ITK  4.6.0
Insight Segmentation and Registration Toolkit
Filtering/SigmoidImageFilter.cxx
/*=========================================================================
*
* 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
* limitations under the License.
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*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {SigmoidImageFilterOutput.png}
// ARGUMENTS: 10 240 10 170
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{SigmoidImageFilter} is commonly used as an intensity
// transform. It maps a specific range of intensity values into a new
// intensity range by making a very smooth and continuous transition in the
// borders of the range. Sigmoids are widely used as a mechanism for focusing
// attention on a particular set of values and progressively attenuating the
// values outside that range. In order to extend the flexibility of the
// Sigmoid filter, its implementation in ITK includes four parameters that can
// be tuned to select its input and output intensity ranges. The following
// equation represents the Sigmoid intensity transformation, applied
// pixel-wise.
//
// \begin{equation}
// I' = (Max-Min)\cdot \frac{1}{\left(1+e^{-\left(\frac{ I - \beta }{\alpha } \right)} \right)} + Min
// \end{equation}
//
// In the equation above, $I$ is the intensity of the input pixel, $I'$ the
// intensity of the output pixel, $Min,Max$ are the minimum and maximum values
// of the output image, $\alpha$ defines the width of the input intensity
// range, and $\beta$ defines the intensity around which the range is
// centered. Figure~\ref{fig:SigmoidParameters} illustrates the significance
// of each parameter.
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{SigmoidParameterAlpha}
// \includegraphics[width=0.44\textwidth]{SigmoidParameterBeta}
// \itkcaption[Sigmoid Parameters]{Effects of the various parameters in the
// SigmoidImageFilter. The alpha parameter defines the width of the intensity
// window. The beta parameter defines the center of the intensity window.}
// \label{fig:SigmoidParameters} \end{figure}
//
// This filter will work on images of any dimension and will take advantage of
// multiple processors when available.
//
// \index{itk::SigmoidImageFilter }
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The header file corresponding to this filter should be included first.
//
// \index{itk::SigmoidImageFilter!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 << " OutputMin OutputMax SigmoidAlpha SigmoidBeta" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Then pixel and image types for the filter input and output must be
// defined.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef unsigned char InputPixelType;
typedef unsigned char OutputPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName( argv[1] );
writer->SetFileName( argv[2] );
// Software Guide : BeginLatex
//
// Using the image types, we instantiate the filter type
// and create the filter object.
//
// \index{itk::SigmoidImageFilter!instantiation}
// \index{itk::SigmoidImageFilter!New()}
// \index{itk::SigmoidImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType, OutputImageType > SigmoidFilterType;
SigmoidFilterType::Pointer sigmoidFilter = SigmoidFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The minimum and maximum values desired in the output are defined using the
// methods \code{SetOutputMinimum()} and \code{SetOutputMaximum()}.
//
// \index{itk::SigmoidImageFilter!SetOutputMaximum()}
// \index{itk::SigmoidImageFilter!SetOutputMinimum()}
//
// Software Guide : EndLatex
const OutputPixelType outputMinimum = atoi( argv[3] );
const OutputPixelType outputMaximum = atoi( argv[4] );
// Software Guide : BeginCodeSnippet
sigmoidFilter->SetOutputMinimum( outputMinimum );
sigmoidFilter->SetOutputMaximum( outputMaximum );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The coefficients $\alpha$ and $\beta$ are set with the methods
// \code{SetAlpha()} and \code{SetBeta()}. Note that $\alpha$ is
// proportional to the width of the input intensity window. As rule of
// thumb, we may say that the window is the interval $[-3\alpha, 3\alpha]$.
// The boundaries of the intensity window are not sharp. The $\alpha$ curve
// approaches its extrema smoothly, as shown in
// Figure~\ref{fig:SigmoidParameters}. You may want to think about this in
// the same terms as when taking a range in a population of measures by
// defining an interval of $[-3 \sigma, +3 \sigma]$ around the population
// mean.
//
// \index{itk::SigmoidImageFilter!SetAlpha()}
// \index{itk::SigmoidImageFilter!SetBeta()}
//
// Software Guide : EndLatex
const double alpha = atof( argv[5] );
const double beta = atof( argv[6] );
// Software Guide : BeginCodeSnippet
sigmoidFilter->SetAlpha( alpha );
sigmoidFilter->SetBeta( beta );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input to the SigmoidImageFilter can be taken from any other filter,
// such as an image file reader, for example. The output can be passed down the
// pipeline to other filters, like an image file writer. An update call on
// any downstream filter will trigger the execution of the Sigmoid filter.
//
// \index{itk::SigmoidImageFilter!SetInput()}
// \index{itk::SigmoidImageFilter!GetOutput()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
sigmoidFilter->SetInput( reader->GetOutput() );
writer->SetInput( sigmoidFilter->GetOutput() );
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{SigmoidImageFilterOutput}
// \itkcaption[Effect of the Sigmoid filter.]{Effect of the Sigmoid filter on a
// slice from a MRI proton density brain image.}
// \label{fig:SigmoidImageFilterOutput}
// \end{figure}
//
// Figure~\ref{fig:SigmoidImageFilterOutput} illustrates the effect of this
// filter on a slice of MRI brain image using the following parameters.
//
// \begin{itemize}
// \item Minimum = 10
// \item Maximum = 240
// \item $\alpha$ = 10
// \item $\beta$ = 170
// \end{itemize}
//
// As can be seen from the figure, the intensities of the white matter
// were expanded in their dynamic range, while intensity values lower than
// $\beta - 3 \alpha$ and higher than $\beta + 3\alpha$ became progressively
// mapped to the minimum and maximum output values. This is the way in which
// a Sigmoid can be used for performing smooth intensity windowing.
//
// Note that both $\alpha$ and $\beta$ can be positive and negative. A
// negative $\alpha$ will have the effect of \emph{negating} the image. This
// is illustrated on the left side of Figure~\ref{fig:SigmoidParameters}. An
// application of the Sigmoid filter as preprocessing for segmentation is
// presented in Section~\ref{sec:FastMarchingImageFilter}.
//
// Sigmoid curves are common in the natural world. They represent the
// plot of sensitivity to a stimulus. They are also the integral curve of
// the Gaussian and, therefore, appear naturally as the response to signals
// whose distribution is Gaussian.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}