void
CalculateCoocurenceFeatures(itk::Image<TPixel, VImageDimension>* itkImage, mitk::Image::Pointer mask, mitk::GIFCooccurenceMatrix::FeatureListType & featureList, double range)
{
typedef itk::Image<TPixel, VImageDimension> ImageType;
typedef itk::Image<TPixel, VImageDimension> MaskType;
typedef itk::Statistics::ScalarImageToTextureFeaturesFilter<ImageType> FilterType;
typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;
typedef typename FilterType::TextureFeaturesFilterType TextureFilterType;
typename MaskType::Pointer maskImage = MaskType::New();
mitk::CastToItkImage(mask, maskImage);
typename FilterType::Pointer filter = FilterType::New();
typename FilterType::OffsetVector::Pointer newOffset = FilterType::OffsetVector::New();
auto oldOffsets = filter->GetOffsets();
auto oldOffsetsIterator = oldOffsets->Begin();
while(oldOffsetsIterator != oldOffsets->End())
{
typename FilterType::OffsetType offset = oldOffsetsIterator->Value();
for (unsigned int i = 0; i < VImageDimension; ++i)
{
offset[i] *= range;
}
newOffset->push_back(offset);
oldOffsetsIterator++;
}
filter->SetOffsets(newOffset);
// All features are required
typename FilterType::FeatureNameVectorPointer requestedFeatures = FilterType::FeatureNameVector::New();
requestedFeatures->push_back(TextureFilterType::Energy);
requestedFeatures->push_back(TextureFilterType::Entropy);
requestedFeatures->push_back(TextureFilterType::Correlation);
requestedFeatures->push_back(TextureFilterType::InverseDifferenceMoment);
requestedFeatures->push_back(TextureFilterType::Inertia);
requestedFeatures->push_back(TextureFilterType::ClusterShade);
requestedFeatures->push_back(TextureFilterType::ClusterProminence);
requestedFeatures->push_back(TextureFilterType::HaralickCorrelation);
typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
minMaxComputer->SetImage(itkImage);
minMaxComputer->Compute();
filter->SetInput(itkImage);
filter->SetMaskImage(maskImage);
filter->SetRequestedFeatures(requestedFeatures);
filter->SetPixelValueMinMax(minMaxComputer->GetMinimum(),minMaxComputer->GetMaximum());
filter->Update();
auto featureMeans = filter->GetFeatureMeans ();
auto featureStd = filter->GetFeatureStandardDeviations();
std::ostringstream ss;
ss << range;
std::string strRange = ss.str();
for (std::size_t i = 0; i < featureMeans->size(); ++i)
{
switch (i)
{
case TextureFilterType::Energy :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Energy Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Energy Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::Entropy :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Entropy Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Entropy Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::Correlation :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Correlation Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Correlation Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::InverseDifferenceMoment :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") InverseDifferenceMoment Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") InverseDifferenceMoment Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::Inertia :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Inertia Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") Inertia Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::ClusterShade :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") ClusterShade Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") ClusterShade Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::ClusterProminence :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") ClusterProminence Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") ClusterProminence Std.",featureStd->ElementAt(i)));
break;
case TextureFilterType::HaralickCorrelation :
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") HaralickCorrelation Means",featureMeans->ElementAt(i)));
featureList.push_back(std::make_pair("co-occ. ("+ strRange+") HaralickCorrelation Std.",featureStd->ElementAt(i)));
break;
default:
break;
}
}
}
void
