ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/DeformableRegistration3.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 demonstrates how to use a variant of the ``demons'' algorithm to
// deformably register two images. This variant uses a different formulation
// for computing the forces to be applied to the image in order to compute the
// deformation fields. The variant uses both the gradient of the fixed image
// and the gradient of the deformed moving image in order to compute the
// forces. This mechanism for computing the forces introduces a symmetry with
// respect to the choice of the fixed and moving images. This symmetry only
// holds during the computation of one iteration of the PDE updates. It is
// unlikely that total symmetry may be achieved by this mechanism for the
// entire registration process.
//
// The first step for using this filter is to include the following header files.
//
// 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.
//
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
itkNewMacro( CommandIterationUpdate );
protected:
CommandIterationUpdate() {};
using InternalImageType = itk::Image< float, 2 >;
using VectorPixelType = itk::Vector< float, 2 >;
using DisplacementFieldType = itk::Image< VectorPixelType, 2 >;
using RegistrationFilterType = itk::SymmetricForcesDemonsRegistrationFilter<
InternalImageType,
InternalImageType,
DisplacementFieldType>;
public:
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
{
const RegistrationFilterType * filter = static_cast< const RegistrationFilterType * >( object );
if( !(itk::IterationEvent().CheckEvent( &event )) )
{
return;
}
std::cout << filter->GetMetric() << 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 ";
std::cerr << " outputImageFile " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Second, we declare the types of the images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 2;
using PixelType = unsigned short;
using FixedImageType = itk::Image< PixelType, Dimension >;
using MovingImageType = itk::Image< PixelType, Dimension >;
// Software Guide : EndCodeSnippet
// Set up the file readers
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] );
// Software Guide : BeginLatex
//
// Image file readers are set up in a similar fashion to previous examples.
// To support the re-mapping of the moving image intensity, we declare an
// internal image type with a floating point pixel type and cast the input
// images to the internal image type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InternalPixelType = float;
using InternalImageType = itk::Image< InternalPixelType, Dimension >;
using FixedImageCasterType = itk::CastImageFilter< FixedImageType,
InternalImageType >;
using MovingImageCasterType = itk::CastImageFilter< MovingImageType,
InternalImageType >;
FixedImageCasterType::Pointer fixedImageCaster = FixedImageCasterType::New();
MovingImageCasterType::Pointer movingImageCaster
= MovingImageCasterType::New();
fixedImageCaster->SetInput( fixedImageReader->GetOutput() );
movingImageCaster->SetInput( movingImageReader->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The demons algorithm relies on the assumption that pixels representing the
// same homologous point on an object have the same intensity on both the
// fixed and moving images to be registered. In this example, we will
// preprocess the moving image to match the intensity between the images
// using the \doxygen{HistogramMatchingImageFilter}.
//
// \index{itk::Histogram\-Matching\-Image\-Filter}
//
// The basic idea is to match the histograms of the two images at a user-specified number of quantile values. For robustness, the histograms are
// matched so that the background pixels are excluded from both histograms.
// For MR images, a simple procedure is to exclude all gray values that are
// smaller than the mean gray value of the image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MatchingFilterType = itk::HistogramMatchingImageFilter<
InternalImageType,
InternalImageType >;
MatchingFilterType::Pointer matcher = MatchingFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// For this example, we set the moving image as the source or input image and
// the fixed image as the reference image.
//
// \index{itk::Histogram\-Matching\-Image\-Filter!SetInput()}
// \index{itk::Histogram\-Matching\-Image\-Filter!SetSourceImage()}
// \index{itk::Histogram\-Matching\-Image\-Filter!SetReferenceImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
matcher->SetInput( movingImageCaster->GetOutput() );
matcher->SetReferenceImage( fixedImageCaster->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We then select the number of bins to represent the histograms and the
// number of points or quantile values where the histogram is to be
// matched.
//
// \index{itk::Histogram\-Matching\-Image\-Filter!Set\-Number\-Of\-Histogram\-Levels()}
// \index{itk::Histogram\-Matching\-Image\-Filter!Set\-Number\-Of\-Match\-Points()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
matcher->SetNumberOfHistogramLevels( 1024 );
matcher->SetNumberOfMatchPoints( 7 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Simple background extraction is done by thresholding at the mean
// intensity.
//
// \index{itk::Histogram\-Matching\-Image\-Filter!Threshold\-At\-Mean\-Intensity\-On()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
matcher->ThresholdAtMeanIntensityOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the \doxygen{SymmetricForcesDemonsRegistrationFilter}, the deformation field is
// represented as an image whose pixels are floating point vectors.
