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itkMahalanobisDistanceMembershipFunction.h

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00001 /*=========================================================================
00002 
00003   Program:   Insight Segmentation & Registration Toolkit
00004   Module:    $RCSfile: itkMahalanobisDistanceMembershipFunction.h,v $
00005   Language:  C++
00006   Date:      $Date: 2003/12/23 18:42:00 $
00007   Version:   $Revision: 1.9 $
00008 
00009   Copyright (c) Insight Software Consortium. All rights reserved.
00010   See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
00011 
00012      This software is distributed WITHOUT ANY WARRANTY; without even 
00013      the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
00014      PURPOSE.  See the above copyright notices for more information.
00015 
00016 =========================================================================*/
00017 #ifndef __itkMahalanobisDistanceMembershipFunction_h
00018 #define __itkMahalanobisDistanceMembershipFunction_h
00019 
00020 #include <vnl/vnl_vector.h>
00021 #include <vnl/vnl_transpose.h>
00022 #include <vnl/vnl_matrix.h>
00023 #include <vnl/algo/vnl_matrix_inverse.h>
00024 #include <vnl/algo/vnl_determinant.h>
00025 
00026 
00027 #include "itkMembershipFunctionBase.h"
00028 
00029 
00030 namespace itk{ 
00031 namespace Statistics{
00032 
00042 template< class TVector >
00043 class ITK_EXPORT MahalanobisDistanceMembershipFunction :
00044     public MembershipFunctionBase< TVector >
00045 {
00046 public:
00048   typedef MahalanobisDistanceMembershipFunction Self;
00049   typedef MembershipFunctionBase< TVector > Superclass ;
00050   typedef SmartPointer<Self> Pointer;
00051   typedef SmartPointer<const Self> ConstPointer;
00052 
00054   itkTypeMacro(MahalanobisDistanceMembershipFunction, MembershipFunctionBase);
00055   itkNewMacro(Self);
00056   
00058   typedef TVector MeasurementVectorType ;
00059 
00061   typedef vnl_vector<double>  MeanVectorType;
00062 
00064   typedef vnl_matrix<double>  CovarianceMatrixType;
00065 
00067   void SetMean(const MeanVectorType &mean) ;
00068   
00070   const MeanVectorType & GetMean() const;
00071 
00076   void SetCovariance(const CovarianceMatrixType &cov); 
00077   
00079   itkGetConstReferenceMacro( Covariance, CovarianceMatrixType );
00080 
00085   void SetInverseCovariance(const CovarianceMatrixType &invcov); 
00086   
00088   itkGetConstReferenceMacro( InverseCovariance, CovarianceMatrixType );
00089 
00091   itkSetMacro( NumberOfSamples, double );
00092 
00094   itkGetMacro( NumberOfSamples, double );
00095 
00099   double Evaluate(const MeasurementVectorType &measurement) const;
00100   
00101 protected:
00102   MahalanobisDistanceMembershipFunction(void) ;
00103   virtual ~MahalanobisDistanceMembershipFunction(void) {}
00104   void PrintSelf(std::ostream& os, Indent indent) const;
00105 
00106 private:
00107   MeanVectorType        m_Mean;              // mean
00108   CovarianceMatrixType  m_Covariance;        // covariance matrix
00109 
00110   // inverse covariance matrix which is automatically calculated 
00111   // when covariace matirx is set.  This speed up the GetProbability()
00112   CovarianceMatrixType  m_InverseCovariance; 
00113 
00114   // Number of samples defining this density
00115   double m_NumberOfSamples;
00116   // pre_factor which is automatically calculated 
00117   // when covariace matirx is set.  This speeds up the GetProbability()  
00118   double       m_PreFactor;
00119   double       m_Epsilon;
00120   double       m_DoubleMax;
00121  
00122   itkStaticConstMacro(VectorDimension, unsigned int, TVector::Dimension);
00123   typedef vnl_matrix_fixed<double,1,itkGetStaticConstMacro(VectorDimension)> ColumnVectorType;
00124 
00125   mutable ColumnVectorType      m_TempVec;
00126   mutable ColumnVectorType      m_TempMat;
00127 
00128   void CalculateInverseCovariance(); 
00129 };
00130 
00131 } // end of namespace Statistics
00132 } // end namespace itk
00133 
00134 #ifndef ITK_MANUAL_INSTANTIATION
00135 #include "itkMahalanobisDistanceMembershipFunction.txx"
00136 #endif
00137 
00138 #endif

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