CalculateFirstOrderStatistics(itk::Image<TPixel, VImageDimension>* itkImage, mitk::Image::Pointer mask, mitk::GIFFirstOrderStatistics::FeatureListType & featureList)
{
typedef itk::Image<TPixel, VImageDimension> ImageType;
typedef itk::Image<int, VImageDimension> MaskType;
typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
typedef typename FilterType::HistogramType HistogramType;
typedef typename HistogramType::IndexType HIndexType;
typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;
typename MaskType::Pointer maskImage = MaskType::New();
mitk::CastToItkImage(mask, maskImage);
typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
minMaxComputer->SetImage(itkImage);
minMaxComputer->Compute();
double imageRange = minMaxComputer->GetMaximum() - minMaxComputer->GetMinimum();
typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
labelStatisticsImageFilter->SetInput( itkImage );
labelStatisticsImageFilter->SetLabelInput(maskImage);
labelStatisticsImageFilter->SetUseHistograms(true);
labelStatisticsImageFilter->SetHistogramParameters(256, minMaxComputer->GetMinimum(),minMaxComputer->GetMaximum());
labelStatisticsImageFilter->Update();
// --------------- Range --------------------
double range = labelStatisticsImageFilter->GetMaximum(1) - labelStatisticsImageFilter->GetMinimum(1);
// --------------- Uniformity, Entropy --------------------
double count = labelStatisticsImageFilter->GetCount(1);
double std_dev = labelStatisticsImageFilter->GetSigma(1);
double mean = labelStatisticsImageFilter->GetMean(1);
auto histogram = labelStatisticsImageFilter->GetHistogram(1);
HIndexType index;
index.SetSize(1);
double binWidth = histogram->GetBinMax(0, 0) - histogram->GetBinMin(0, 0);
double uniformity = 0;
double entropy = 0;
double squared_sum = 0;
double kurtosis = 0;
double mean_absolut_deviation = 0;
double skewness = 0;
for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
{
index[0] = i;
double prob = histogram->GetFrequency(index);
double voxelValue = histogram->GetBinMin(0, i) +binWidth * 0.5;
squared_sum += prob * voxelValue*voxelValue;
kurtosis += prob* (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean);
skewness += prob* (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean);
mean_absolut_deviation = prob* std::abs(voxelValue - mean);
prob /= count;
uniformity += prob*prob;
if (prob > 0)
entropy += prob * std::log(prob);
}
double rms = std::sqrt(squared_sum / count);
kurtosis = kurtosis / count / (std_dev * std_dev);
skewness = skewness / count / (std_dev * std_dev * std_dev);
mean_absolut_deviation = mean_absolut_deviation / count;
double coveredGrayValueRange = range / imageRange;
featureList.push_back(std::make_pair("FirstOrder Range",range));
featureList.push_back(std::make_pair("FirstOrder Uniformity",uniformity));
featureList.push_back(std::make_pair("FirstOrder Entropy",entropy));
featureList.push_back(std::make_pair("FirstOrder Energy",squared_sum));
featureList.push_back(std::make_pair("FirstOrder RMS",rms));
featureList.push_back(std::make_pair("FirstOrder Kurtosis",kurtosis));
featureList.push_back(std::make_pair("FirstOrder Skewness",skewness));
featureList.push_back(std::make_pair("FirstOrder Mean absolute deviation",mean_absolut_deviation));
featureList.push_back(std::make_pair("FirstOrder Covered Image Intensity Range",coveredGrayValueRange));
featureList.push_back(std::make_pair("FirstOrder Minimum",labelStatisticsImageFilter->GetMinimum(1)));
featureList.push_back(std::make_pair("FirstOrder Maximum ",labelStatisticsImageFilter->GetMaximum(1)));
featureList.push_back(std::make_pair("FirstOrder Mean",labelStatisticsImageFilter->GetMean(1)));
featureList.push_back(std::make_pair("FirstOrder Variance",labelStatisticsImageFilter->GetVariance(1)));
featureList.push_back(std::make_pair("FirstOrder Sum",labelStatisticsImageFilter->GetSum(1)));
featureList.push_back(std::make_pair("FirstOrder Median",labelStatisticsImageFilter->GetMedian(1)));
featureList.push_back(std::make_pair("FirstOrder Standard deviation",labelStatisticsImageFilter->GetSigma(1)));
