ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
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// Software Guide : BeginLatex
//
// The following example illustrates the use of the
// \doxygen{NeighborhoodConnectedImageFilter}. This filter is a close variant
// of the \doxygen{ConnectedThresholdImageFilter}. On one hand, the
// ConnectedThresholdImageFilter accepts a pixel in the region if its intensity
// is in the interval defined by two user-provided threshold values. The
// NeighborhoodConnectedImageFilter, on the other hand, will only accept a
// pixel if \textbf{all} its neighbors have intensities that fit in the
// interval. The size of the neighborhood to be considered around each pixel is
// defined by a user-provided integer radius.
//
// The reason for considering the neighborhood intensities instead of only the
// current pixel intensity is that small structures are less likely to be
// accepted in the region. The operation of this filter is equivalent to
// applying the ConnectedThresholdImageFilter followed by mathematical
// morphology erosion using a structuring element of the same shape as
// the neighborhood provided to the NeighborhoodConnectedImageFilter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The \doxygen{CurvatureFlowImageFilter} is used here to smooth the image
// while preserving edges.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main( int argc, char *argv[] )
{
if( argc < 7 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage seedX seedY lowerThreshold upperThreshold" << std::endl;
return 1;
}
// Software Guide : BeginLatex
//
// We now define the image type using a particular pixel type and image
// 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
typedef float InternalPixelType;
const unsigned int Dimension = 2;
typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
// Software Guide : EndCodeSnippet
typedef unsigned char OutputPixelType;
CastingFilterType;
CastingFilterType::Pointer caster = CastingFilterType::New();
// We instantiate reader and writer types
//
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[2] );
// Software Guide : BeginLatex
//
// The smoothing filter type is instantiated using the image type as
// a template parameter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
CurvatureFlowImageFilterType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then, the filter is created by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
CurvatureFlowImageFilterType::Pointer smoothing =
CurvatureFlowImageFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We now declare the type of the region growing filter. In this case it is
// the NeighborhoodConnectedImageFilter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::NeighborhoodConnectedImageFilter<InternalImageType,
InternalImageType > ConnectedFilterType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// One filter of this class is constructed using the \code{New()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ConnectedFilterType::Pointer neighborhoodConnected
= ConnectedFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now it is time to create a simple, linear data processing pipeline. A
// file reader is added at the beginning of the pipeline and a cast
// filter and writer are added at the end. The cast filter is required
// to convert \code{float} pixel types to integer types since only a
// few image file formats support \code{float} types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
neighborhoodConnected->SetInput( smoothing->GetOutput() );
caster->SetInput( neighborhoodConnected->GetOutput() );
writer->SetInput( caster->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The CurvatureFlowImageFilter requires a couple of parameters.
// The following are typical values for $2D$ images. However
// they may have to be adjusted depending on the amount of noise present in
// the input image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
smoothing->SetNumberOfIterations( 5 );
smoothing->SetTimeStep( 0.125 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The NeighborhoodConnectedImageFilter requires that two main parameters
// are specified. They are the lower and upper thresholds of the interval
// in which intensity values must fall to be included in the
// region. Setting these two values too close will not allow enough
// flexibility for the region to grow. Setting them too far apart will
// result in a region that engulfs the image.
//
// \index{itk::NeighborhoodConnectedImageFilter!SetLower()}
// \index{itk::NeighborhoodConnectedImageFilter!SetUppder()}
//
// Software Guide : EndLatex
const InternalPixelType lowerThreshold = atof( argv[5] );
const InternalPixelType upperThreshold = atof( argv[6] );
// Software Guide : BeginCodeSnippet
neighborhoodConnected->SetLower( lowerThreshold );
neighborhoodConnected->SetUpper( upperThreshold );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Here, we add the crucial parameter that defines the neighborhood size
// used to determine whether a pixel lies in the region. The larger the
// neighborhood, the more stable this filter will be against noise in the
// input image, but also the longer the computing time will be. Here we
// select a filter of radius $2$ along each dimension. This results in a
// neighborhood of $5 \times 5$ pixels.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
radius[0] = 2; // two pixels along X
radius[1] = 2; // two pixels along Y
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As in the ConnectedThresholdImageFilter we must now provide the
// intensity value to be used for the output pixels accepted in the region
// and at least one seed point to define the initial region.
//
// \index{itk::NeighborhoodConnectedImageFilter!SetSeed()}
// \index{itk::NeighborhoodConnectedImageFilter!SetReplaceValue()}
//
// Software Guide : EndLatex
InternalImageType::IndexType index;
index[0] = atoi( argv[3] );
index[1] = atoi( argv[4] );
// Software Guide : BeginCodeSnippet
neighborhoodConnected->SetSeed( index );
neighborhoodConnected->SetReplaceValue( 255 );
// 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( itk::ExceptionObject & excep )
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we'll run this example using the image
// \code{BrainProtonDensitySlice.png} as input available from the
// directory \code{Examples/Data}. We can easily segment the major
// anatomical structures by providing seeds in the appropriate locations
// and defining values for the lower and upper thresholds. For example
//
// \begin{center}
// \begin{tabular}{|l|c|c|c|c|}
// \hline
// Structure & Seed Index & Lower & Upper & Output Image \\ \hline
// White matter & $(60,116)$ & 150 & 180 & Second from left in Figure \ref{fig:NeighborhoodConnectedImageFilterOutput} \\ \hline
// Ventricle & $(81,112)$ & 210 & 250 & Third from left in Figure \ref{fig:NeighborhoodConnectedImageFilterOutput} \\ \hline
// Gray matter & $(107,69)$ & 180 & 210 & Fourth from left in Figure \ref{fig:NeighborhoodConnectedImageFilterOutput} \\ \hline
// \end{tabular}
// \end{center}
//
// \begin{figure} \center
// \includegraphics[width=0.24\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.24\textwidth]{NeighborhoodConnectedImageFilterOutput1}
// \includegraphics[width=0.24\textwidth]{NeighborhoodConnectedImageFilterOutput2}
// \includegraphics[width=0.24\textwidth]{NeighborhoodConnectedImageFilterOutput3}
// \itkcaption[NeighborhoodConnected segmentation results ]{Segmentation results
// of the NeighborhoodConnectedImageFilter for various seed points.}
// \label{fig:NeighborhoodConnectedImageFilterOutput}
// \end{figure}
//
// As with the ConnectedThresholdImageFilter, several seeds could
// be provided to the filter by using the \code{AddSeed()} method.
// Compare the output of Figure
// \ref{fig:NeighborhoodConnectedImageFilterOutput} with those of Figure
// \ref{fig:ConnectedThresholdOutput} produced by the
// ConnectedThresholdImageFilter. You may want to play with the
// value of the neighborhood radius and see how it affect the smoothness of
// the segmented object borders, the size of the segmented region and how
// much that costs in computing time.
//
// Software Guide : EndLatex
return 0;
}