ITK  4.6.0
Insight Segmentation and Registration Toolkit
Statistics/ScalarImageKmeansClassifier.cxx
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*
* 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
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* http://www.apache.org/licenses/LICENSE-2.0.txt
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* 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
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// OUTPUTS: {BrainT1Slice_labelled.png}
// ARGUMENTS: 0 3 14.8 91.6 134.9
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example shows how to use the KMeans model for classifying the pixel of
// a scalar image.
//
// The \subdoxygen{Statistics}{ScalarImageKmeansImageFilter} is used for taking
// a scalar image and applying the K-Means algorithm in order to define classes
// that represents statistical distributions of intensity values in the pixels.
// The classes are then used in this filter for generating a labeled image where
// every pixel is assigned to one of the classes.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
// Software Guide : EndCodeSnippet
int main( int argc, char * argv [] )
{
if( argc < 5 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0];
std::cerr << " inputScalarImage outputLabeledImage contiguousLabels";
std::cerr << " numberOfClasses mean1 mean2... meanN " << std::endl;
return EXIT_FAILURE;
}
const char * inputImageFileName = argv[1];
// Software Guide : BeginLatex
//
// First we define the pixel type and dimension of the image that we intend to
// classify. With this image type we can =also declare the
// \doxygen{ImageFileReader} needed for reading the input image, create one and
// set its input filename.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef signed short PixelType;
const unsigned int Dimension = 2;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( inputImageFileName );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// With the \code{ImageType} we instantiate the type of the
// \doxygen{ScalarImageKmeansImageFilter} that will compute the K-Means model
// and then classify the image pixels.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
KMeansFilterType::Pointer kmeansFilter = KMeansFilterType::New();
kmeansFilter->SetInput( reader->GetOutput() );
const unsigned int numberOfInitialClasses = atoi( argv[4] );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In general the classification will produce as output an image whose pixel
// values are integers associated to the labels of the classes. Since typically
// these integers will be generated in order (0,1,2,...N), the output image
// will tend to look very dark when displayed with naive viewers. It is
// therefore convenient to have the option of spreading the label values over
// the dynamic range of the output image pixel type. When this is done, the
// dynamic range of the pixels is divide by the number of classes in order to
// define the increment between labels. For example, an output image of 8 bits
// will have a dynamic range of [0:256], and when it is used for holding four
// classes, the non-contiguous labels will be (0,64,128,192). The selection of
// the mode to use is done with the method \code{SetUseContiguousLabels()}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int useNonContiguousLabels = atoi( argv[3] );
kmeansFilter->SetUseNonContiguousLabels( useNonContiguousLabels );
// Software Guide : EndCodeSnippet
const unsigned int argoffset = 5;
if( static_cast<unsigned int>(argc) <
numberOfInitialClasses + argoffset )
{
std::cerr << "Error: " << std::endl;
std::cerr << numberOfInitialClasses << " classes has been specified ";
std::cerr << "but no enough means have been provided in the command ";
std::cerr << "line arguments " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// For each one of the classes we must provide a tentative initial value for
// the mean of the class. Given that this is a scalar image, each one of the
// means is simply a scalar value. Note however that in a general case of
// K-Means, the input image would be a vector image and therefore the means
// will be vectors of the same dimension as the image pixels.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
for( unsigned k=0; k < numberOfInitialClasses; k++ )
{
const double userProvidedInitialMean = atof( argv[k+argoffset] );
kmeansFilter->AddClassWithInitialMean( userProvidedInitialMean );
}
// Software Guide : EndCodeSnippet
const char * outputImageFileName = argv[2];
// Software Guide : BeginLatex
//
// The \doxygen{ScalarImageKmeansImageFilter} is predefined for producing an 8
// bits scalar image as output. This output image contains labels associated
// to each one of the classes in the K-Means algorithm. In the following lines
// we use the \code{OutputImageType} in order to instantiate the type of a
// \doxygen{ImageFileWriter}. Then create one, and connect it to the output of
// the classification filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef KMeansFilterType::OutputImageType OutputImageType;
WriterType::Pointer writer = WriterType::New();
writer->SetInput( kmeansFilter->GetOutput() );
writer->SetFileName( outputImageFileName );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We are now ready for triggering the execution of the pipeline. This is done
// by simply invoking the \code{Update()} method in the writer. This call will
// propagate the update request to the reader and then to the classifier.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
writer->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Problem encountered while writing ";
std::cerr << " image file : " << argv[2] << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// At this point the classification is done, the labeled image is saved in a
// file, and we can take a look at the means that were found as a result of the
// model estimation performed inside the classifier filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
KMeansFilterType::ParametersType estimatedMeans =
kmeansFilter->GetFinalMeans();
const unsigned int numberOfClasses = estimatedMeans.Size();
for ( unsigned int i = 0; i < numberOfClasses; ++i )
{
std::cout << "cluster[" << i << "] ";
std::cout << " estimated mean : " << estimatedMeans[i] << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{BrainT1Slice_labelled}
// \itkcaption[Output of the KMeans classifier]{Effect of the
// KMeans classifier on a T1 slice of the brain.}
// \label{fig:ScalarImageKMeansClassifierOutput}
// \end{figure}
//
// Figure \ref{fig:ScalarImageKMeansClassifierOutput}
// illustrates the effect of this filter with three classes.
// The means were estimated by ScalarImageKmeansModelEstimator.cxx.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}