Examples/Filtering/MeanImageFilter.cxx

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 *  Copyright NumFOCUS
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 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {MeanImageFilterOutput.png}
//  Software Guide : EndCommandLineArgs
 
//  Software Guide : BeginLatex
//
//  The \doxygen{MeanImageFilter} is commonly used for noise reduction. The
//  filter computes the value of each output pixel by finding the statistical
//  mean of the neighborhood of the corresponding input pixel.  The following
//  figure illustrates the local effect of the MeanImageFilter in a $2D$
//  case. The statistical mean of the neighborhood on the left is passed as
//  the output value associated with the pixel at the center of the
//  neighborhood.
//
//  \begin{center}
//  \begin{picture}(200,46)
//  \put(   5.0,  0.0 ){\framebox(30.0,15.0){25}}
//  \put(  35.0,  0.0 ){\framebox(30.0,15.0){30}}
//  \put(  65.0,  0.0 ){\framebox(30.0,15.0){32}}
//  \put(   5.0, 15.0 ){\framebox(30.0,15.0){27}}
//  \put(  35.0, 15.0 ){\framebox(30.0,15.0){25}}
//  \put(  65.0, 15.0 ){\framebox(30.0,15.0){29}}
//  \put(   5.0, 30.0 ){\framebox(30.0,15.0){28}}
//  \put(  35.0, 30.0 ){\framebox(30.0,15.0){26}}
//  \put(  65.0, 30.0 ){\framebox(30.0,15.0){50}}
//  \put( 100.0, 22.0 ){\vector(1,0){20.0}}
//  \put( 125.0, 15.0 ){\framebox(34.0,15.0){30.22}}
//  \put( 160.0, 22.0 ){\vector(1,0){20.0}}
//  \put( 185.0, 15.0 ){\framebox(30.0,15.0){30}}
//  \end{picture}
//  \end{center}
//
//  Note that this algorithm is sensitive to the presence of outliers in the
//  neighborhood.  This filter will work on images of any dimension thanks to
//  the internal use of \doxygen{SmartNeighborhoodIterator} and
//  \doxygen{NeighborhoodOperator}.  The size of the neighborhood over which
//  the mean is computed can be set by the user.
//
//  \index{itk::MeanImageFilter}
//
//  Software Guide : EndLatex
 
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
 
//  Software Guide : BeginLatex
//
//  The header file corresponding to this filter should be included first.
//
//  \index{itk::MeanImageFilter!header}
//
//  Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkMeanImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 3)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile   outputImageFile" << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Then the pixel types for input and output image must be defined and,
  //  with them, the image types can be instantiated.
  //
  //  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 instantiate the filter type
  //  and create the filter object.
  //
  //  \index{itk::MeanImageFilter!instantiation}
  //  \index{itk::MeanImageFilter!New()}
  //  \index{itk::MeanImageFilter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType = itk::MeanImageFilter<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::MeanImageFilter!Radius}
  //  \index{itk::MeanImageFilter!Neighborhood}
  //
  //  Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  InputImageType::SizeType indexRadius;
 
  indexRadius[0] = 1; // radius along x
  indexRadius[1] = 1; // 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 mean filter.
  //
  //  \index{itk::MeanImageFilter!SetInput()}
  //  \index{itk::MeanImageFilter!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]{BrainProtonDensitySlice}
  // \includegraphics[width=0.44\textwidth]{MeanImageFilterOutput}
  // \itkcaption[Effect of the MedianImageFilter]{Effect of the
  // MeanImageFilter on a slice from a MRI proton density brain image.}
  // \label{fig:MeanImageFilterOutput} \end{figure}
  //
  //  Figure \ref{fig:MeanImageFilterOutput} illustrates the effect of this
  //  filter on a slice of MRI brain image using neighborhood radii of
  //  \(1,1\) which corresponds to a $ 3 \times 3 $ classical neighborhood.
  //  It can be seen from this picture that edges are rapidly degraded by the
  //  diffusion of intensity values among neighbors.
  //
  //  Software Guide : EndLatex
 
 
  return EXIT_SUCCESS;
}