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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkImageGaussianModelEstimator.h,v $
Language: C++
Date: $Date: 2005-03-30 15:13:39 $
Version: $Revision: 1.11 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef _itkImageGaussianModelEstimator_h
#define _itkImageGaussianModelEstimator_h
#include <math.h>
#include <float.h>
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
#include "vnl/vnl_matrix_fixed.h"
#include "vnl/vnl_math.h"
#include "vnl/algo/vnl_matrix_inverse.h"
#include "itkImageRegionIterator.h"
#include "itkExceptionObject.h"
#include "itkImageModelEstimatorBase.h"
namespace itk
{
/** \class ImageGaussianModelEstimator
* \brief Base class for ImageGaussianModelEstimator object
*
* itkImageGaussianModelEstimator generated the gaussian model for given
* tissue types (or class types) in an input training set.
* training data set for segmentation. The training data set is typically
* provided as a set of labelled/classified data set by the user. A gaussian
* model is generated for each label present in the training data set.
* from the training data set.
*
* The user should ensure that both the input and training images
* are of the same size. The input data consists of the raw data and the
* training data has class labels associated with each pixel. However, only
* a subset of the data need to be labelled. Unlabelled data could be
* represented by a non zero, non positive number. The training data are
* analysed for identifying the classes. Any non zero, non negative value is
* considered a valid label. It is important that the maximum value of the
* training label be equal to N, where N is the number of classes represented
* by the maximum label value in the training data set. The pixels
* corresponding to each training label is parsed and the mean and covariance
* is calculated for each class. The background is identified by the label zero
* and is not parsed for further computation to improve efficiency.
*
* This object supports data handling of multiband images. The object
* accepts the input image in vector format only, where each pixel is a
* vector and each element of the vector corresponds to an entry from
* 1 particular band of a multiband dataset. A single band image is treated
* as a vector image with a single element for every vector. The classified
* image is treated as a single band scalar image.
*
* This function is templated over the type of input and output images. In
* addition, a third parameter for the MembershipFunction needs to be
* specified. In this case a Membership function that store Gaussian models
* needs to be specified.
*
* The function EstimateModels() calculated the various models, creates the
* membership function objects and populates them.
*
* \ingroup ClassificationFilters
*/
template <class TInputImage,
class TMembershipFunction,
class TTrainingImage>
class ITK_EXPORT ImageGaussianModelEstimator:
public ImageModelEstimatorBase<TInputImage, TMembershipFunction>
{
public:
/** Standard class typedefs. */
typedef ImageGaussianModelEstimator Self;
typedef ImageModelEstimatorBase<TInputImage,TMembershipFunction> Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(ImageGaussianModelEstimator, ImageModelEstimatorBase);
/** Type definition for the input image. */
typedef typename TInputImage::Pointer InputImagePointer;
/** Type definitions for the training image. */
typedef typename TTrainingImage::Pointer TrainingImagePointer;
/** Type definition for the vector associated with
* input image pixel type. */
typedef typename TInputImage::PixelType InputImagePixelType;
/** Type definitions for the vector holding
* training image pixel type. */
typedef typename TTrainingImage::PixelType TrainingImagePixelType;
/** Type definitions for the iterators for the input and training images. */
typedef
ImageRegionIterator< TInputImage > InputImageIterator;
typedef
ImageRegionIterator< TTrainingImage > TrainingImageIterator;
/** Type definitions for the membership function . */
typedef typename TMembershipFunction::Pointer MembershipFunctionPointer ;
/** Set the training image. */
itkSetMacro(TrainingImage,TrainingImagePointer);
/** Get the training image. */
itkGetMacro(TrainingImage,TrainingImagePointer);
protected:
ImageGaussianModelEstimator();
~ImageGaussianModelEstimator();
virtual void PrintSelf(std::ostream& os, Indent indent) const;
/** Starts the image modelling process */
void GenerateData() ;
private:
ImageGaussianModelEstimator(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
typedef vnl_matrix<double> MatrixType;
typedef vnl_vector<double> VectorType;
typedef typename TInputImage::SizeType InputImageSizeType;
/** Dimension of the each individual pixel vector. */
itkStaticConstMacro(VectorDimension, unsigned int,
InputImagePixelType::Dimension);
typedef vnl_matrix_fixed<double,1,itkGetStaticConstMacro(VectorDimension)> ColumnVectorType;
MatrixType m_NumberOfSamples;
MatrixType m_Means;
MatrixType *m_Covariance;
TrainingImagePointer m_TrainingImage;
/** A function that generates the
* model based on the training input data
* Achieves the goal of training the classifier. */
virtual void EstimateModels();
void EstimateGaussianModelParameters();
}; // class ImageGaussianModelEstimator
} // namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkImageGaussianModelEstimator.txx"
#endif
#endif
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