Examples/Segmentation/VectorConfidenceConnected.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {VectorConfidenceConnectedOutput1.png}
//    ARGUMENTS:    70 120 7 1
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {VectorConfidenceConnectedOutput2.png}
//    ARGUMENTS:    23 93 7 1
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {VectorConfidenceConnectedOutput3.png}
//    ARGUMENTS:    66 66 3 1
//  Software Guide : EndCommandLineArgs
 
//  Software Guide : BeginLatex
//
//  This example illustrates the use of the confidence connected concept
//  applied to images with vector pixel types. The confidence connected
//  algorithm is implemented for vector images in the class
//  \doxygen{VectorConfidenceConnected}. The basic difference between the
//  scalar and vector version is that the vector version uses the covariance
//  matrix instead of a variance, and a vector mean instead of a scalar mean.
//  The membership of a vector pixel value to the region is measured using the
//  Mahalanobis distance as implemented in the class
//  \subdoxygen{Statistics}{MahalanobisDistanceThresholdImageFunction}.
//
//  Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkVectorConfidenceConnectedImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRGBPixel.h"
 
 
int
main(int argc, char * argv[])
{
  if (argc < 7)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0] << " inputImage  outputImage"
              << " seedX seedY"
              << " multiplier iterations" << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  We now define the image type using a particular pixel type and
  //  dimension. In this case the \code{float} type is used for the pixels
  //  due to the requirements of the smoothing filter.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 2;
 
  using PixelComponentType = unsigned char;
  using InputPixelType = itk::RGBPixel<PixelComponentType>;
  using InputImageType = itk::Image<InputPixelType, Dimension>;
  // Software Guide : EndCodeSnippet
 
  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
 
 
  // We instantiate reader and writer types
  //
  using ReaderType = itk::ImageFileReader<InputImageType>;
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  auto reader = ReaderType::New();
  auto writer = WriterType::New();
 
  reader->SetFileName(argv[1]);
  writer->SetFileName(argv[2]);
 
  //  Software Guide : BeginLatex
  //
  //  We now declare the type of the region-growing filter. In this case it
  //  is the \doxygen{VectorConfidenceConnectedImageFilter}.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using ConnectedFilterType =
    itk::VectorConfidenceConnectedImageFilter<InputImageType,
                                              OutputImageType>;
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Then, we construct one filter of this class using the \code{New()}
  //  method.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto confidenceConnected = ConnectedFilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Next we create a simple, linear data processing pipeline.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetInput(reader->GetOutput());
  writer->SetInput(confidenceConnected->GetOutput());
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  \code{VectorConfidenceConnectedImageFilter} requires two
  //  parameters.  First, the multiplier factor $f$ defines how large the
  //  range of intensities will be. Small values of the multiplier will
  //  restrict the inclusion of pixels to those having similar intensities to
  //  those already in the current region. Larger values of the multiplier
  //  relax the accepting condition and result in more generous growth of the
  //  region. Values that are too large will cause the region to grow into
  //  neighboring regions which may actually belong to separate anatomical
  //  structures.
  //
  //  \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetMultiplier()}
  //
  //  Software Guide : EndLatex
 
  const double multiplier = std::stod(argv[5]);
 
  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetMultiplier(multiplier);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The number of iterations is typically determined based on the
  //  homogeneity of the image intensity representing the anatomical
  //  structure to be segmented. Highly homogeneous regions may only require
  //  a couple of iterations. Regions with ramp effects, like MRI images with
  //  inhomogeneous fields, may require more iterations. In practice, it seems
  //  to be more relevant to carefully select the multiplier factor than the
  //  number of iterations.  However, keep in mind that there is no reason to
  //  assume that this algorithm should converge to a stable region. It is
  //  possible that by letting the algorithm run for more iterations the
  //  region will end up engulfing the entire image.
  //
  //  \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetNumberOfIterations()}
  //
  //  Software Guide : EndLatex
 
  const unsigned int iterations = std::stoi(argv[6]);
 
