ITK  4.9.0
Insight Segmentation and Registration Toolkit
Examples/Filtering/MathematicalMorphologyGrayscaleFilters.cxx
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*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
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*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {MathematicalMorphologyGrayscaleErosionOutput.png}
// OUTPUTS: {MathematicalMorphologyGrayscaleDilationOutput.png}
// ARGUMENTS: 150 180
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The following section illustrates the use of filters for performing basic
// mathematical morphology operations on grayscale images. The
// \doxygen{GrayscaleErodeImageFilter} and
// \doxygen{GrayscaleDilateImageFilter} are covered in this example. The
// filter names clearly specify the type of image on which they operate.
// The header files required for a simple example of the use of
// grayscale mathematical morphology filters are presented below.
//
// \index{itk::GrayscaleDilateImageFilter!header}
// \index{itk::GrayscaleErodeImageFilter!header}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main( int argc, char * argv[] )
{
if( argc < 4 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile ";
std::cerr << " outputImageFileErosion outputImageFileDilation" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// The following code defines the input and output pixel types and their
// associated image types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
typedef unsigned char InputPixelType;
typedef unsigned char OutputPixelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Mathematical morphology operations are based on the application of an
// operator over a neighborhood of each input pixel. The combination of
// the rule and the neighborhood is known as \emph{structuring
// element}. Although some rules have become the de facto standard in image
// processing there is a good deal of freedom as to what kind of
// algorithmic rule should be applied on the neighborhood. The
// implementation in ITK follows the typical rule of minimum for
// erosion and maximum for dilation.
//
// The structuring element is implemented as a
// \doxygen{NeighborhoodOperator}. In particular, the default structuring
// element is the \doxygen{BinaryBallStructuringElement} class. This class
// is instantiated using the pixel type and dimension of the input image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputPixelType,
Dimension > StructuringElementType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The structuring element type is then used along with the input and output
// image types for instantiating the type of the filters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType,
OutputImageType,
StructuringElementType > ErodeFilterType;
InputImageType,
OutputImageType,
StructuringElementType > DilateFilterType;
// Software Guide : EndCodeSnippet
// Creation of Reader and Writer filters
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writerDilation = WriterType::New();
WriterType::Pointer writerErosion = WriterType::New();
// Software Guide : BeginLatex
//
// The filters can now be created by invoking the \code{New()} method and
// assigning the result to SmartPointers.
//
// \index{itk::GrayscaleDilateImageFilter!New()}
// \index{itk::GrayscaleErodeImageFilter!New()}
// \index{itk::GrayscaleDilateImageFilter!Pointer}
// \index{itk::GrayscaleErodeImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ErodeFilterType::Pointer grayscaleErode = ErodeFilterType::New();
DilateFilterType::Pointer grayscaleDilate = DilateFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The structuring element is not a reference counted class. Thus it is
// created as a C++ stack object instead of using \code{New()} and
// SmartPointers. The radius of the neighborhood associated with the
// structuring element is defined with the \code{SetRadius()} method and the
// \code{CreateStructuringElement()} method is invoked in order to initialize the
// operator. The resulting structuring element is passed to the
// mathematical morphology filter through the \code{SetKernel()} method, as
// illustrated below.
//
// \index{itk::BinaryBallStructuringElement!SetRadius()}
// \index{itk::BinaryBallStructuringElement!CreateStructuringElement()}
// \index{itk::GrayscaleDilateImageFilter!SetKernel()}
// \index{itk::GrayscaleErodeImageFilter!SetKernel()}
// \index{SetRadius()!itk::BinaryBallStructuringElement}
// \index{SetKernel()!itk::GrayscaleDilateImageFilter}
// \index{SetKernel()!itk::GrayscaleErodeImageFilter}
// \index{SetRadius()!itk::BinaryBallStructuringElement}
// \index{CreateStructuringElement()!itk::BinaryBallStructuringElement}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
StructuringElementType structuringElement;
structuringElement.SetRadius( 1 ); // 3x3 structuring element
structuringElement.CreateStructuringElement();
grayscaleErode->SetKernel( structuringElement );
grayscaleDilate->SetKernel( structuringElement );
// Software Guide : EndCodeSnippet
reader->SetFileName( argv[1] );
writerErosion->SetFileName( argv[2] );
writerDilation->SetFileName( argv[3] );
// Software Guide : BeginLatex
//
// A grayscale image is provided as input to the filters. This image might be,
// for example, the output of a reader.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
grayscaleErode->SetInput( reader->GetOutput() );
grayscaleDilate->SetInput( reader->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter is executed by invoking its \code{Update()} method, or by
// updating any downstream filter, such as an image writer.
//
// \index{itk::GrayscaleDilateImageFilter!Update()}
// \index{itk::GrayscaleErodeImageFilter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
writerDilation->SetInput( grayscaleDilate->GetOutput() );
writerDilation->Update();
// Software Guide : EndCodeSnippet
writerErosion->SetInput( grayscaleErode->GetOutput() );
writerErosion->Update();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.32\textwidth]{MathematicalMorphologyGrayscaleErosionOutput}
// \includegraphics[width=0.32\textwidth]{MathematicalMorphologyGrayscaleDilationOutput}
// \itkcaption[Effect of erosion and dilation in a grayscale image.]{Effect of
// erosion and dilation in a grayscale image.}
// \label{fig:MathematicalMorphologyGrayscaleFilters}
// \end{figure}
//
// Figure \ref{fig:MathematicalMorphologyGrayscaleFilters} illustrates the
// effect of the erosion and dilation filters on a binary image from a MRI
// brain slice. The figure shows how these operations can be used to remove
// spurious details from segmented images.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}