//
// \index{itk::Symmetric\-Forces\-Demons\-Registration\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using VectorPixelType = itk::Vector< float, Dimension >;
using DisplacementFieldType = itk::Image< VectorPixelType, Dimension >;
using RegistrationFilterType = itk::SymmetricForcesDemonsRegistrationFilter<
InternalImageType,
InternalImageType,
DisplacementFieldType>;
RegistrationFilterType::Pointer filter = RegistrationFilterType::New();
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the registration filter.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
filter->AddObserver( itk::IterationEvent(), observer );
// Software Guide : BeginLatex
//
// The input fixed image is simply the output of the fixed image casting
// filter. The input moving image is the output of the histogram matching
// filter.
//
// \index{itk::Symmetric\-Forces\-Demons\-Registration\-Filter!SetFixedImage()}
// \index{itk::Symmetric\-Forces\-Demons\-Registration\-Filter!SetMovingImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetFixedImage( fixedImageCaster->GetOutput() );
filter->SetMovingImage( matcher->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The demons registration filter has two parameters: the number of
// iterations to be performed and the standard deviation of the Gaussian
// smoothing kernel to be applied to the deformation field after each
// iteration.
// \index{itk::Symmetric\-Forces\-Demons\-Registration\-Filter!SetNumberOfIterations()}
// \index{itk::Symmetric\-Forces\-Demons\-Registration\-Filter!SetStandardDeviations()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNumberOfIterations( 50 );
filter->SetStandardDeviations( 1.0 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The registration algorithm is triggered by updating the filter. The
// filter output is the computed deformation field.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \doxygen{WarpImageFilter} can be used to warp the moving image with
// the output deformation field. Like the \doxygen{ResampleImageFilter},
// the WarpImageFilter requires the specification of the input image to be
// resampled, an input image interpolator, and the output image spacing and
// origin.
//
// \index{itk::WarpImageFilter}
// \index{itk::WarpImageFilter!SetInput()}
// \index{itk::WarpImageFilter!SetInterpolator()}
// \index{itk::WarpImageFilter!SetOutputSpacing()}
// \index{itk::WarpImageFilter!SetOutputOrigin()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using WarperType = itk::WarpImageFilter<
MovingImageType,
MovingImageType,
DisplacementFieldType >;
using InterpolatorType = itk::LinearInterpolateImageFunction<
MovingImageType,
double >;
WarperType::Pointer warper = WarperType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
warper->SetInput( movingImageReader->GetOutput() );
warper->SetInterpolator( interpolator );
warper->SetOutputSpacing( fixedImage->GetSpacing() );
warper->SetOutputOrigin( fixedImage->GetOrigin() );
warper->SetOutputDirection( fixedImage->GetDirection() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Unlike the ResampleImageFilter, the WarpImageFilter
// warps or transform the input image with respect to the deformation field
// represented by an image of vectors. The resulting warped or resampled
// image is written to file as per previous examples.
//
// \index{itk::WarpImageFilter!SetDisplacementField()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
warper->SetDisplacementField( filter->GetOutput() );
// Software Guide : EndCodeSnippet
// Write warped image out to file
using OutputPixelType = unsigned char;
using CastFilterType = itk::CastImageFilter<
MovingImageType,
OutputImageType >;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( warper->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
// Software Guide : BeginLatex
//
// Let's execute this example using the rat lung data from the previous example.
// The associated data files can be found in \code{Examples/Data}:
//
// \begin{itemize}
// \item \code{RatLungSlice1.mha}
// \item \code{RatLungSlice2.mha}
// \end{itemize}
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{DeformableRegistration2CheckerboardBefore}
// \includegraphics[width=0.44\textwidth]{DeformableRegistration2CheckerboardAfter}
// \itkcaption[Demon's deformable registration output]{Checkerboard comparisons
// before and after demons-based deformable registration.}
// \label{fig:DeformableRegistration3Output}
// \end{figure}
//
// The result of the demons-based deformable registration is presented in
// Figure \ref{fig:DeformableRegistration3Output}. The checkerboard
// comparison shows that the algorithm was able to recover the misalignment
// due to expiration.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// It may be also desirable to write the deformation field as an image of
// vectors. This can be done with the following code.
//
// Software Guide : EndLatex
if( argc > 4 ) // if a fourth line argument has been provided...
{
// Software Guide : BeginCodeSnippet
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetFileName( argv[4] );
fieldWriter->SetInput( filter->GetOutput() );
fieldWriter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that the file format used for writing the deformation field must be
// capable of representing multiple components per pixel. This is the case
// for the MetaImage and VTK file formats for example.
//
// Software Guide : EndLatex
}
return EXIT_SUCCESS;
}