ITK  4.13.0 Insight Segmentation and Registration Toolkit
/*=========================================================================
*
*
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// Software Guide : EndCommandLineArgs
//
// Software Guide : BeginLatex
//
// The magnitude of the image gradient is extensively used in image analysis,
// mainly to help in the determination of object contours and the
// separation of homogeneous regions. The
// \doxygen{GradientMagnitudeImageFilter} computes the magnitude of the
// image gradient at each pixel location using a simple finite differences
// approach. For example, in the case of $2D$ the computation is equivalent
// to convolving the image with masks of type
//
// \begin{center}
// \begin{picture}(200,50)
// \put( 5.0,32.0){\framebox(30.0,15.0){-1}}
// \put(35.0,32.0){\framebox(30.0,15.0){0}}
// \put(65.0,32.0){\framebox(30.0,15.0){1}}
// \put(105.0,17.0){\framebox(20.0,15.0){1}}
// \put(105.0,32.0){\framebox(20.0,15.0){0}}
// \put(105.0,47.0){\framebox(20.0,15.0){-1}}
// \end{picture}
// \end{center}
//
// then adding the sum of their squares and computing the square root of the sum.
//
// This filter will work on images of any dimension thanks to the internal
// use of \doxygen{NeighborhoodIterator} and \doxygen{NeighborhoodOperator}.
//
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main( int argc, char * argv[] )
{
if( argc < 3 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be chosen for the pixels of the input and output images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef float InputPixelType;
typedef float OutputPixelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input and output image types can be defined using the pixel types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The type of the gradient magnitude filter is defined by the
// input image and the output image types.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType, OutputImageType > FilterType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// A filter object is created by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// the source is an image reader.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the filter is executed by invoking the \code{Update()} method.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// If the output of this filter has been connected to other filters in a
// pipeline, updating any of the downstream filters will also trigger an
// update of this filter. For example, the gradient magnitude filter may be
// connected to an image writer.
//
// Software Guide : EndLatex
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
OutputImageType, WriteImageType > RescaleFilterType;
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[2] );
// Software Guide : BeginCodeSnippet
rescaler->SetInput( filter->GetOutput() );
writer->SetInput( rescaler->GetOutput() );
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// GradientMagnitudeImageFilter on a slice from a MRI proton density image
// of the brain.}
// \end{figure}
//
// effect of the gradient magnitude filter on a MRI proton density image of
// the brain. The figure shows the sensitivity of this filter to noisy data.
//
// Attention should be paid to the image type chosen to represent the output
// image since the dynamic range of the gradient magnitude image is usually
// smaller than the dynamic range of the input image. As always, there are
// exceptions to this rule, for example, synthetic images that contain high
// contrast objects.
//
// This filter does not apply any smoothing to the image before computing the
// gradients. The results can therefore be very sensitive to noise and may
// not be the best choice for scale-space analysis.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}