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
|
/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* 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
* limitations under the License.
*
*=========================================================================*/
#ifndef itkImageGaussianModelEstimator_h
#define itkImageGaussianModelEstimator_h
#include <cmath>
#include <cfloat>
#include <memory> // For unique_ptr.
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
#include "vnl/vnl_matrix_fixed.h"
#include "itkMath.h"
#include "vnl/algo/vnl_matrix_inverse.h"
#include "itkImageRegionIterator.h"
#include "itkMacro.h"
#include "itkImageModelEstimatorBase.h"
namespace itk
{
/**
* \class ImageGaussianModelEstimator
* \brief Base class for ImageGaussianModelEstimator object.
*
* itkImageGaussianModelEstimator generates the Gaussian model for given
* tissue types (or class types) in an input 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.
*
* 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.
*
* A zero label is used to identify the background. A model is not
* calculated for the background (its mean and covariance will be
* zero). Positive labels are classes for which models will be
* estimated. Negative labels indicate unlabeled data where no models
* will be estimated.
*
* 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 stores Gaussian models
* needs to be specified.
*
* The function EstimateModels() calculates the various models, creates the
* membership function objects and populates them.
*
* \ingroup ClassificationFilters
* \ingroup ITKClassifiers
*/
template <typename TInputImage, typename TMembershipFunction, typename TTrainingImage>
class ITK_TEMPLATE_EXPORT ImageGaussianModelEstimator : public ImageModelEstimatorBase<TInputImage, TMembershipFunction>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(ImageGaussianModelEstimator);
/** Standard class type aliases. */
using Self = ImageGaussianModelEstimator;
using Superclass = ImageModelEstimatorBase<TInputImage, TMembershipFunction>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(ImageGaussianModelEstimator);
/** Type definition for the input image. */
using InputImageType = TInputImage;
using InputImagePointer = typename TInputImage::Pointer;
using InputImageConstPointer = typename TInputImage::ConstPointer;
/** Type definitions for the training image. */
using TrainingImageType = TTrainingImage;
using TrainingImagePointer = typename TTrainingImage::Pointer;
using TrainingImageConstPointer = typename TTrainingImage::ConstPointer;
/** Type definition for the vector associated with
* input image pixel type. */
using InputImagePixelType = typename TInputImage::PixelType;
/** Type definitions for the vector holding
* training image pixel type. */
using TrainingImagePixelType = typename TTrainingImage::PixelType;
/** Type definitions for the iterators for the input and training images. */
using InputImageIterator = ImageRegionIterator<TInputImage>;
using InputImageConstIterator = ImageRegionConstIterator<TInputImage>;
using TrainingImageIterator = ImageRegionIterator<TTrainingImage>;
using TrainingImageConstIterator = ImageRegionConstIterator<TTrainingImage>;
/** Type definitions for the membership function . */
using MembershipFunctionType = TMembershipFunction;
using MembershipFunctionPointer = typename TMembershipFunction::Pointer;
/** Get/Set the training image. */
itkSetObjectMacro(TrainingImage, TrainingImageType);
itkGetModifiableObjectMacro(TrainingImage, TrainingImageType);
protected:
ImageGaussianModelEstimator() = default;
~ImageGaussianModelEstimator() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** Starts the image modeling process */
void
GenerateData() override;
private:
using MatrixType = vnl_matrix<double>;
using InputImageSizeType = typename TInputImage::SizeType;
/** Dimension of each individual pixel vector. */
static constexpr unsigned int VectorDimension = InputImagePixelType::Dimension;
/** Generate the model based on the training input data.
* Achieves the goal of training the classifier.
* Takes the set of training images and internally computes the means and
* variance of the various classes defined in the training set.
*/
void
EstimateModels() override;
void
EstimateGaussianModelParameters();
MatrixType m_NumberOfSamples{};
MatrixType m_Means{};
std::unique_ptr<MatrixType[]> m_Covariance{ nullptr };
TrainingImagePointer m_TrainingImage{};
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkImageGaussianModelEstimator.hxx"
#endif
#endif
|
