Examples/Filtering/LaplacianRecursiveGaussianImageFilter1.cxx

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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}
//    ARGUMENTS:    LaplacianRecursiveGaussianImageFilterOutput3.mha 3
//    OUTPUTS: {LaplacianRecursiveGaussianImageFilterOutput3.png}
//  Software Guide : EndCommandLineArgs

//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    ARGUMENTS:    LaplacianRecursiveGaussianImageFilterOutput5.mha 5
//    OUTPUTS: {LaplacianRecursiveGaussianImageFilterOutput5.png}
//  Software Guide : EndCommandLineArgs

//  Software Guide : BeginLatex
//
//  This example illustrates how to use the
//  \doxygen{RecursiveGaussianImageFilter} for computing the Laplacian of a 2D
//  image.
//
//  \index{itk::RecursiveGaussianImageFilter}
//
//  Software Guide : EndLatex


#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkAddImageFilter.h"

//  Software Guide : BeginLatex
//
//  The first step required to use this filter is to include its header file.
//
//  \index{itk::RecursiveGaussianImageFilter!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkRecursiveGaussianImageFilter.h"
// Software Guide : EndCodeSnippet
#include "itkRescaleIntensityImageFilter.h"


int main( int argc, char * argv[] )
{
  if( argc < 4 )
    {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputImageFile  sigma [RescaledOutputImageFile] " << std::endl;
    return EXIT_FAILURE;
    }


  //  Software Guide : BeginLatex
  //
  //  Types should be selected on the desired input and output pixel types.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using InputPixelType = float;
  using OutputPixelType = float;
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The input and output image types are instantiated using the pixel types.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using InputImageType = itk::Image< InputPixelType,  2 >;
  using OutputImageType = itk::Image< OutputPixelType, 2 >;
  // Software Guide : EndCodeSnippet


  using ReaderType = itk::ImageFileReader< InputImageType >;


  //  Software Guide : BeginLatex
  //
  //  The filter type is now instantiated using both the input image and the
  //  output image types.
  //
  //  \index{itk::RecursiveGaussianImageFilter!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using FilterType = itk::RecursiveGaussianImageFilter<
                        InputImageType, OutputImageType >;
  // Software Guide : EndCodeSnippet


  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName( argv[1] );


  //  Software Guide : BeginLatex
  //
  //  This filter applies the approximation of the convolution along a single
  //  dimension. It is therefore necessary to concatenate several of these filters
  //  to produce smoothing in all directions.  In this example, we create a pair
  //  of filters since we are processing a $2D$ image.  The filters are
  //  created by invoking the \code{New()} method and assigning the result to
  //  a \doxygen{SmartPointer}.
  //
  //  We need two filters for computing the X component of the Laplacian and
  //  two other filters for computing the Y component.
  //
  //  \index{itk::RecursiveGaussianImageFilter!New()}
  //  \index{itk::RecursiveGaussianImageFilter!Pointer}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  FilterType::Pointer filterX1 = FilterType::New();
  FilterType::Pointer filterY1 = FilterType::New();

  FilterType::Pointer filterX2 = FilterType::New();
  FilterType::Pointer filterY2 = FilterType::New();
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Since each one of the newly created filters has the potential to perform
  //  filtering along any dimension, we have to restrict each one to a
  //  particular direction. This is done with the \code{SetDirection()} method.
  //
  //  \index{RecursiveGaussianImageFilter!SetDirection()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filterX1->SetDirection( 0 );   // 0 --> X direction
  filterY1->SetDirection( 1 );   // 1 --> Y direction

  filterX2->SetDirection( 0 );   // 0 --> X direction
  filterY2->SetDirection( 1 );   // 1 --> Y direction
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The \doxygen{RecursiveGaussianImageFilter} can approximate the
  //  convolution with the Gaussian or with its first and second
  //  derivatives. We select one of these options by using the
  //  \code{SetOrder()} method. Note that the argument is an \code{enum} whose
  //  values can be \code{ZeroOrder}, \code{FirstOrder} and
  //  \code{SecondOrder}. For example, to compute the $x$ partial derivative we
  //  should select \code{FirstOrder} for $x$ and \code{ZeroOrder} for
  //  $y$. Here we want only to smooth in $x$ and $y$, so we select
  //  \code{ZeroOrder} in both directions.
  //
  //  \index{RecursiveGaussianImageFilter!SetOrder()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filterX1->SetOrder( FilterType::ZeroOrder );
  filterY1->SetOrder( FilterType::SecondOrder );

