ITK  4.13.0
Insight Segmentation and Registration Toolkit
WikiExamples/Registration/MutualInformation.cxx
const unsigned int Dimension = 2;
typedef unsigned char PixelType;
static void CreateEllipseImage(ImageType::Pointer image);
static void CreateCircleImage(ImageType::Pointer image);
int main( int argc, char *argv[] )
{
// Generate synthetic fixed and moving images
ImageType::Pointer fixedImage = ImageType::New();
CreateCircleImage(fixedImage);
ImageType::Pointer movingImage = ImageType::New();
CreateEllipseImage(movingImage);
// Write the two synthetic inputs
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();
// We use floats internally
typedef float InternalPixelType;
typedef itk::Image< float, 2> InternalImageType;
// Normalize the images
NormalizeFilterType::Pointer fixedNormalizer = NormalizeFilterType::New();
NormalizeFilterType::Pointer movingNormalizer = NormalizeFilterType::New();
fixedNormalizer->SetInput( fixedImage);
movingNormalizer->SetInput( movingImage);
// Smooth the normalized images
GaussianFilterType::Pointer fixedSmoother = GaussianFilterType::New();
GaussianFilterType::Pointer movingSmoother = GaussianFilterType::New();
fixedSmoother->SetVariance( 2.0 );
movingSmoother->SetVariance( 2.0 );
fixedSmoother->SetInput( fixedNormalizer->GetOutput() );
movingSmoother->SetInput( movingNormalizer->GetOutput() );
typedef itk::GradientDescentOptimizer OptimizerType;
InternalImageType,
double > InterpolatorType;
InternalImageType,
InternalImageType > RegistrationType;
InternalImageType,
InternalImageType > MetricType;
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetInterpolator( interpolator );
MetricType::Pointer metric = MetricType::New();
registration->SetMetric( metric );
// The metric requires a number of parameters to be selected, including
// the standard deviation of the Gaussian kernel for the fixed image
// density estimate, the standard deviation of the kernel for the moving
// image density and the number of samples use to compute the densities
// and entropy values. Details on the concepts behind the computation of
// the metric can be found in Section
// \ref{sec:MutualInformationMetric}. Experience has
// shown that a kernel standard deviation of $0.4$ works well for images
// which have been normalized to a mean of zero and unit variance. We
// will follow this empirical rule in this example.
metric->SetFixedImageStandardDeviation( 0.4 );
metric->SetMovingImageStandardDeviation( 0.4 );
registration->SetFixedImage( fixedSmoother->GetOutput() );
registration->SetMovingImage( movingSmoother->GetOutput() );
fixedNormalizer->Update();
ImageType::RegionType fixedImageRegion =
fixedNormalizer->GetOutput()->GetBufferedRegion();
registration->SetFixedImageRegion( fixedImageRegion );
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 );
// Software Guide : BeginLatex
//
// We should now define the number of spatial samples to be considered in
// the metric computation. Note that we were forced to postpone this setting
// until we had done the preprocessing of the images because the number of
// samples is usually defined as a fraction of the total number of pixels in
// the fixed image.
//
// The number of spatial samples can usually be as low as $1\%$ of the total
// number of pixels in the fixed image. Increasing the number of samples
// improves the smoothness of the metric from one iteration to another and
// therefore helps when this metric is used in conjunction with optimizers
// that rely of the continuity of the metric values. The trade-off, of
// course, is that a larger number of samples result in longer computation
// times per every evaluation of the metric.
//
// It has been demonstrated empirically that the number of samples is not a
// critical parameter for the registration process. When you start fine
// tuning your own registration process, you should start using high values
// of number of samples, for example in the range of $20\%$ to $50\%$ of the
// number of pixels in the fixed image. Once you have succeeded to register
// your images you can then reduce the number of samples progressively until
// you find a good compromise on the time it takes to compute one evaluation
// of the Metric. Note that it is not useful to have very fast evaluations
// of the Metric if the noise in their values results in more iterations
// being required by the optimizer to converge. You must then study the
// behavior of the metric values as the iterations progress.
const unsigned int numberOfPixels = fixedImageRegion.GetNumberOfPixels();
const unsigned int numberOfSamples =
static_cast< unsigned int >( numberOfPixels * 0.01 );
metric->SetNumberOfSpatialSamples( numberOfSamples );
// Since larger values of mutual information indicate better matches than
// smaller values, we need to maximize the cost function in this example.
// By default the GradientDescentOptimizer class is set to minimize the
// value of the cost-function. It is therefore necessary to modify its
// default behavior by invoking the \code{MaximizeOn()} method.
// Additionally, we need to define the optimizer's step size using the
// \code{SetLearningRate()} method.
optimizer->SetLearningRate( 15.0 );
optimizer->SetNumberOfIterations( 200 );
optimizer->MaximizeOn(); // We want to maximize mutual information (the default of the optimizer is to minimize)
// Note that large values of the learning rate will make the optimizer
// unstable. Small values, on the other hand, may result in the optimizer
// needing too many iterations in order to walk to the extrema of the cost
// function. The easy way of fine tuning this parameter is to start with
// small values, probably in the range of $\{5.0,10.0\}$. Once the other
// registration parameters have been tuned for producing convergence, you
// may want to revisit the learning rate and start increasing its value until
// you observe that the optimization becomes unstable. The ideal value for
// this parameter is the one that results in a minimum number of iterations
// while still keeping a stable path on the parametric space of the
// optimization. Keep in mind that this parameter is a multiplicative factor
// applied on the gradient of the Metric. Therefore, its effect on the
// optimizer step length is proportional to the Metric values themselves.
// Metrics with large values will require you to use smaller values for the
// learning rate in order to maintain a similar optimizer behavior.
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch( itk::ExceptionObject & err )
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[0];
double TranslationAlongY = finalParameters[1];
unsigned int numberOfIterations = optimizer->GetCurrentIteration();
double bestValue = optimizer->GetValue();
// Print out results
std::cout << std::endl;
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;
std::cout << " Numb. Samples = " << numberOfSamples << std::endl;
ImageType,
ImageType > ResampleFilterType;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( finalTransform );
resample->SetInput( movingImage);
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( 100 );
WriterType::Pointer writer = WriterType::New();
writer->SetFileName("output.png");
writer->SetInput( resample->GetOutput() );
writer->Update();
return EXIT_SUCCESS;
}
void CreateEllipseImage(ImageType::Pointer image)
{
EllipseType, ImageType > SpatialObjectToImageFilterType;
SpatialObjectToImageFilterType::Pointer imageFilter =
SpatialObjectToImageFilterType::New();
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 CreateCircleImage(ImageType::Pointer image)
{
EllipseType, ImageType > SpatialObjectToImageFilterType;
SpatialObjectToImageFilterType::Pointer imageFilter =
SpatialObjectToImageFilterType::New();
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());
}