ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/Iterators/NeighborhoodIterators4.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.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4a.png}
// ARGUMENTS: 0
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4b.png}
// ARGUMENTS: 1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4c.png}
// ARGUMENTS: 2
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {NeighborhoodIterators4d.png}
// ARGUMENTS: 5
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// We now introduce a variation on convolution filtering that is useful when a
// convolution kernel is separable. In this example, we create a different
// neighborhood iterator for each axial direction of the image and then take
// separate inner products with a 1D discrete Gaussian kernel.
// The idea of using several neighborhood iterators at once has applications
// beyond convolution filtering and may improve efficiency when the size of
// the whole neighborhood relative to the portion of the neighborhood used
// in calculations becomes large.
//
// The only new class necessary for this example is the Gaussian operator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main( int argc, char ** argv )
{
if ( argc < 4 )
{
std::cerr << "Missing parameters. " << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0]
<< " inputImageFile outputImageFile sigma"
<< std::endl;
return EXIT_FAILURE;
}
using PixelType = float;
using ImageType = itk::Image< PixelType, 2 >;
using NeighborhoodIteratorType = itk::ConstNeighborhoodIterator< ImageType >;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
try
{
reader->Update();
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
ImageType::Pointer output = ImageType::New();
output->SetRegions(reader->GetOutput()->GetRequestedRegion());
output->Allocate();
FaceCalculatorType faceCalculator;
FaceCalculatorType::FaceListType faceList;
FaceCalculatorType::FaceListType::iterator fit;
IteratorType out;
NeighborhoodIteratorType it;
// Software Guide : BeginLatex
//
// The Gaussian operator, like the Sobel operator, is instantiated with a pixel
// type and a dimensionality. Additionally, we set the variance of the
// Gaussian, which has been read from the command line as standard deviation.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
gaussianOperator.SetVariance( ::std::stod(argv[3]) * ::std::stod(argv[3]) );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The only further changes from the previous example are in the main loop.
// Once again we use the results from face calculator to construct a loop that
// processes boundary and non-boundary image regions separately. Separable
// convolution, however, requires an additional, outer loop over all the image
// dimensions. The direction of the Gaussian operator is reset at each
// iteration of the outer loop using the new dimension. The iterators change
// direction to match because they are initialized with the radius of the
// Gaussian operator.
//
// Input and output buffers are swapped at each iteration so that the output of
// the previous iteration becomes the input for the current iteration. The swap
// is not performed on the last iteration.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ImageType::Pointer input = reader->GetOutput();
for (unsigned int i = 0; i < ImageType::ImageDimension; ++i)
{
gaussianOperator.SetDirection(i);
gaussianOperator.CreateDirectional();
faceList = faceCalculator(input, output->GetRequestedRegion(),
gaussianOperator.GetRadius());
for ( fit=faceList.begin(); fit != faceList.end(); ++fit )
{
it = NeighborhoodIteratorType( gaussianOperator.GetRadius(),
input, *fit );
out = IteratorType( output, *fit );
for (it.GoToBegin(), out.GoToBegin(); ! it.IsAtEnd(); ++it, ++out)
{
out.Set( innerProduct(it, gaussianOperator) );
}
}
// Swap the input and output buffers
if (i != ImageType::ImageDimension - 1)
{
ImageType::Pointer tmp = input;
input = output;
output = tmp;
}
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output is rescaled and written as in the previous examples.
// Figure~\ref{fig:NeighborhoodExample4} shows the results of Gaussian blurring
// the image \code{Examples/Data/BrainT1Slice.png} using increasing
// kernel widths.
//
// \begin{figure}
// \centering
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4a}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4b}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4c}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4d}
// \itkcaption[Gaussian blurring by convolution filtering]{Results of
// convolution filtering with a Gaussian kernel of increasing standard
// deviation $\sigma$ (from left to right, $\sigma = 0$, $\sigma = 1$, $\sigma
// = 2$, $\sigma = 5$). Increased blurring reduces contrast and changes the
// average intensity value of the image, which causes the image to appear
// brighter when rescaled.}
// \protect\label{fig:NeighborhoodExample4}
// \end{figure}
//
// Software Guide : EndLatex
using WritePixelType = unsigned char;
using WriteImageType = itk::Image< WritePixelType, 2 >;
using RescaleFilterType = itk::RescaleIntensityImageFilter<
ImageType, WriteImageType >;
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
rescaler->SetInput(output);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[2] );
writer->SetInput( rescaler->GetOutput() );
try
{
writer->Update();
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}