Examples/Filtering/VotingBinaryIterativeHoleFillingImageFilter.cxx

/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
 
// Software Guide : BeginCommandLineArgs
//   INPUTS:  {BinaryThresholdImageFilterOutput.png}
//   OUTPUTS: {VotingBinaryIterativeHoleFillingImageFilterOutput1.png}
//   ARGUMENTS:    1 1 20
// Software Guide : EndCommandLineArgs
 
// Software Guide : BeginCommandLineArgs
//   INPUTS:  {BinaryThresholdImageFilterOutput.png}
//   OUTPUTS: {VotingBinaryIterativeHoleFillingImageFilterOutput2.png}
//   ARGUMENTS:    2 2 20
// Software Guide : EndCommandLineArgs
 
// Software Guide : BeginCommandLineArgs
//   INPUTS:  {BinaryThresholdImageFilterOutput.png}
//   OUTPUTS: {VotingBinaryIterativeHoleFillingImageFilterOutput3.png}
//   ARGUMENTS:    3 3 20
// Software Guide : EndCommandLineArgs
 
 
//  Software Guide : BeginLatex
//
//  The \doxygen{VotingBinaryIterativeHoleFillingImageFilter} applies a voting
//  operation in order to fill in cavities. This can be used for smoothing
//  contours and for filling holes in binary images. This filter runs
//  a \doxygen{VotingBinaryHoleFillingImageFilter} internally until no
//  pixels change or the maximum number of iterations has been reached.
//
//  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter}
//
//  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::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!header}
//
//  Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkVotingBinaryIterativeHoleFillingImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0]
              << "  inputImageFile outputImageFile radiusX radiusY iterations"
              << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Then the pixel and image types must be defined. Note that this filter
  //  requires the input and output images to be of the same type, therefore a
  //  single image type is required for the template instantiation.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using PixelType = unsigned char;
 
  using ImageType = itk::Image<PixelType, 2>;
  // Software Guide : EndCodeSnippet
 
 
  using ReaderType = itk::ImageFileReader<ImageType>;
  using WriterType = itk::ImageFileWriter<ImageType>;
 
  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::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!instantiation}
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!New()}
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::VotingBinaryIterativeHoleFillingImageFilter<ImageType>;
 
  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::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!Radius}
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!Neighborhood}
  //
  //  Software Guide : EndLatex
 
  const unsigned int radiusX = std::stoi(argv[3]);
  const unsigned int radiusY = std::stoi(argv[4]);
 
  // Software Guide : BeginCodeSnippet
  ImageType::SizeType indexRadius;
 
  indexRadius[0] = radiusX; // radius along x
  indexRadius[1] = radiusY; // radius along y
 
  filter->SetRadius(indexRadius);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Since the filter is expecting a binary image as input, we must specify
  //  the levels that are going to be considered background and foreground.
  //  This is done with the \code{SetForegroundValue()} and
  //  \code{SetBackgroundValue()} methods.
  //
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!SetForegroundValue()}
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!SetBackgroundValue()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetBackgroundValue(0);
  filter->SetForegroundValue(255);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  We must also specify the majority threshold that is going to be used as
  //  the decision criterion for converting a background pixel into a
  //  foreground pixel. The rule of conversion is that a background pixel will
  //  be converted into a foreground pixel if the number of foreground
  //  neighbors surpass the number of background neighbors by the majority
  //  value. For example, in a 2D image, with neighborhood of radius 1, the
  //  neighborhood will have size $3 \times 3$. If we set the majority value
  //  to 2, then we are requiring that the number of foreground neighbors
  //  should be at least (3x3 -1 )/2 + majority. This is done with the
  //  \code{SetMajorityThreshold()} method.
  //
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!SetMajorityThreshold()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetMajorityThreshold(2);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Finally we specify the maximum number of iterations for which this
  //  filter should run. The number of iterations will determine the maximum
  //  size of holes and cavities that this filter will be able to fill. The
  //  more iterations you run, the larger the cavities that will be filled in.
  //
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!SetMaximumNumberOfIterations()}
  //
  //  Software Guide : EndLatex
 
  const unsigned int numberOfIterations = std::stoi(argv[5]);
 
  // Software Guide : BeginCodeSnippet
  filter->SetMaximumNumberOfIterations(numberOfIterations);
  // 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::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!SetInput()}
  //  \index{itk::Voting\-Binary\-Iterative\-Hole\-Filling\-Image\-Filter!GetOutput()}
  //
  //  Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  filter->SetInput(reader->GetOutput());
  writer->SetInput(filter->GetOutput());
  writer->Update();
  // Software Guide : EndCodeSnippet
 
  const unsigned int iterationsUsed = filter->GetCurrentNumberOfIterations();
 
  std::cout << "The filter used " << iterationsUsed << " iterations "
            << std::endl;
 
  const unsigned int numberOfPixelsChanged =
    filter->GetNumberOfPixelsChanged();
 
  std::cout << "and changed a total of " << numberOfPixelsChanged << " pixels"
            << std::endl;
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BinaryThresholdImageFilterOutput}
  // \includegraphics[width=0.44\textwidth]{VotingBinaryIterativeHoleFillingImageFilterOutput1}
  // \includegraphics[width=0.44\textwidth]{VotingBinaryIterativeHoleFillingImageFilterOutput2}
  // \includegraphics[width=0.44\textwidth]{VotingBinaryIterativeHoleFillingImageFilterOutput3}
  // \itkcaption[Effect of the VotingBinaryIterativeHoleFilling
  // filter.]{Effect of the VotingBinaryIterativeHoleFillingImageFilter on a
  // slice from a MRI proton density brain image that has been thresholded in
  // order to produce a binary image. The output images have used radius 1,2
  // and 3 respectively.}
  // \label{fig:VotingBinaryIterativeHoleFillingImageFilterOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:VotingBinaryIterativeHoleFillingImageFilterOutput}
  //  illustrates the effect of the
  //  VotingBinaryIterativeHoleFillingImageFilter filter on a thresholded
  //  slice of MRI brain image using neighborhood radii of \(1,1\), \(2,2\)
  //  and \(3,3\) that correspond respectively to neighborhoods of size $ 3
  //  \times 3 $,  $ 5 \times 5 $, $ 7 \times 7 $.  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;
}