featureList.push_back(std::make_pair("FirstOrder No. of Voxel",labelStatisticsImageFilter->GetCount(1)));
}
void mitk::MRNormLinearStatisticBasedFilter::InternalComputeMask(itk::Image<TPixel, VImageDimension>* itkImage)
{
// Define all necessary Types
typedef itk::Image<TPixel, VImageDimension> ImageType;
typedef itk::Image<int, VImageDimension> MaskType;
typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;
typename MaskType::Pointer itkMask0 = MaskType::New();
mitk::CastToItkImage(this->GetMask(), itkMask0);
typename ImageType::Pointer outImage = ImageType::New();
mitk::CastToItkImage(this->GetOutput(0), outImage);
typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
minMaxComputer->SetImage(itkImage);
minMaxComputer->Compute();
typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
labelStatisticsImageFilter->SetUseHistograms(true);
labelStatisticsImageFilter->SetHistogramParameters(256, minMaxComputer->GetMinimum(),minMaxComputer->GetMaximum());
labelStatisticsImageFilter->SetInput( itkImage );
labelStatisticsImageFilter->SetLabelInput(itkMask0);
labelStatisticsImageFilter->Update();
double median0 = labelStatisticsImageFilter->GetMedian(1);
double mean0 = labelStatisticsImageFilter->GetMean(1);
double stddev = labelStatisticsImageFilter->GetSigma(1);
double modulo0=0;
{
auto histo = labelStatisticsImageFilter->GetHistogram(1);
double maxFrequency=0;
for (auto hIter=histo->Begin();hIter!=histo->End();++hIter)
{
if (maxFrequency < hIter.GetFrequency())
{
maxFrequency = hIter.GetFrequency();
modulo0 = (histo->GetBinMin(0,hIter.GetInstanceIdentifier()) + histo->GetBinMax(0,hIter.GetInstanceIdentifier())) / 2.0;
}
}
}
double value0=0;
switch (m_CenterMode)
{
case MRNormLinearStatisticBasedFilter::MEAN:
value0=mean0; break;
case MRNormLinearStatisticBasedFilter::MEDIAN:
value0=median0; break;
case MRNormLinearStatisticBasedFilter::MODE:
value0=modulo0; break;
}
double offset = value0;
double scaling = stddev;
if (scaling < 0.0001)
return;
itk::ImageRegionIterator<ImageType> inIter(itkImage, itkImage->GetLargestPossibleRegion());
itk::ImageRegionIterator<ImageType> outIter(outImage, outImage->GetLargestPossibleRegion());
while (! inIter.IsAtEnd())
{
TPixel value = inIter.Value();
outIter.Set((value - offset) / scaling);
++inIter;
++outIter;
}
}
void
CalculateFirstOrderStatistics(itk::Image<TPixel, VImageDimension>* itkImage, mitk::Image::Pointer mask, mitk::GIFFirstOrderStatistics::FeatureListType & featureList, mitk::GIFFirstOrderStatistics::ParameterStruct params)
{
typedef itk::Image<TPixel, VImageDimension> ImageType;
typedef itk::Image<unsigned short, VImageDimension> MaskType;
typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
typedef typename FilterType::HistogramType HistogramType;
typedef typename HistogramType::IndexType HIndexType;
typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;
typename MaskType::Pointer maskImage = MaskType::New();
mitk::CastToItkImage(mask, maskImage);
double voxelVolume = 1;
for (unsigned int i = 0; i < std::min<unsigned int>(3, VImageDimension); ++i)
voxelVolume *= itkImage->GetSpacing()[i];
double voxelSpace = 1;
for (unsigned int i = 0; i < VImageDimension; ++i)
voxelSpace *= itkImage->GetSpacing()[i];
typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
minMaxComputer->SetImage(itkImage);
minMaxComputer->Compute();
double imageRange = minMaxComputer->GetMaximum() - minMaxComputer->GetMinimum();
typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
labelStatisticsImageFilter->SetInput( itkImage );
labelStatisticsImageFilter->SetLabelInput(maskImage);
labelStatisticsImageFilter->SetUseHistograms(true);
double min = params.MinimumIntensity;
double max = params.MaximumIntensity;
labelStatisticsImageFilter->SetHistogramParameters(params.Bins, min,max);
labelStatisticsImageFilter->Update();
// --------------- Range --------------------
double range = labelStatisticsImageFilter->GetMaximum(1) - labelStatisticsImageFilter->GetMinimum(1);
// --------------- Uniformity, Entropy --------------------
double count = labelStatisticsImageFilter->GetCount(1);
