.h
"
// 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 possible to represent a wide
// variety of deformations, but it also has the price of requiring a
// significant amount of computation time.
//
// \index{itk::BSplineTransform!header}
//
// Software Guide : EndLatex
#include "
itkImageFileReader.h
"
#include "
itkImageFileWriter.h
"
#include "
itkResampleImageFilter.h
"
#include "
itkCastImageFilter.h
"
#include "
itkSquaredDifferenceImageFilter.h
"
// The following section of code implements a Command observer
// used to monitor the evolution of the registration process.
//
#include "
itkCommand.h
"
class
CommandIterationUpdate :
public
itk::Command
{
public
:
typedef
CommandIterationUpdate
Self
;
typedef
itk::Command
Superclass
;
typedef
itk::SmartPointer<Self>
Pointer
;
itkNewMacro( Self );
protected
:
CommandIterationUpdate() {};
public
:
typedef
itk::LBFGSBOptimizerv4
OptimizerType;
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 =
dynamic_cast<
OptimizerPointer
>
( object );
if
( !(
itk::IterationEvent
().CheckEvent( &event )) )
{
return
;
}
std::cout << optimizer->GetCurrentIteration() <<
" "
;
std::cout << optimizer->GetCurrentMetricValue() <<
" "
;
std::cout << optimizer->GetInfinityNormOfProjectedGradient() << 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 outputImagefile "
;
std::cerr <<
" [differenceOutputfile] [differenceBeforeRegistration] "
;
std::cerr <<
" [deformationField] "
;
std::cerr <<
" [filenameForFinalTransformParameters] "
;
std::cerr <<
" [numberOfGridNodesInOneDimension] "
;
std::cerr << std::endl;
return
EXIT_FAILURE;
}
const
unsigned
int
ImageDimension = 2;
typedef
float
PixelType;
typedef
itk::Image< PixelType, ImageDimension >
FixedImageType;
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
typedef
itk::ImageFileReader< FixedImageType >
FixedImageReaderType;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
fixedImageReader->Update();
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
FixedImageType::RegionType fixedRegion = fixedImage->GetBufferedRegion();
typedef
itk::ImageFileReader< MovingImageType >
MovingImageReaderType;
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
movingImageReader->SetFileName( argv[2] );
movingImageReader->Update();
MovingImageType::ConstPointer movingImage = movingImageReader->GetOutput();
// Software Guide : BeginCodeSnippet
const
unsigned
int
SpaceDimension = ImageDimension;
const
unsigned
int
SplineOrder = 3;
typedef
double
CoordinateRepType;
typedef
itk::BSplineTransform
<
CoordinateRepType,
SpaceDimension,
SplineOrder > TransformType;
// Software Guide : EndCodeSnippet
typedef
itk::ImageRegistrationMethodv4
<
FixedImageType,
MovingImageType > RegistrationType;
RegistrationType::Pointer registration = RegistrationType::New();
// Software Guide : BeginLatex
//
// Final BSpline transform will be the output of the registration method.
//
// Software Guide : EndLatex
//
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
// Software Guide : EndCodeSnippet
unsigned
int
numberOfGridNodesInOneDimension = 7;
if
( argc > 8 )
{
numberOfGridNodesInOneDimension = atoi( argv[8] );
}
// Software Guide : BeginCodeSnippet
TransformType::PhysicalDimensionsType fixedPhysicalDimensions;
TransformType::MeshSizeType meshSize;
TransformType::OriginType fixedOrigin;
for
(
unsigned
int
i=0; i< SpaceDimension; i++ )
{
fixedOrigin[i] = fixedImage->GetOrigin()[i];
fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
static_cast<
double
>
( fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
}
meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
transform->SetTransformDomainOrigin( fixedOrigin );
transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
transform->SetTransformDomainMeshSize( meshSize );
transform->SetTransformDomainDirection( fixedImage->GetDirection() );
registration->SetInitialTransform( transform );
registration->InPlaceOn();
typedef
TransformType::ParametersType ParametersType;
const
unsigned
int
numberOfParameters = transform->GetNumberOfParameters();
ParametersType parameters( numberOfParameters );
parameters.Fill( 0.0 );
transform->SetParameters( parameters );
// Software Guide : EndCodeSnippet
typedef
itk::MattesMutualInformationImageToImageMetricv4
<
FixedImageType,
MovingImageType > MetricType;
MetricType::Pointer metric = MetricType::New();
metric->SetNumberOfHistogramBins( 32 );
metric->SetUseMovingImageGradientFilter(
false
);
metric->SetUseFixedImageGradientFilter(
false
);
metric->SetUseFixedSampledPointSet(
false
);
typedef
itk::LBFGSBOptimizerv4
OptimizerType;
OptimizerType::Pointer optimizer = OptimizerType::New();
// Software Guide : BeginCodeSnippet
const
unsigned
int
numParameters = transform->GetNumberOfParameters();
