ITK  5.2.0
Insight Toolkit
Examples/Filtering/LaplacianRecursiveGaussianImageFilter1.cxx
/*=========================================================================
*
* 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
*
* 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.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// ARGUMENTS: LaplacianRecursiveGaussianImageFilterOutput3.mha 3
// OUTPUTS: {LaplacianRecursiveGaussianImageFilterOutput3.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// ARGUMENTS: LaplacianRecursiveGaussianImageFilterOutput5.mha 5
// OUTPUTS: {LaplacianRecursiveGaussianImageFilterOutput5.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates how to use the
// \doxygen{RecursiveGaussianImageFilter} for computing the Laplacian of a 2D
// image.
//
// \index{itk::RecursiveGaussianImageFilter}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::RecursiveGaussianImageFilter!header}
//
// Software Guide : EndLatex
// 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 outputImageFile sigma [RescaledOutputImageFile] "
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be selected on the desired input and output pixel types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input and output image types are instantiated using the pixel types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputImageType = itk::Image<InputPixelType, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types.
//
// \index{itk::RecursiveGaussianImageFilter!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
// Software Guide : BeginLatex
//
// This filter applies the approximation of the convolution along a single
// dimension. It is therefore necessary to concatenate several of these
// filters to produce smoothing in all directions. In this example, we
// create a pair of filters since we are processing a $2D$ image. The
// filters are created by invoking the \code{New()} method and assigning
// the result to a \doxygen{SmartPointer}.
//
// We need two filters for computing the X component of the Laplacian and
// two other filters for computing the Y component.
//
// \index{itk::RecursiveGaussianImageFilter!New()}
// \index{itk::RecursiveGaussianImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer filterX1 = FilterType::New();
FilterType::Pointer filterY1 = FilterType::New();
FilterType::Pointer filterX2 = FilterType::New();
FilterType::Pointer filterY2 = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Since each one of the newly created filters has the potential to perform
// filtering along any dimension, we have to restrict each one to a
// particular direction. This is done with the \code{SetDirection()}
// method.
//
// \index{RecursiveGaussianImageFilter!SetDirection()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filterX1->SetDirection(0); // 0 --> X direction
filterY1->SetDirection(1); // 1 --> Y direction
filterX2->SetDirection(0); // 0 --> X direction
filterY2->SetDirection(1); // 1 --> Y direction
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \doxygen{RecursiveGaussianImageFilter} can approximate the
// convolution with the Gaussian or with its first and second
// derivatives. We select one of these options by using the
// \code{SetOrder()} method. Note that the argument is an \code{enum} whose
// values can be \code{ZeroOrder}, \code{FirstOrder} and
// \code{SecondOrder}. For example, to compute the $x$ partial derivative
// we should select \code{FirstOrder} for $x$ and \code{ZeroOrder} for $y$.
// Here we want only to smooth in $x$ and $y$, so we select
// \code{ZeroOrder} in both directions.
//
// \index{RecursiveGaussianImageFilter!SetOrder()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filterX1->SetOrder(itk::GaussianOrderEnum::ZeroOrder);
filterY2->SetOrder(itk::GaussianOrderEnum::ZeroOrder);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// There are two typical ways of normalizing Gaussians depending on their
// application. For scale-space analysis it is desirable to use a
// normalization that will preserve the maximum value of the input. This
// normalization is represented by the following equation.
//
// \begin{equation}
// \frac{ 1 }{ \sigma \sqrt{ 2 \pi } }
// \end{equation}
//
// In applications that use the Gaussian as a solution of the diffusion
// equation it is desirable to use a normalization that preserves the
// integral of the signal. This last approach can be seen as a conservation
// of mass principle. This is represented by the following equation.
//
// \begin{equation}
// \frac{ 1 }{ \sigma^2 \sqrt{ 2 \pi } }
// \end{equation}
//
// The \doxygen{RecursiveGaussianImageFilter} has a boolean flag that
// allows users to select between these two normalization options.
// Selection is done with the method \code{SetNormalizeAcrossScale()}.
// Enable this flag when analyzing an image across scale-space. In the
// current example, this setting has no impact because we are actually
// renormalizing the output to the dynamic range of the reader, so we
// simply disable the flag.
