ITK  5.1.0
Insight Toolkit
Examples/Segmentation/IsolatedConnectedImageFilter.cxx
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* Copyright Insight Software Consortium
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* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {IsolatedConnectedImageFilterOutput1.png}
// ARGUMENTS: 61 140 150 63 43
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The following example illustrates the use of the
// \doxygen{IsolatedConnectedImageFilter}. This filter is a close variant of
// the \doxygen{ConnectedThresholdImageFilter}. In this filter two seeds and a
// lower threshold are provided by the user. The filter will grow a region
// connected to the first seed and \textbf{not connected} to the second one. In
// order to do this, the filter finds an intensity value that could be used as
// upper threshold for the first seed. A binary search is used to find the
// value that separates both seeds.
//
// This example closely follows the previous ones. Only the relevant pieces
// of code are highlighted here.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The header of the IsolatedConnectedImageFilter is included below.
//
// \index{itk::Isolated\-Connected\-Image\-Filter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
#include "itkImage.h"
int
main(int argc, char * argv[])
{
if (argc < 7)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage seedX1 seedY1";
std::cerr << " lowerThreshold seedX2 seedY2" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We define the image type using a pixel type and a particular
// dimension.
//
// 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
//
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName(argv[1]);
writer->SetFileName(argv[2]);
using CurvatureFlowImageFilterType =
CurvatureFlowImageFilterType::Pointer smoothing = CurvatureFlowImageFilterType::New();
// Software Guide : BeginLatex
//
// The \code{IsolatedConnectedImageFilter} is instantiated in the lines below.
//
// 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 isolatedConnected = ConnectedFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now it is time to connect the pipeline.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
smoothing->SetInput(reader->GetOutput());
isolatedConnected->SetInput(smoothing->GetOutput());
caster->SetInput(isolatedConnected->GetOutput());
writer->SetInput(caster->GetOutput());
// Software Guide : EndCodeSnippet
smoothing->SetNumberOfIterations(5);
smoothing->SetTimeStep(0.125);
// Software Guide : BeginLatex
//
// The \code{IsolatedConnectedImageFilter} expects the user to specify a
// threshold and two seeds. In this example, we take all of them from the
// command line arguments.
//
// \index{itk::Isolated\-Connected\-Image\-Filter!SetLower()}
// \index{itk::Isolated\-Connected\-Image\-Filter!AddSeed1()}
// \index{itk::Isolated\-Connected\-Image\-Filter!AddSeed2()}
//
// Software Guide : EndLatex
indexSeed1[0] = std::stoi(argv[3]);
indexSeed1[1] = std::stoi(argv[4]);
const InternalPixelType lowerThreshold = std::stod(argv[5]);
indexSeed2[0] = std::stoi(argv[6]);
indexSeed2[1] = std::stoi(argv[7]);
// Software Guide : BeginCodeSnippet
isolatedConnected->SetLower(lowerThreshold);
isolatedConnected->AddSeed1(indexSeed1);
isolatedConnected->AddSeed2(indexSeed2);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As in the \doxygen{ConnectedThresholdImageFilter} we must now specify
// the intensity value to be set on the output pixels and at least one
// seed point to define the initial region.
//
// \index{itk::Isolated\-Connected\-Image\-Filter!SetReplaceValue()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
isolatedConnected->SetReplaceValue(255);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The invocation of the \code{Update()} method on the writer triggers the
// execution of the pipeline.
//
// 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
//
// The intensity value allowing us to separate both regions can be
// recovered with the method \code{GetIsolatedValue()}.
//
// \index{itk::Isolated\-Connected\-Image\-Filter!GetIsolatedValue()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
std::cout << "Isolated Value Found = ";
std::cout << isolatedConnected->GetIsolatedValue() << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Let's now run this example using the image
// \code{BrainProtonDensitySlice.png} provided in the directory
// \code{Examples/Data}. We can easily segment the major anatomical
// structures by providing seed pairs in the appropriate locations and
// defining values for the lower threshold. It is important to keep in
// mind in this and the previous examples that the segmentation is being
// performed using the smoothed version of the image. The selection of
// threshold values should therefore be performed in the smoothed image
// since the distribution of intensities could be quite different from
// that of the input image. As a reminder of this fact, Figure
// \ref{fig:IsolatedConnectedImageFilterOutput} presents, from left to
// right, the input image and the result of smoothing with the
// \doxygen{CurvatureFlowImageFilter} followed by segmentation results.
//
// This filter is intended to be used in cases where adjacent anatomical
// structures are difficult to separate. Selecting one seed in one structure
// and the other seed in the adjacent structure creates the appropriate
// setup for computing the threshold that will separate both structures.
// Table~\ref{tab:IsolatedConnectedImageFilterOutput} presents the
// parameters used to obtain the images shown in
// Figure~\ref{fig:IsolatedConnectedImageFilterOutput}.
//
// \begin{table}
// \begin{center}
// \begin{tabular}{|l|c|c|c|c|}
// \hline
// Adjacent Structures & Seed1 & Seed2 & Lower & Isolated value found \\ \hline
// Gray matter vs White matter & $(61,140)$ & $(63,43)$ & $150$ & $183.31$ \\ \hline
// \end{tabular}
// \end{center}
// \itkcaption[IsolatedConnectedImageFilter example parameters]{Parameters
// used for separating white matter from gray matter in
// Figure~\ref{fig:IsolatedConnectedImageFilterOutput} using the
// IsolatedConnectedImageFilter.\label{tab:IsolatedConnectedImageFilterOutput}}
// \end{table}
//
// \begin{figure} \center
// \includegraphics[width=0.32\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.32\textwidth]{IsolatedConnectedImageFilterOutput0}
// \includegraphics[width=0.32\textwidth]{IsolatedConnectedImageFilterOutput1}
// \itkcaption[IsolatedConnected segmentation results]{Segmentation results of
// the IsolatedConnectedImageFilter.}
// \label{fig:IsolatedConnectedImageFilterOutput}
// \end{figure}
//
//
// 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
itkImageFileReader.h
itkImage.h
itkCastImageFilter.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkCurvatureFlowImageFilter.h
itkImageFileWriter.h
itkIsolatedConnectedImageFilter.h
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::IsolatedConnectedImageFilter
Label pixels that are connected to one set of seeds but not another.
Definition: itkIsolatedConnectedImageFilter.h:71
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44