ITK  5.2.0 Insight Toolkit
Examples/Filtering/SmoothingRecursiveGaussianImageFilter2.cxx
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// Software Guide : BeginLatex
//
// Setting up a pipeline of $m$ filters in order to smooth an N-dimensional
// image may be a lot of work to do for achieving a simple goal. In order to
// avoid this inconvenience, a filter packaging this $m$ filters internally
// is available. This filter is the
// \doxygen{SmoothingRecursiveGaussianImageFilter}.
//
// \index{itk::SmoothingRecursiveGaussianImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// In order to use this filter the following header file must be included.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 4)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile sigma "
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Appropriate pixel types must be selected to support input and output
// images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// With them, the input and output image types can be instantiated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputImageType = itk::Image<InputPixelType, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types. If the second template parameter is omitted, the
// filter will assume that the output image has the same type as the input
// image.
//
// \index{itk::SmoothingRecursiveGaussianImageFilter!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
OutputImageType>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now a single filter is enough for smoothing the image along all the
// dimensions. The filter is created by invoking the \code{New()} method
// and assigning the result to a \doxygen{SmartPointer}.
//
// \index{itk::SmoothingRecursiveGaussianImageFilter!New()}
// \index{itk::SmoothingRecursiveGaussianImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As in the previous examples we should decide what type of normalization
// to use during the computation of the Gaussians. The method
// \code{SetNormalizeAcrossScale()} serves this purpose.
// \index{SmoothingRecursiveGaussianImageFilter!SetNormalizeAcrossScale()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNormalizeAcrossScale(false);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as source. The image is passed directly to the
// smoothing filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// It is now time for selecting the $\sigma$ of the Gaussian to use for
// smoothing the data. Note that $\sigma$ is considered to be in
// millimeters. That is, at the moment of applying the smoothing process,
// the filter will take into account the spacing values defined in the
// image.
//
// \index{itk::SmoothingRecursiveGaussianImageFilter!SetSigma()}
// \index{SetSigma()!itk::SmoothingRecursiveGaussianImageFilter}
//
// Software Guide : EndLatex
const double sigma = std::stod(argv[3]);
// Software Guide : BeginCodeSnippet
filter->SetSigma(sigma);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally the pipeline is executed by invoking the \code{Update()} method.
//
// \index{itk::SmoothingRecursiveGaussianImageFilter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{SmoothingRecursiveGaussianImageFilterOutput3}
// \includegraphics[width=0.44\textwidth]{SmoothingRecursiveGaussianImageFilterOutput5}
// \itkcaption[SmoothingRecursiveGaussianImageFilter output]{Effect of the
// SmoothingRecursiveGaussianImageFilter on a slice from a MRI proton
// density image of the brain.}
// \label{fig:SmoothingRecursiveGaussianImageFilterInputOutput} \end{figure}
//
// Figure \ref{fig:SmoothingRecursiveGaussianImageFilterInputOutput}
// illustrates the effect of this filter on a MRI proton density image of
// the brain using a $\sigma$ value of $3$ (left) and a value of $5$
// (right). The figure shows how the attenuation of noise can be
// regulated by selecting an appropriate sigma. This type of scale-tunable
// filter is suitable for performing scale-space analysis.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkSmoothingRecursiveGaussianImageFilter.h
Data source that reads image data from a single file.