ITK  4.13.0 Insight Segmentation and Registration Toolkit
Examples/Iterators/NeighborhoodIterators5.cxx
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// Software Guide : BeginLatex
//
// This example introduces slice-based neighborhood processing. A slice, in
// this context, is a 1D path through an ND neighborhood. Slices are defined
// for generic arrays by the \code{std::slice} class as a start index, a step
// size, and an end index. Slices simplify the implementation of certain
// neighborhood calculations. They also provide a mechanism for taking inner
// products with subregions of neighborhoods.
//
// Suppose, for example, that we want to take partial derivatives in the $y$
// direction of a neighborhood, but offset those derivatives by one pixel
// position along the positive $x$ direction. For a $3\times3$, 2D
// neighborhood iterator, we can construct an \code{std::slice}, \code{(start =
// 2, stride = 3, end = 8)}, that represents the neighborhood offsets $(1, // -1)$, $(1, 0)$, $(1, 1)$ (see Figure~\ref{fig:NeighborhoodIteratorFig2}). If we
// pass this slice as an extra argument to the
// \doxygen{NeighborhoodInnerProduct} function, then the inner product is taken
// only along that slice. This sliced'' inner product with a 1D
// \doxygen{DerivativeOperator} gives the desired derivative.
//
// The previous separable Gaussian filtering example can be rewritten using
// slices and slice-based inner products. In general, slice-based processing
// is most useful when doing many different calculations on the same
// neighborhood, where defining multiple iterators as in
// Section~\ref{sec:NeighborhoodExample4} becomes impractical or inefficient.
// Good examples of slice-based neighborhood processing can be found in any of
// the ND anisotropic diffusion function objects, such as
// \doxygen{CurvatureNDAnisotropicDiffusionFunction}.
//
// Software Guide : EndLatex
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;
}
typedef float PixelType;
typedef itk::Image< PixelType, 2 > ImageType;
typedef itk::ConstNeighborhoodIterator< ImageType > NeighborhoodIteratorType;
try
{
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
ImageType::Pointer output = ImageType::New();
output->Allocate();
FaceCalculatorType faceCalculator;
FaceCalculatorType::FaceListType faceList;
FaceCalculatorType::FaceListType::iterator fit;
IteratorType out;
NeighborhoodIteratorType it;
// Software Guide: BeginLatex
//
// The first difference between this example and the previous example is that
// the Gaussian operator is only initialized once. Its direction is not
// important because it is only a 1D array of coefficients.
//
// Software Guide: EndLatex
// Software Guide : BeginCodeSnippet
gaussianOperator.SetDirection(0);
gaussianOperator.SetVariance( ::atof(argv[3]) * ::atof(argv[3]) );
gaussianOperator.CreateDirectional();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next we need to define a radius for the iterator. The radius in all
// directions matches that of the single extent of the Gaussian operator,
// defining a square neighborhood.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide EndCodeSnippet
// Software Guide : BeginLatex
//
// The inner product and face calculator are defined for the main processing
// loop as before, but now the iterator is reinitialized each iteration with
// inner product is taken using a slice along the axial direction corresponding
// to the current iteration. Note the use of \code{GetSlice()} to return the
// proper slice from the iterator itself. \code{GetSlice()} can only be used
// to return the slice along the complete extent of the axial direction of a
// neighborhood.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
for (unsigned int i = 0; i < ImageType::ImageDimension; ++i)
{
for ( fit=faceList.begin(); fit != faceList.end(); ++fit )
{
it = NeighborhoodIteratorType( radius, input, *fit );
out = IteratorType( output, *fit );
for (it.GoToBegin(), out.GoToBegin(); ! it.IsAtEnd(); ++it, ++out)
{
out.Set( innerProduct(it.GetSlice(i), 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
//
// This technique produces exactly the same results as the previous example. A
// little experimentation, however, will reveal that it is less efficient since
// the neighborhood iterator is keeping track of extra, unused pixel locations
// for each iteration, while the previous example only references those pixels
// that it needs. In cases, however, where an algorithm takes multiple
// derivatives or convolution products over the same neighborhood, slice-based
// processing can increase efficiency and simplify the implementation.
//
// Software Guide : EndLatex
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
WriteImageType > RescaleFilterType;
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;
}