forked from InsightSoftwareConsortium/ITK
-
Notifications
You must be signed in to change notification settings - Fork 0
/
itkImageGaussianModelEstimator.h
174 lines (134 loc) · 6.21 KB
/
itkImageGaussianModelEstimator.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkImageGaussianModelEstimator.h
Language: C++
Date: $Date$
Version: $Revision$
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