Examples/Filtering/BinaryMedianImageFilter.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BinaryThresholdImageFilterOutput.png}
//    OUTPUTS: {BinaryMedianImageFilterOutput.png}
//    ARGUMENTS:    2 2
//  Software Guide : EndCommandLineArgs
//
//    BinaryThresholdImageFilterOutput.png was obtained from the
//    BinaryThreshold ImageFilter example.
//
//  Software Guide : BeginLatex
//
//  The \doxygen{BinaryMedianImageFilter} is commonly used as a robust
//  approach for noise reduction. BinaryMedianImageFilter computes the value
//  of each output pixel as the statistical median of the neighborhood of
//  values around the corresponding input pixel. When the input images are
//  binary, the implementation can be optimized by simply counting the number
//  of pixels ON/OFF around the current pixel.
//
//  This filter will work on images of any dimension thanks to the internal
//  use of \doxygen{NeighborhoodIterator} and \doxygen{NeighborhoodOperator}.
//  The size of the neighborhood over which the median is computed can be set
//  by the user.
//
//  \index{itk::BinaryMedianImageFilter}
//
//  Software Guide : EndLatex
 
 
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
 
//  Software Guide : BeginLatex
//
//  The header file corresponding to this filter should be included first.
//
//  \index{itk::BinaryMedianImageFilter!header}
//
//  Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkBinaryMedianImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile outputImageFile radiusX radiusY"
              << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Then the pixel and image types of the input and output must be defined.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InputPixelType = unsigned char;
  using OutputPixelType = unsigned char;
 
  using InputImageType = itk::Image<InputPixelType, 2>;
  using OutputImageType = itk::Image<OutputPixelType, 2>;
  // Software Guide : EndCodeSnippet
 
 
  using ReaderType = itk::ImageFileReader<InputImageType>;
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  ReaderType::Pointer reader = ReaderType::New();
  WriterType::Pointer writer = WriterType::New();
 
  reader->SetFileName(argv[1]);
  writer->SetFileName(argv[2]);
 
  //  Software Guide : BeginLatex
  //
  //  Using the image types, it is now possible to define the filter type
  //  and create the filter object.
  //
  //  \index{itk::BinaryMedianImageFilter!instantiation}
  //  \index{itk::BinaryMedianImageFilter!New()}
  //  \index{itk::BinaryMedianImageFilter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::BinaryMedianImageFilter<InputImageType, OutputImageType>;
 
  FilterType::Pointer filter = FilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The size of the neighborhood is defined along every dimension by
  //  passing a \code{SizeType} object with the corresponding values. The
  //  value on each dimension is used as the semi-size of a rectangular
  //  box. For example, in $2D$ a size of \(1,2\) will result in a $3 \times
  //  5$ neighborhood.
  //
  //  \index{itk::BinaryMedianImageFilter!Radius}
  //  \index{itk::BinaryMedianImageFilter!Neighborhood}
  //
  //  Software Guide : EndLatex
 
  const unsigned int radiusX = std::stoi(argv[3]);
  const unsigned int radiusY = std::stoi(argv[4]);
 
  // Software Guide : BeginCodeSnippet
  InputImageType::SizeType indexRadius;
 
  indexRadius[0] = radiusX; // radius along x
  indexRadius[1] = radiusY; // radius along y
 
  filter->SetRadius(indexRadius);
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  The input to the filter can be taken from any other filter, for example
  //  a reader. The output can be passed down the pipeline to other filters,
  //  for example, a writer. An update call on any downstream filter will
  //  trigger the execution of the median filter.
  //
  //  \index{itk::BinaryMedianImageFilter!SetInput()}
  //  \index{itk::BinaryMedianImageFilter!GetOutput()}
  //
  //  Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  filter->SetInput(reader->GetOutput());
  writer->SetInput(filter->GetOutput());
  writer->Update();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BinaryThresholdImageFilterOutput}
  // \includegraphics[width=0.44\textwidth]{BinaryMedianImageFilterOutput}
  // \itkcaption[Effect of the BinaryMedian filter.]{Effect of the
  // BinaryMedianImageFilter on a slice from a MRI proton density brain image
  // that has been thresholded in order to produce a binary image.}
  // \label{fig:BinaryMedianImageFilterOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:BinaryMedianImageFilterOutput} illustrates the effect of
  //  the BinaryMedianImageFilter filter on a slice of MRI brain image using a
  //  neighborhood radius of \(2,2\), which corresponds to a $ 5 \times 5 $
  //  classical neighborhood.  The filtered image demonstrates the capability
  //  of this filter for reducing noise both in the background and foreground
  //  of the image, as well as smoothing the contours of the regions.
  //
  //  Software Guide : EndLatex
 
 
  return EXIT_SUCCESS;
}