ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/MeanSquaresImageMetric1.cxx
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// Software Guide : BeginLatex
//
// This example illustrates how to explore the domain of an image metric. This
// is a useful exercise to do before starting a registration process, since
// getting familiar with the characteristics of the metric is fundamental for
// the appropriate selection of the optimizer to be use for driving the
// registration process, as well as for selecting the optimizer parameters.
// This process makes possible to identify how noisy a metric may be in a given
// range of parameters, and it will also give an idea of the number of local
// minima or maxima in which an optimizer may get trapped while exploring the
// parametric space.
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// We start by including the headers of the basic components: Metric, Transform
// and Interpolator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main( int argc, char * argv[] )
{
if( argc < 3 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " fixedImage movingImage" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We define the dimension and pixel type of the images to be used in the
// evaluation of the Metric.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
typedef unsigned char PixelType;
// Software Guide : EndCodeSnippet
try
{
}
catch( itk::ExceptionObject & excep )
{
std::cerr << "Exception catched !" << std::endl;
std::cerr << excep << std::endl;
}
// Software Guide : BeginLatex
//
// The type of the Metric is instantiated and one is constructed. In this case
// we decided to use the same image type for both the fixed and the moving
// images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ImageType, ImageType > MetricType;
MetricType::Pointer metric = MetricType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We also instantiate the transform and interpolator types, and create objects
// of each class.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
ImageType, double > InterpolatorType;
InterpolatorType::Pointer interpolator = InterpolatorType::New();
// Software Guide : EndCodeSnippet
transform->SetIdentity();
// Software Guide : BeginLatex
//
// The classes required by the metric are connected to it. This includes the
// fixed and moving images, the interpolator and the transform.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->SetTransform( transform );
metric->SetInterpolator( interpolator );
metric->SetFixedImage( fixedImage );
metric->SetMovingImage( movingImage );
// Software Guide : EndCodeSnippet
metric->SetFixedImageRegion( fixedImage->GetBufferedRegion() );
try
{
metric->Initialize();
}
catch( itk::ExceptionObject & excep )
{
std::cerr << "Exception catched !" << std::endl;
std::cerr << excep << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Finally we select a region of the parametric space to explore. In this case
// we are using a translation transform in 2D, so we simply select translations
// from a negative position to a positive position, in both $x$ and $y$. For
// each one of those positions we invoke the GetValue() method of the Metric.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MetricType::TransformParametersType displacement( Dimension );
const int rangex = 50;
const int rangey = 50;
for( int dx = -rangex; dx <= rangex; dx++ )
{
for( int dy = -rangey; dy <= rangey; dy++ )
{
displacement[0] = dx;
displacement[1] = dy;
const double value = metric->GetValue( displacement );
std::cout << dx << " " << dy << " " << value << std::endl;
}
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[height=0.33\textwidth]{MeanSquaresMetricPlot1}
// \includegraphics[height=0.33\textwidth]{MeanSquaresMetricPlot2}
// \itkcaption[Mean Squares Metric Plots]{Plots of the Mean Squares Metric for
// an image compared to itself under multiple translations.}
// \label{fig:MeanSquaresMetricPlot}
// \end{figure}
//
// Running this code using the image BrainProtonDensitySlice.png as both the
// fixed and the moving images results in the plot shown in
// Figure~\ref{fig:MeanSquaresMetricPlot}. From this Figure, it can be seen
// that a gradient based optimizer will be appropriate for finding the extrema
// of the Metric. It is also possible to estimate a good value for the step
// length of a gradient-descent optimizer.
//
// This exercise of plotting the Metric is probably the best thing to do when a
// registration process is not converging and when it is unclear how to fine
// tune the different parameters involved in the registration. This includes
// the optimizer parameters, the metric parameters and even options such as
// preprocessing the image data with smoothing filters.
//
// The shell and Gnuplot\footnote{http://www.gnuplot.info} scripts used for
// generating the graphics in Figure~\ref{fig:MeanSquaresMetricPlot} are
// available in the directory
//
// \code{InsightDocuments/SoftwareGuide/Art}
//
// Of course, this plotting exercise becomes more challenging when the
// transform has more than three parameters, and when those parameters have
// very different range of values. In those cases is necessary to select only a
// key subset of parameters from the transform and to study the behavior of the
// metric when those parameters are varied.
//
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}