ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/Filtering/VotingBinaryIterativeHoleFillingImageFilter.cxx
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// 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"
// Software Guide : BeginLatex
//
// The header file corresponding to this filter should be included first.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// 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
typedef unsigned char PixelType;
typedef itk::Image< PixelType, 2 > ImageType;
// Software Guide : EndCodeSnippet
WriterType::Pointer writer = WriterType::New();
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
ImageType > FilterType;
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!Neighborhood}
//
// Software Guide : EndLatex
const unsigned int radiusX = atoi( argv[3] );
const unsigned int radiusY = atoi( argv[4] );
// Software Guide : BeginCodeSnippet
// 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 = atoi( 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
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;
}