  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetNumberOfIterations(iterations);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The output of this filter is a binary image with zero-value pixels
  //  everywhere except on the extracted region. The intensity value to be
  //  put inside the region is selected with the method
  //  \code{SetReplaceValue()}.
  //
  //  \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetReplaceValue()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetReplaceValue(255);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The initialization of the algorithm requires the user to provide a seed
  //  point. This point should be placed in a \emph{typical} region of the
  //  anatomical structure to be segmented. A small neighborhood around the
  //  seed point will be used to compute the initial mean and standard
  //  deviation for the inclusion criterion. The seed is passed in the form
  //  of an \doxygen{Index} to the \code{SetSeed()} method.
  //
  //  \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetSeed()}
  //  \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetInitialNeighborhoodRadius()}
  //
  //  Software Guide : EndLatex
 
  InputImageType::IndexType index;
 
  index[0] = std::stoi(argv[3]);
  index[1] = std::stoi(argv[4]);
 
 
  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetSeed(index);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The size of the initial neighborhood around the seed is defined with the
  //  method \code{SetInitialNeighborhoodRadius()}. The neighborhood will be
  //  defined as an $N$-Dimensional rectangular region with $2r+1$ pixels on
  //  the side, where $r$ is the value passed as initial neighborhood radius.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetInitialNeighborhoodRadius(3);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The invocation of the \code{Update()} method on the writer triggers the
  //  execution of the pipeline.  It is usually wise to put update calls in a
  //  \code{try/catch} block in case errors occur and exceptions are thrown.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & excep)
  {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
  }
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Now let's run this example using as input the image
  //  \code{VisibleWomanEyeSlice.png} provided in the directory
  //  \code{Examples/Data}. We can easily segment the major anatomical
  //  structures by providing seeds in the appropriate locations. For example,
  //
  //  \begin{center}
  //  \begin{tabular}{|l|c|c|c|c|}
  //  \hline
  //  Structure & Seed Index & Multiplier & Iterations
  //  & Output Image \\ \hline
  //  Rectum & $(70,120)$ & 7 & 1 & Second from left in Figure
  //  \ref{fig:VectorConfidenceConnectedOutput} \\ \hline Rectum & $(23, 93)$
  //  & 7 & 1 & Third  from left in Figure
  //  \ref{fig:VectorConfidenceConnectedOutput} \\ \hline Vitreo & $(66, 66)$
  //  & 3 & 1 & Fourth from left in Figure
  //  \ref{fig:VectorConfidenceConnectedOutput} \\ \hline \end{tabular}
  //  \end{center}
  //
  //  \begin{figure} \center
  //  \includegraphics[width=0.24\textwidth]{VisibleWomanEyeSlice}
  //  \includegraphics[width=0.24\textwidth]{VectorConfidenceConnectedOutput1}
  //  \includegraphics[width=0.24\textwidth]{VectorConfidenceConnectedOutput2}
  //  \includegraphics[width=0.24\textwidth]{VectorConfidenceConnectedOutput3}
  //  \itkcaption[VectorConfidenceConnected segmentation results]{Segmentation
  //  results of the VectorConfidenceConnected filter for various seed
  //  points.} \label{fig:VectorConfidenceConnectedOutput} \end{figure}
  //
  //  The coloration of muscular tissue makes it easy to distinguish them from
  //  the surrounding anatomical structures. The optic vitrea on the other
  //  hand has a coloration that is not very homogeneous inside the eyeball
  //  and does not facilitate a full segmentation based only on color.
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginLatex
  //
  //  The values of the final mean vector and covariance matrix used for the
  //  last iteration can be queried using the methods \code{GetMean()} and
  //  \code{GetCovariance()}.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using MeanVectorType = ConnectedFilterType::MeanVectorType;
  using CovarianceMatrixType = ConnectedFilterType::CovarianceMatrixType;
 
  const MeanVectorType &       mean = confidenceConnected->GetMean();
  const CovarianceMatrixType & covariance =
    confidenceConnected->GetCovariance();
 
  std::cout << "Mean vector = " << mean << std::endl;
  std::cout << "Covariance matrix = " << covariance << std::endl;
  // Software Guide : EndCodeSnippet
 
  return EXIT_SUCCESS;
}