//
// \index{RecursiveGaussianImageFilter!SetNormalizeAcrossScale()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const bool normalizeAcrossScale = false;
filterX1->SetNormalizeAcrossScale(normalizeAcrossScale);
filterY1->SetNormalizeAcrossScale(normalizeAcrossScale);
filterX2->SetNormalizeAcrossScale(normalizeAcrossScale);
filterY2->SetNormalizeAcrossScale(normalizeAcrossScale);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another
// filter. Here, an image reader is used as the source. The image is passed
// to the $x$ filter and then to the $y$ filter. The reason for keeping
// these two filters separate is that it is usual in scale-space
// applications to compute not only the smoothing but also combinations of
// derivatives at different orders and smoothing. Some factorization is
// possible when separate filters are used to generate the intermediate
// results. Here this capability is less interesting, though, since we only
// want to smooth the image in all directions.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filterX1->SetInput(reader->GetOutput());
filterY1->SetInput(filterX1->GetOutput());
filterY2->SetInput(reader->GetOutput());
filterX2->SetInput(filterY2->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// It is now time to select the $\sigma$ of the Gaussian used to smooth the
// data. Note that $\sigma$ must be passed to both filters and that sigma
// is considered to be in millimeters. That is, at the moment of applying
// the smoothing process, the filter will take into account the spacing
// values defined in the image.
//
// \index{itk::RecursiveGaussianImageFilter!SetSigma()}
// \index{SetSigma()!itk::RecursiveGaussianImageFilter}
//
// Software Guide : EndLatex
const double sigma = std::stod(argv[3]);
// Software Guide : BeginCodeSnippet
filterX1->SetSigma(sigma);
filterY1->SetSigma(sigma);
filterX2->SetSigma(sigma);
filterY2->SetSigma(sigma);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally the two components of the Laplacian should be added together.
// The \doxygen{AddImageFilter} is used for this purpose.
//
// \index{itk::AddImageFilter!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using AddFilterType =
AddFilterType::Pointer addFilter = AddFilterType::New();
addFilter->SetInput1(filterY1->GetOutput());
addFilter->SetInput2(filterX2->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filters are triggered by invoking \code{Update()} on the Add filter
// at the end of the pipeline.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
addFilter->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The resulting image could be saved to a file using the
// \doxygen{ImageFileWriter} class.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using WritePixelType = float;
using WriteImageType = itk::Image<WritePixelType, 2>;
WriterType::Pointer writer = WriterType::New();
writer->SetInput(addFilter->GetOutput());
writer->SetFileName(argv[2]);
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilterOutput3}
// \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilterOutput5}
// \itkcaption[Output of the LaplacianRecursiveGaussianImageFilter.]{Effect
// of the LaplacianRecursiveGaussianImageFilter on a slice from a MRI proton
// density image of the brain.}
// \label{fig:LaplacianRecursiveGaussianImageFilterInputOutput}
// \end{figure}
//
// Software Guide : EndLatex
// Rescale float outputs to png for inclusion in the Software guide
//
if (argc > 4)
{
using CharPixelType = unsigned char;
using CharImageType = itk::Image<CharPixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescale = RescaleFilterType::New();
rescale->SetInput(addFilter->GetOutput());
rescale->SetOutputMinimum(0);
rescale->SetOutputMaximum(255);
using CharWriterType = itk::ImageFileWriter<CharImageType>;
CharWriterType::Pointer charWriter = CharWriterType::New();
charWriter->SetFileName(argv[4]);
charWriter->SetInput(rescale->GetOutput());
charWriter->Update();
}
return EXIT_SUCCESS;
}
itkRecursiveGaussianImageFilter.h
itkImageFileReader.h
itkImage.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::RecursiveGaussianImageFilterEnums::GaussianOrder::SecondOrder
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itkAddImageFilter.h
itk::RecursiveGaussianImageFilterEnums::GaussianOrder::ZeroOrder
itk::AddImageFilter
Pixel-wise addition of two images.
Definition: itkAddImageFilter.h:80
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
itk::RecursiveGaussianImageFilter
Base class for computing IIR convolution with an approximation of a Gaussian kernel.
Definition: itkRecursiveGaussianImageFilter.h:100