ITK
4.1.0
Insight Segmentation and Registration Toolkit
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00001 /*========================================================================= 00002 * 00003 * Copyright Insight Software Consortium 00004 * 00005 * Licensed under the Apache License, Version 2.0 (the "License"); 00006 * you may not use this file except in compliance with the License. 00007 * You may obtain a copy of the License at 00008 * 00009 * http://www.apache.org/licenses/LICENSE-2.0.txt 00010 * 00011 * Unless required by applicable law or agreed to in writing, software 00012 * distributed under the License is distributed on an "AS IS" BASIS, 00013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 00014 * See the License for the specific language governing permissions and 00015 * limitations under the License. 00016 * 00017 *=========================================================================*/ 00018 #ifndef __itkMahalanobisDistanceMetric_h 00019 #define __itkMahalanobisDistanceMetric_h 00020 00021 #include "vnl/vnl_vector.h" 00022 #include "vnl/vnl_vector_ref.h" 00023 #include "vnl/vnl_transpose.h" 00024 #include "vnl/vnl_matrix.h" 00025 #include "vnl/algo/vnl_matrix_inverse.h" 00026 #include "vnl/algo/vnl_determinant.h" 00027 #include "itkArray.h" 00028 00029 #include "itkDistanceMetric.h" 00030 00031 namespace itk 00032 { 00033 namespace Statistics 00034 { 00045 template< class TVector > 00046 class ITK_EXPORT MahalanobisDistanceMetric: 00047 public DistanceMetric< TVector > 00048 { 00049 public: 00051 typedef MahalanobisDistanceMetric Self; 00052 typedef DistanceMetric< TVector > Superclass; 00053 typedef SmartPointer< Self > Pointer; 00054 typedef SmartPointer< const Self > ConstPointer; 00055 00057 itkTypeMacro(MahalanobisDistanceMetric, DistanceMetric); 00058 itkNewMacro(Self); 00060 00062 typedef typename Superclass::MeasurementVectorType MeasurementVectorType; 00063 00065 typedef typename Superclass::MeasurementVectorSizeType MeasurementVectorSizeType; 00066 00068 typedef typename Superclass::OriginType MeanVectorType; 00069 00071 typedef vnl_matrix< double > CovarianceMatrixType; 00072 00074 virtual void SetMeasurementVectorSize(MeasurementVectorSizeType); 00075 00077 void SetMean(const MeanVectorType & mean); 00078 00080 const MeanVectorType & GetMean() const; 00081 00086 void SetCovariance(const CovarianceMatrixType & cov); 00087 00089 itkGetConstReferenceMacro(Covariance, CovarianceMatrixType); 00090 00093 void SetInverseCovariance(const CovarianceMatrixType & invcov); 00094 00096 itkGetConstReferenceMacro(InverseCovariance, CovarianceMatrixType); 00097 00101 double Evaluate(const MeasurementVectorType & measurement) const; 00102 00104 double Evaluate(const MeasurementVectorType & x1, const MeasurementVectorType & x2) const; 00105 00107 itkSetMacro(Epsilon, double); 00108 itkGetConstMacro(Epsilon, double); 00110 00111 itkSetMacro(DoubleMax, double); 00112 itkGetConstMacro(DoubleMax, double); 00113 protected: 00114 MahalanobisDistanceMetric(void); 00115 virtual ~MahalanobisDistanceMetric(void) {} 00116 void PrintSelf(std::ostream & os, Indent indent) const; 00117 00118 private: 00119 MeanVectorType m_Mean; // mean 00120 CovarianceMatrixType m_Covariance; // covariance matrix 00121 00122 // inverse covariance matrix which is automatically calculated 00123 // when covariace matirx is set. This speed up the GetProbability() 00124 CovarianceMatrixType m_InverseCovariance; 00125 00126 double m_Epsilon; 00127 double m_DoubleMax; 00128 00129 void CalculateInverseCovariance(); 00130 }; 00131 } // end of namespace Statistics 00132 } // end namespace itk 00133 00134 #ifndef ITK_MANUAL_INSTANTIATION 00135 #include "itkMahalanobisDistanceMetric.hxx" 00136 #endif 00137 00138 #endif 00139