forked from InsightSoftwareConsortium/ITK
-
Notifications
You must be signed in to change notification settings - Fork 0
/
itkOtsuMultipleThresholdsCalculator.txx
262 lines (220 loc) · 8.57 KB
/
itkOtsuMultipleThresholdsCalculator.txx
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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkOtsuMultipleThresholdsCalculator.txx
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 _itkOtsuMultipleThresholdsCalculator_txx
#define _itkOtsuMultipleThresholdsCalculator_txx
#include "itkOtsuMultipleThresholdsCalculator.h"
namespace itk
{
template<class TInputHistogram>
OtsuMultipleThresholdsCalculator<TInputHistogram>
::OtsuMultipleThresholdsCalculator()
{
m_NumberOfThresholds = 1;
m_Output.resize(m_NumberOfThresholds);
std::fill(m_Output.begin(),m_Output.end(),NumericTraits<MeasurementType>::Zero);
}
template<class TInputHistogram>
const typename OtsuMultipleThresholdsCalculator<TInputHistogram>::OutputType&
OtsuMultipleThresholdsCalculator< TInputHistogram >
::GetOutput()
{
return m_Output ;
}
/*
* Increment the thresholds of one position along the histogram
*/
template<class TInputHistogram>
bool
OtsuMultipleThresholdsCalculator<TInputHistogram>
::IncrementThresholds(InstanceIdentifierVectorType& thresholdIndexes, MeanType globalMean, MeanVectorType& classMean, FrequencyVectorType& classFrequency)
{
typename TInputHistogram::ConstPointer histogram = this->GetInputHistogram();
unsigned long numberOfHistogramBins = histogram->Size();
unsigned long numberOfClasses = classMean.size();
MeanType meanOld;
FrequencyType freqOld;
unsigned int k;
int j;
// from the upper threshold down
for(j=static_cast<int>(m_NumberOfThresholds-1); j>=0; j--)
{
// if this threshold can be incremented (i.e. we're not at the end of the histogram)
if (thresholdIndexes[j] < numberOfHistogramBins - 2 - (m_NumberOfThresholds-1 - j) )
{
// increment it and update mean and frequency of the class bounded by the threshold
++thresholdIndexes[j];
meanOld = classMean[j];
freqOld = classFrequency[j];
classFrequency[j] += histogram->GetFrequency(thresholdIndexes[j]);
if (NumericTraits<FrequencyType>::IsPositive(classFrequency[j]))
{
classMean[j] = (meanOld * static_cast<MeanType>(freqOld) + static_cast<MeanType>(histogram->GetMeasurementVector(thresholdIndexes[j])[0]) * static_cast<MeanType>(histogram->GetFrequency(thresholdIndexes[j]))) / static_cast<MeanType>(classFrequency[j]);
}
else
{
classMean[j] = NumericTraits<MeanType>::Zero;
}
// set higher thresholds adjacent to their previous ones, and update mean and frequency of the respective classes
for (k=j+1; k<m_NumberOfThresholds; k++)
{
thresholdIndexes[k] = thresholdIndexes[k-1] + 1;
classFrequency[k] = histogram->GetFrequency(thresholdIndexes[k]);
if (NumericTraits<FrequencyType>::IsPositive(classFrequency[k]))
{
classMean[k] = static_cast<MeanType>(histogram->GetMeasurementVector(thresholdIndexes[k])[0]);
}
else
{
classMean[k] = NumericTraits<MeanType>::Zero;
}
}
// update mean and frequency of the highest class
classFrequency[numberOfClasses-1] = histogram->GetTotalFrequency();
classMean[numberOfClasses-1] = globalMean * histogram->GetTotalFrequency();
for(k=0; k<numberOfClasses-1; k++)
{
classFrequency[numberOfClasses-1] -= classFrequency[k];
classMean[numberOfClasses-1] -= classMean[k] * static_cast<MeanType>(classFrequency[k]);
}
if (NumericTraits<FrequencyType>::IsPositive(classFrequency[numberOfClasses-1]))
{
classMean[numberOfClasses-1] /= static_cast<MeanType>(classFrequency[numberOfClasses-1]);
}
else
{
classMean[numberOfClasses-1] = NumericTraits<MeanType>::Zero;
}
// exit the for loop if a threshold has been incremented
break;
}
else // if this threshold can't be incremented
{
// if it's the lowest threshold
if (j==0)
{
// we couldn't increment because we're done
return false;
}
}
}
// we incremented
return true;
}
/*
* Compute Otsu's thresholds
*/
template<class TInputHistogram>
void
OtsuMultipleThresholdsCalculator<TInputHistogram>
::GenerateData()
{
typename TInputHistogram::ConstPointer histogram = this->GetInputHistogram();
// TODO: as an improvement, the class could accept multi-dimensional histograms
// and the user could specify the dimension to apply the algorithm to.
