ITK  5.1.0 Insight 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
// \code{ConnectedThresholdImageFilter} considers only the value of the pixel
// itself when determining whether it belongs to the region: if its value is
// within the interval [lowerThreshold,upperThreshold] it is included,
// otherwise it is excluded. \code{NeighborhoodConnectedImageFilter},
// on the other hand, considers a user-defined neighborhood surrounding the
// pixel, requiring that the intensity of \textbf{each} neighbor be within
// the interval for it to be included.
//
// 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 \code{ConnectedThresholdImageFilter} followed by mathematical
// morphology erosion using a structuring element of the same shape as
// the neighborhood provided to the \code{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] << " inputImage outputImage"
<< " seedX seedY"
<< " lowerThreshold upperThreshold" << std::endl;
return EXIT_FAILURE;
}
// 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
using InternalPixelType = float;
constexpr unsigned int Dimension = 2;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
// Software Guide : EndCodeSnippet
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
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
using 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
using 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
//
// \code{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
//
// \code{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 = std::stod(argv[5]);
const InternalPixelType upperThreshold = std::stod(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 \code{ConnectedThresholdImageFilter} example, we must
// provide the intensity value to be used for the output pixels accepted
// in the region and at least one seed point to define the starting point.
//
// \index{itk::NeighborhoodConnectedImageFilter!SetSeed()}
// \index{itk::NeighborhoodConnectedImageFilter!SetReplaceValue()}
//
// Software Guide : EndLatex
index[0] = std::stoi(argv[3]);
index[1] = std::stoi(argv[4]);
// Software Guide : BeginCodeSnippet
neighborhoodConnected->SetSeed(index);
neighborhoodConnected->SetReplaceValue(255);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// 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 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 \code{ConnectedThresholdImageFilter} example, several seeds could
// be provided to the filter by repetedly calling the \code{AddSeed()} method
// with different indices. Compare Figures
// \ref{fig:NeighborhoodConnectedImageFilterOutput} and
// \ref{fig:ConnectedThresholdOutput}, demonstrating the outputs of
// \code{NeighborhoodConnectedThresholdImageFilter} and
// \code{ConnectedThresholdImageFilter}, respectively. It is instructive to adjust
// the neighborhood radii and observe its effect on the smoothness of segmented
// object borders, size of the segmented region, and computing time.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itk::CurvatureFlowImageFilter
Denoise an image using curvature driven flow.
Definition: itkCurvatureFlowImageFilter.h:95
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itkImage.h
itkCastImageFilter.h
Data source that reads image data from a single file.
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itkNeighborhoodConnectedImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkCurvatureFlowImageFilter.h
itkImageFileWriter.h
itk::NeighborhoodConnectedImageFilter
Label pixels that are connected to a seed and lie within a neighborhood.
Definition: itkNeighborhoodConnectedImageFilter.h:36
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44