ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/Iterators/NeighborhoodIterators3.cxx
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// Software Guide : BeginLatex
//
// This example illustrates a technique for improving the efficiency of
// neighborhood calculations by eliminating unnecessary bounds checking. As
// described in Section~\ref{sec:NeighborhoodIterators}, the neighborhood
// iterator automatically enables or disables bounds checking based on the
// iteration region in which it is initialized. By splitting our image into
// boundary and non-boundary regions, and then processing each region using a
// different neighborhood iterator, the algorithm will only perform
// bounds-checking on those pixels for which it is actually required. This
// trick can provide a significant speedup for simple algorithms such as our
// Sobel edge detection, where iteration speed is a critical.
//
// Splitting the image into the necessary regions is an easy task when you use
// the \doxygen{ImageBoundaryFacesCalculator}. The face
// calculator is so named because it returns a list of the faces'' of the ND
// dataset. Faces are those regions whose pixels all lie within a distance $d$
// from the boundary, where $d$ is the radius of the neighborhood stencil used
// for the numerical calculations. In other words, faces are those regions
// where a neighborhood iterator of radius $d$ will always overlap the boundary
// of the image. The face calculator also returns the single \emph{inner}
// region, in which out-of-bounds values are never required and bounds checking
// is not necessary.
//
// The face calculator object is defined in \code{itkNeighborhoodAlgorithm.h}.
// We include this file in addition to those from the previous two examples.
//
// 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 direction"
<< std::endl;
return -1;
}
typedef float PixelType;
typedef itk::Image< PixelType, 2 > ImageType;
typedef itk::ConstNeighborhoodIterator< ImageType > NeighborhoodIteratorType;
try
{
}
catch ( itk::ExceptionObject &err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return -1;
}
ImageType::Pointer output = ImageType::New();
output->Allocate();
sobelOperator.SetDirection( ::atoi(argv[3]) );
sobelOperator.CreateDirectional();
// Software Guide : BeginLatex
//
// First we load the input image and create the output image and inner product
// function as in the previous examples. The image iterators will be created
// in a later step. Next we create a face calculator object. An empty list is
// created to hold the regions that will later on be returned by the face
// calculator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FaceCalculatorType faceCalculator;
FaceCalculatorType::FaceListType faceList;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The face calculator function is invoked by passing it an image pointer, an
// image region, and a neighborhood radius. The image pointer is the same
// image used to initialize the neighborhood iterator, and the image region is
// the region that the algorithm is going to process. The radius is the radius
// of the iterator.
//
// Notice that in this case the image region is given as the region of the
// \emph{output} image and the image pointer is given as that of the
// \emph{input} image. This is important if the input and output images differ
// in size, i.e. the input image is larger than the output image. ITK image
// filters, for example, operate on data from the input image but only generate
// results in the \code{RequestedRegion} of the output image, which may be
// smaller than the full extent of the input.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The face calculator has returned a list of $2N+1$ regions. The first element
// in the list is always the inner region, which may or may not be important
// depending on the application. For our purposes it does not matter because
// all regions are processed the same way. We use an iterator to traverse the
// list of faces.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FaceCalculatorType::FaceListType::iterator fit;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We now rewrite the main loop of the previous example so that each region in the
// list is processed by a separate iterator. The iterators \code{it} and
// \code{out} are reinitialized over each region in turn. Bounds checking is
// automatically enabled for those regions that require it, and disabled for
// the region that does not.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
IteratorType out;
NeighborhoodIteratorType it;
for ( fit=faceList.begin(); fit != faceList.end(); ++fit)
{
out = IteratorType( output, *fit );
for (it.GoToBegin(), out.GoToBegin(); ! it.IsAtEnd(); ++it, ++out)
{
out.Set( innerProduct(it, sobelOperator) );
}
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output is written as before. Results for this example are the same as
// the previous example. You may not notice the speedup except on larger
// images. When moving to 3D and higher dimensions, the effects are greater
// because the volume to surface area ratio is usually larger. In other
// words, as the number of interior pixels increases relative to the number of
// face pixels, there is a corresponding increase in efficiency from disabling
// bounds checking on interior pixels.
//
// Software Guide : EndLatex
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
ImageType, 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::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return -1;
}
return 0;
}