if (histogram->GetSize().GetSizeDimension() != 1)
{
itkExceptionMacro(<<"Histogram must be 1-dimensional.");
}
// compute global mean
typename TInputHistogram::ConstIterator iter = histogram->Begin() ;
typename TInputHistogram::ConstIterator end = histogram->End() ;
MeanType globalMean = NumericTraits<MeanType>::Zero;
FrequencyType globalFrequency = histogram->GetTotalFrequency();
while (iter != end)
{
globalMean += static_cast<MeanType>(iter.GetMeasurementVector()[0]) * static_cast<MeanType>(iter.GetFrequency());
++iter ;
}
globalMean /= static_cast<MeanType>(globalFrequency);
unsigned long numberOfClasses = m_NumberOfThresholds + 1;
// initialize thresholds
InstanceIdentifierVectorType thresholdIndexes(m_NumberOfThresholds);
unsigned long j;
for(j=0; j<m_NumberOfThresholds; j++)
{
thresholdIndexes[j] = j;
}
InstanceIdentifierVectorType maxVarThresholdIndexes = thresholdIndexes;
// compute frequency and mean of initial classes
FrequencyType freqSum = NumericTraits<FrequencyType>::Zero;
FrequencyVectorType classFrequency(numberOfClasses);
for (j=0; j<numberOfClasses-1; j++)
{
classFrequency[j] = histogram->GetFrequency(thresholdIndexes[j]);
freqSum += classFrequency[j];
}
classFrequency[numberOfClasses-1] = globalFrequency - freqSum;
MeanType meanSum = NumericTraits<MeanType>::Zero;
MeanVectorType classMean(numberOfClasses);
for (j=0; j < numberOfClasses-1; j++)
{
if (NumericTraits<FrequencyType>::IsPositive(classFrequency[j]))
{
classMean[j] = static_cast<MeanType>(histogram->GetMeasurementVector(j)[0]);
}
else
{
classMean[j] = NumericTraits<MeanType>::Zero;
}
meanSum += classMean[j] * static_cast<MeanType>(classFrequency[j]);
}
if (NumericTraits<FrequencyType>::IsPositive(classFrequency[numberOfClasses-1]))
{
classMean[numberOfClasses-1] = (globalMean * static_cast<MeanType>(globalFrequency) - meanSum) / static_cast<MeanType>(classFrequency[numberOfClasses-1]);
}
else
{
classMean[numberOfClasses-1] = NumericTraits<MeanType>::Zero;
}
VarianceType maxVarBetween = NumericTraits<VarianceType>::Zero;
for (j=0; j<numberOfClasses; j++)
{
maxVarBetween += static_cast<VarianceType>(classFrequency[j]) * static_cast<VarianceType>((globalMean - classMean[j]) * (globalMean - classMean[j]));
}
// explore all possible threshold configurations and choose the one that yields maximum between-class variance
while (Self::IncrementThresholds(thresholdIndexes, globalMean, classMean, classFrequency))
{
VarianceType varBetween = NumericTraits<VarianceType>::Zero;
for (j=0; j<numberOfClasses; j++)
{
varBetween += static_cast<VarianceType>(classFrequency[j]) * static_cast<VarianceType>((globalMean - classMean[j]) * (globalMean - classMean[j]));
}
if (varBetween > maxVarBetween)
{
maxVarBetween = varBetween;
maxVarThresholdIndexes = thresholdIndexes;
}
}
// copy corresponding bin max to threshold vector
m_Output.resize(m_NumberOfThresholds);
for (j=0; j<m_NumberOfThresholds; j++)
{
m_Output[j] = histogram->GetBinMax(0,maxVarThresholdIndexes[j]);
}
}
template<class TInputHistogram>
void
OtsuMultipleThresholdsCalculator<TInputHistogram>
::PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os,indent);
os << indent << "NumberOfThresholds: " << m_NumberOfThresholds;
os << indent << "Output: ";
for (unsigned long j=0; j<m_NumberOfThresholds; j++)
{
os << m_Output[j] << " ";
}
os << std::endl;
}
} // end namespace itk
#endif