//double std_dev = labelStatisticsImageFilter->GetSigma(1);
double mean = labelStatisticsImageFilter->GetMean(1);
double median = labelStatisticsImageFilter->GetMedian(1);
auto histogram = labelStatisticsImageFilter->GetHistogram(1);
bool histogramIsCalculated = histogram;
HIndexType index;
index.SetSize(1);
double uniformity = 0;
double entropy = 0;
double squared_sum = 0;
double kurtosis = 0;
double mean_absolut_deviation = 0;
double median_absolut_deviation = 0;
double skewness = 0;
double sum_prob = 0;
double binWidth = 0;
double p05th = 0, p10th = 0, p15th = 0, p20th = 0, p25th = 0, p30th = 0, p35th = 0, p40th = 0, p45th = 0, p50th = 0;
double p55th = 0, p60th = 0, p65th = 0, p70th = 0, p75th = 0, p80th = 0, p85th = 0, p90th = 0, p95th = 0;
double voxelValue = 0;
if (histogramIsCalculated)
{
binWidth = histogram->GetBinMax(0, 0) - histogram->GetBinMin(0, 0);
p05th = histogram->Quantile(0, 0.05);
p10th = histogram->Quantile(0, 0.10);
p15th = histogram->Quantile(0, 0.15);
p20th = histogram->Quantile(0, 0.20);
p25th = histogram->Quantile(0, 0.25);
p30th = histogram->Quantile(0, 0.30);
p35th = histogram->Quantile(0, 0.35);
p40th = histogram->Quantile(0, 0.40);
p45th = histogram->Quantile(0, 0.45);
p50th = histogram->Quantile(0, 0.50);
p55th = histogram->Quantile(0, 0.55);
p60th = histogram->Quantile(0, 0.60);
p65th = histogram->Quantile(0, 0.65);
p70th = histogram->Quantile(0, 0.70);
p75th = histogram->Quantile(0, 0.75);
p80th = histogram->Quantile(0, 0.80);
p85th = histogram->Quantile(0, 0.85);
p90th = histogram->Quantile(0, 0.90);
p95th = histogram->Quantile(0, 0.95);
}
double Log2=log(2);
double mode_bin;
double mode_value = 0;
double variance = 0;
if (histogramIsCalculated)
{
for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
{
index[0] = i;
double prob = histogram->GetFrequency(index);
if (prob < 0.00000001)
continue;
voxelValue = histogram->GetBinMin(0, i) + binWidth * 0.5;
if (prob > mode_value)
{
mode_value = prob;
mode_bin = voxelValue;
}
sum_prob += prob;
squared_sum += prob * voxelValue*voxelValue;
prob /= count;
mean_absolut_deviation += prob* std::abs(voxelValue - mean);
median_absolut_deviation += prob* std::abs(voxelValue - median);
variance += prob * (voxelValue - mean) * (voxelValue - mean);
kurtosis += prob* (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean);
skewness += prob* (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean);
uniformity += prob*prob;
if (prob > 0)
{
entropy += prob * std::log(prob) / Log2;
}
}
}
entropy = -entropy;
double uncorrected_std_dev = std::sqrt(variance);
double rms = std::sqrt(squared_sum / count);
kurtosis = kurtosis / (variance * variance);
skewness = skewness / (variance * uncorrected_std_dev);
double coveredGrayValueRange = range / imageRange;
double coefficient_of_variation = (mean == 0) ? 0 : std::sqrt(variance) / mean;
double quantile_coefficient_of_dispersion = (p75th - p25th) / (p75th + p25th);
//Calculate the robust mean absolute deviation
//First, set all frequencies to 0 that are <10th or >90th percentile
double meanRobust = 0.0;
double robustMeanAbsoluteDeviation = 0.0;
if (histogramIsCalculated)
{
for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
{
index[0] = i;
if (histogram->GetBinMax(0, i) < p10th)
{
histogram->SetFrequencyOfIndex(index, 0);
}
else if (histogram->GetBinMin(0, i) > p90th)
{
histogram->SetFrequencyOfIndex(index, 0);
}
}
//Calculate the mean
for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
{
index[0] = i;
meanRobust += histogram->GetFrequency(index) * 0.5 * (histogram->GetBinMin(0, i) + histogram->GetBinMax(0, i));
}
meanRobust = meanRobust / histogram->GetTotalFrequency();
for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
{
index[0] = i;
robustMeanAbsoluteDeviation += std::abs(histogram->GetFrequency(index) *
((0.5 * (histogram->GetBinMin(0, i) + histogram->GetBinMax(0, i)))
- meanRobust
));
}
robustMeanAbsoluteDeviation = robustMeanAbsoluteDeviation / histogram->GetTotalFrequency();
}
featureList.push_back(std::make_pair(params.prefix + "Mean", labelStatisticsImageFilter->GetMean(1)));