  filterX2->SetOrder( FilterType::SecondOrder );
  filterY2->SetOrder( FilterType::ZeroOrder );
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  There are two typical ways of normalizing Gaussians depending on their
  //  application. For scale-space analysis it is desirable to use a
  //  normalization that will preserve the maximum value of the input. This
  //  normalization is represented by the following equation.
  //
  //  \begin{equation}
  //          \frac{ 1 }{ \sigma  \sqrt{ 2 \pi } }
  //  \end{equation}
  //
  //  In applications that use the Gaussian as a solution of the diffusion
  //  equation it is desirable to use a normalization that preserves the
  //  integral of the signal. This last approach can be seen as a conservation
  //  of mass principle. This is represented by the following equation.
  //
  //  \begin{equation}
  //          \frac{ 1 }{ \sigma^2  \sqrt{ 2 \pi } }
  //  \end{equation}
  //
  //  The \doxygen{RecursiveGaussianImageFilter} has a boolean flag that allows
  //  users to select between these two normalization options. Selection is
  //  done with the method \code{SetNormalizeAcrossScale()}. Enable this flag
  //  when analyzing an image across scale-space.  In the current example, this
  //  setting has no impact because we are actually renormalizing the output to
  //  the dynamic range of the reader, so we simply disable the flag.
  //
  //  \index{RecursiveGaussianImageFilter!SetNormalizeAcrossScale()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  const bool normalizeAcrossScale = false;
  filterX1->SetNormalizeAcrossScale( normalizeAcrossScale );
  filterY1->SetNormalizeAcrossScale( normalizeAcrossScale );
  filterX2->SetNormalizeAcrossScale( normalizeAcrossScale );
  filterY2->SetNormalizeAcrossScale( normalizeAcrossScale );
  // 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 the source. The image is passed to
  //  the $x$ filter and then to the $y$ filter. The reason for keeping these
  //  two filters separate is that it is usual in scale-space applications to
  //  compute not only the smoothing but also combinations of derivatives at
  //  different orders and smoothing. Some factorization is possible when
  //  separate filters are used to generate the intermediate results. Here
  //  this capability is less interesting, though, since we only want to smooth
  //  the image in all directions.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filterX1->SetInput( reader->GetOutput() );
  filterY1->SetInput( filterX1->GetOutput() );

  filterY2->SetInput( reader->GetOutput() );
  filterX2->SetInput( filterY2->GetOutput() );
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  It is now time to select the $\sigma$ of the Gaussian used to smooth the
  //  data.  Note that $\sigma$ must be passed to both filters and 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::RecursiveGaussianImageFilter!SetSigma()}
  //  \index{SetSigma()!itk::RecursiveGaussianImageFilter}
  //
  //  Software Guide : EndLatex

  const double sigma = std::stod( argv[3] );

  // Software Guide : BeginCodeSnippet
  filterX1->SetSigma( sigma );
  filterY1->SetSigma( sigma );
  filterX2->SetSigma( sigma );
  filterY2->SetSigma( sigma );
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Finally the two components of the Laplacian should be added together. The
  //  \doxygen{AddImageFilter} is used for this purpose.
  //
  //  \index{itk::AddImageFilter!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using AddFilterType = itk::AddImageFilter<
                OutputImageType,
                OutputImageType,
                OutputImageType >;

  AddFilterType::Pointer addFilter = AddFilterType::New();

  addFilter->SetInput1( filterY1->GetOutput() );
  addFilter->SetInput2( filterX2->GetOutput() );
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The filters are triggered by invoking \code{Update()} on the Add filter
  //  at the end of the pipeline.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  try
    {
    addFilter->Update();
    }
  catch( itk::ExceptionObject & err )
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
    }
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The resulting image could be saved to a file using the
  //  \doxygen{ImageFileWriter} class.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using WritePixelType = float;

  using WriteImageType = itk::Image< WritePixelType, 2 >;

  using WriterType = itk::ImageFileWriter< WriteImageType >;

  WriterType::Pointer writer = WriterType::New();

  writer->SetInput( addFilter->GetOutput() );

  writer->SetFileName( argv[2] );

  writer->Update();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilterOutput3}
  // \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilterOutput5}
  // \itkcaption[Output of the LaplacianRecursiveGaussianImageFilter.]{Effect of the
  // LaplacianRecursiveGaussianImageFilter on a slice from a MRI proton density image
  // of the brain.}
  // \label{fig:LaplacianRecursiveGaussianImageFilterInputOutput}
  // \end{figure}
  //
  //  Software Guide : EndLatex


  // Rescale float outputs to png for inclusion in the Software guide
  //
  if (argc > 4)
    {
    using CharPixelType = unsigned char;
    using CharImageType = itk::Image<CharPixelType, 2>;

    using RescaleFilterType =
          itk::RescaleIntensityImageFilter< OutputImageType, CharImageType>;

    RescaleFilterType::Pointer rescale = RescaleFilterType::New();
    rescale->SetInput( addFilter->GetOutput() );
    rescale->SetOutputMinimum(   0 );
    rescale->SetOutputMaximum( 255 );
    using CharWriterType = itk::ImageFileWriter< CharImageType >;
    CharWriterType::Pointer charWriter = CharWriterType::New();
    charWriter->SetFileName( argv[4] );
    charWriter->SetInput( rescale->GetOutput() );
    charWriter->Update();
    }


  return EXIT_SUCCESS;
}