OptimizerType::BoundSelectionType boundSelect( numParameters );
OptimizerType::BoundValueType upperBound( numParameters );
OptimizerType::BoundValueType lowerBound( numParameters );
boundSelect.Fill( 0 );
upperBound.Fill( 0.0 );
lowerBound.Fill( 0.0 );
optimizer->SetBoundSelection( boundSelect );
optimizer->SetUpperBound( upperBound );
optimizer->SetLowerBound( lowerBound );
optimizer->SetCostFunctionConvergenceFactor( 1.e7 );
optimizer->SetGradientConvergenceTolerance( 1
e
-35 );
optimizer->SetNumberOfIterations( 200 );
optimizer->SetMaximumNumberOfFunctionEvaluations( 200 );
optimizer->SetMaximumNumberOfCorrections( 7 );
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(
itk::IterationEvent
(), observer );
// One level registration is performed using the shrink factor 1 and smoothing sigma 1
//
const
unsigned
int
numberOfLevels = 1;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize( 1 );
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize( 1 );
smoothingSigmasPerLevel[0] = 0;
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImage );
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetNumberOfLevels( numberOfLevels );
registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );
registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );
// Add time and memory probes
itk::TimeProbesCollectorBase
chronometer;
itk::MemoryProbesCollectorBase
memorymeter;
std::cout << std::endl <<
"Starting Registration"
<< std::endl;
try
{
memorymeter.
Start
(
"Registration"
);
chronometer.
Start
(
"Registration"
);
registration->Update();
chronometer.
Stop
(
"Registration"
);
memorymeter.
Stop
(
"Registration"
);
const
OptimizerType::ConstPointer outputOptimizer =
dynamic_cast<
const
OptimizerType *
>
( registration->GetOptimizer() );
if
( outputOptimizer.IsNotNull() )
{
std::cout <<
"Optimizer stop condition = "
<< outputOptimizer->GetStopConditionDescription()
<< std::endl;
}
else
{
std::cerr <<
"Output optimizer is null."
<< std::endl;
return
EXIT_FAILURE;
}
}
catch
(
itk::ExceptionObject
& err )
{
std::cerr <<
"ExceptionObject caught !"
<< std::endl;
std::cerr << err << std::endl;
return
EXIT_FAILURE;
}
// While the registration filter is run, it updates the output transform parameters with the final registration parameters
OptimizerType::ParametersType finalParameters = transform->GetParameters();
// Report the time and memory taken by the registration
chronometer.
Report
( std::cout );
memorymeter.
Report
( std::cout );
typedef
itk::ResampleImageFilter
<
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() );
// This value is set to zero in order to make easier to perform
// regression testing in this example. However, for didactic
// exercise it will be better to set it to a medium gray value
// such as 100 or 128.
resample->SetDefaultPixelValue( 0 );
typedef
unsigned
char
OutputPixelType;
typedef
itk::Image< OutputPixelType, ImageDimension >
OutputImageType;
typedef
itk::CastImageFilter
<
FixedImageType,
OutputImageType > CastFilterType;
typedef
itk::ImageFileWriter< OutputImageType >
WriterType;
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;
}
typedef
itk::SquaredDifferenceImageFilter
<
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 > 4 )
{
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 > 5 )
{
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.
if
( argc > 6 )
{
typedef
itk::Vector< float, ImageDimension >
VectorType;
typedef
itk::Image< VectorType, ImageDimension >
DisplacementFieldType;
DisplacementFieldType::Pointer field = DisplacementFieldType::New();
field->SetRegions( fixedRegion );
field->SetOrigin( fixedImage->GetOrigin() );
field->SetSpacing( fixedImage->GetSpacing() );
field->SetDirection( fixedImage->GetDirection() );
field->Allocate();
typedef
itk::ImageRegionIterator< DisplacementFieldType >
FieldIterator;
FieldIterator fi( field, fixedRegion );
fi.GoToBegin();
TransformType::InputPointType fixedPoint;
TransformType::OutputPointType movingPoint;
DisplacementFieldType::IndexType index;
VectorType displacement;
while
( ! fi.IsAtEnd() )
{
index = fi.GetIndex();
field->TransformIndexToPhysicalPoint( index, fixedPoint );
movingPoint = transform->TransformPoint( fixedPoint );
displacement = movingPoint - fixedPoint;
fi.Set( displacement );
++fi;
}
typedef
itk::ImageFileWriter< DisplacementFieldType >
FieldWriterType;
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput( field );
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;
}
}
// Optionally, save the transform parameters in a file
if
( argc > 7 )
{
std::ofstream parametersFile;
parametersFile.open( argv[7] );
parametersFile << finalParameters << std::endl;
parametersFile.close();
}
return
EXIT_SUCCESS;
}
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