ITK  5.2.0
Insight Toolkit
Examples/Filtering/BinaryMinMaxCurvatureFlowImageFilter.cxx
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*
* Copyright NumFOCUS
*
* 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
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* 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: {BinaryMinMaxCurvatureFlowImageFilterOutput.png}
// ARGUMENTS: 10 0.125 1 128
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{BinaryMinMaxCurvatureFlowImageFilter} applies a variant of
// the CurvatureFlow algorithm. Which means that the speed of propagation is
// proportional to the curvature $\kappa$ of iso-contours. This filter adds
// however, the restriction that negative curvatures are only accepted in
// regions of the image having low intensities. The user should provide an
// intensity threshold over which negative curvatures are not considered for
// the propagation.
//
// In practice the algorithm do the following for each pixel. First, the
// curvature $\kappa$ is computed on the current pixel. If the computed
// curvature is null this is returned as value. Otherwise, an average of
// neighbor pixel intensities is computed and it is compared against a
// user-provided threshold. If this average is less than the threshold then
// the algorithm returns $\min(\kappa,0)$. If the average intensity is
// greater or equal than user-provided threshold, then the returned value is
// $\max(\kappa,0)$.
//
// \begin{equation}
// I_t = F |\nabla I|
// \end{equation}
//
// where $F$ is defined as
//
// \begin{equation}
// F = \left\{ \begin{array} {r@{\quad:\quad}l} \min(\kappa,0) &
// \mbox{Average} < \mbox{Threshold} \\ \max(\kappa,0) & \mbox{Average} \ge
// \mbox{Threshold} \end{array} \right.
// \end{equation}
//
// \index{itk::Binary\-MinMax\-Curvature\-Flow\-Image\-Filter}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required for using this filter is to include its header
// file
//
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 7)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile ";
std::cerr << "numberOfIterations timeStep stencilRadius threshold"
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be chosen for the pixels of the input and output images and
// with them the image types are instantiated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
using InputImageType = itk::Image<InputPixelType, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The BinaryMinMaxCurvatureFlowFilter type is now instantiated using both
// the input image and the output image types. The filter is then created
// using the \code{New()} method.
//
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!instantiation}
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!New()}
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
OutputImageType>;
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as source.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput(reader->GetOutput());
// Software Guide : EndCodeSnippet
const unsigned int numberOfIterations = std::stoi(argv[3]);
const double timeStep = std::stod(argv[4]);
using RadiusType = FilterType::RadiusValueType;
const RadiusType radius = atol(argv[5]);
const double threshold = std::stod(argv[6]);
// Software Guide : BeginLatex
//
// The \doxygen{BinaryMinMaxCurvatureFlowImageFilter} requires the same
// parameters of the MinMaxCurvatureFlowImageFilter plus the value of the
// threshold against which the neighborhood average will be compared. The
// threshold is passed using the \code{SetThreshold()} method. Then the
// filter can be executed by invoking \code{Update()}.
//
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!Update()}
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetTimeStep()}
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetNumberOfIterations()}
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetStencilRadius()}
// \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetThreshold()}
// \index{SetTimeStep()!itk::BinaryMinMaxCurvatureFlowImageFilter}
// \index{SetStencilRadius()!itk::BinaryMinMaxCurvatureFlowImageFilter}
// \index{SetThreshold()!itk::BinaryMinMaxCurvatureFlowImageFilter}
// \index{SetNumberOfIterations()!itk::BinaryMinMaxCurvatureFlowImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetTimeStep(timeStep);
filter->SetNumberOfIterations(numberOfIterations);
filter->SetStencilRadius(radius);
filter->SetThreshold(threshold);
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Typical values for the time step are $0.125$ in $2D$ images and $0.0625$
// in $3D$ images. The number of iterations can be usually around $10$,
// more iterations will result in further smoothing and will increase
// linearly the computing time. The radius of the stencil can be typically
// $1$. The value of the threshold should be selected according to the gray
// levels of the object of interest and the gray level of its background.
//
// Software Guide : EndLatex
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{BinaryMinMaxCurvatureFlowImageFilterOutput}
// \itkcaption[BinaryMinMaxCurvatureFlowImageFilter output]{Effect of the
// BinaryMinMaxCurvatureFlowImageFilter on a slice from a MRI proton density
// image of the brain.}
// \label{fig:BinaryMinMaxCurvatureFlowImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:BinaryMinMaxCurvatureFlowImageFilterInputOutput}
// illustrates the effect of this filter on a MRI proton density image of
// the brain. In this example the filter was run with a time step of
// $0.125$, $10$ iterations, a stencil radius of $1$ and a threshold of
// $128$.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkImageFileReader.h
itkBinaryMinMaxCurvatureFlowImageFilter.h
itkImage.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::BinaryMinMaxCurvatureFlowImageFilter
Denoise a binary image using min/max curvature flow.
Definition: itkBinaryMinMaxCurvatureFlowImageFilter.h:81
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86