featureList.push_back(std::make_pair(params.prefix + "Unbiased Variance", labelStatisticsImageFilter->GetVariance(1))); //Siehe Definition von Unbiased Variance estimation. (Wird nicht durch n sondern durch n-1 normalisiert)
featureList.push_back(std::make_pair(params.prefix + "Biased Variance", variance));
featureList.push_back(std::make_pair(params.prefix + "Skewness", skewness));
featureList.push_back(std::make_pair(params.prefix + "Kurtosis", kurtosis));
featureList.push_back(std::make_pair(params.prefix + "Median", labelStatisticsImageFilter->GetMedian(1)));
featureList.push_back(std::make_pair(params.prefix + "Minimum", labelStatisticsImageFilter->GetMinimum(1)));
featureList.push_back(std::make_pair(params.prefix + "Maximum", labelStatisticsImageFilter->GetMaximum(1)));
featureList.push_back(std::make_pair(params.prefix + "Range", range));
featureList.push_back(std::make_pair(params.prefix + "Mean Absolute Deviation", mean_absolut_deviation));
featureList.push_back(std::make_pair(params.prefix + "Robust Mean Absolute Deviation", robustMeanAbsoluteDeviation));
featureList.push_back(std::make_pair(params.prefix + "Median Absolute Deviation", median_absolut_deviation));
featureList.push_back(std::make_pair(params.prefix + "Coefficient Of Variation", coefficient_of_variation));
featureList.push_back(std::make_pair(params.prefix + "Quantile Coefficient Of Dispersion", quantile_coefficient_of_dispersion));
featureList.push_back(std::make_pair(params.prefix + "Energy", squared_sum));
featureList.push_back(std::make_pair(params.prefix + "Root Mean Square", rms));
typename HistogramType::MeasurementVectorType mv(1);
mv[0] = 0;
typename HistogramType::IndexType resultingIndex;
histogram->GetIndex(mv, resultingIndex);
featureList.push_back(std::make_pair(params.prefix + "Robust Mean", meanRobust));
featureList.push_back(std::make_pair(params.prefix + "Uniformity", uniformity));
featureList.push_back(std::make_pair(params.prefix + "Entropy", entropy));
featureList.push_back(std::make_pair(params.prefix + "Excess Kurtosis", kurtosis - 3));
featureList.push_back(std::make_pair(params.prefix + "Covered Image Intensity Range", coveredGrayValueRange));
featureList.push_back(std::make_pair(params.prefix + "Sum", labelStatisticsImageFilter->GetSum(1)));
featureList.push_back(std::make_pair(params.prefix + "Mode", mode_bin));
featureList.push_back(std::make_pair(params.prefix + "Mode Probability", mode_value));
featureList.push_back(std::make_pair(params.prefix + "Unbiased Standard deviation", labelStatisticsImageFilter->GetSigma(1)));
featureList.push_back(std::make_pair(params.prefix + "Biased Standard deviation", sqrt(variance)));
featureList.push_back(std::make_pair(params.prefix + "Number Of Voxels", labelStatisticsImageFilter->GetCount(1)));
featureList.push_back(std::make_pair(params.prefix + "05th Percentile", p05th));
featureList.push_back(std::make_pair(params.prefix + "10th Percentile", p10th));
featureList.push_back(std::make_pair(params.prefix + "15th Percentile", p15th));
featureList.push_back(std::make_pair(params.prefix + "20th Percentile", p20th));
featureList.push_back(std::make_pair(params.prefix + "25th Percentile", p25th));
featureList.push_back(std::make_pair(params.prefix + "30th Percentile", p30th));
featureList.push_back(std::make_pair(params.prefix + "35th Percentile", p35th));
featureList.push_back(std::make_pair(params.prefix + "40th Percentile", p40th));
featureList.push_back(std::make_pair(params.prefix + "45th Percentile", p45th));
featureList.push_back(std::make_pair(params.prefix + "50th Percentile", p50th));
featureList.push_back(std::make_pair(params.prefix + "55th Percentile", p55th));
featureList.push_back(std::make_pair(params.prefix + "60th Percentile", p60th));
featureList.push_back(std::make_pair(params.prefix + "65th Percentile", p65th));
featureList.push_back(std::make_pair(params.prefix + "70th Percentile", p70th));
featureList.push_back(std::make_pair(params.prefix + "75th Percentile", p75th));
featureList.push_back(std::make_pair(params.prefix + "80th Percentile", p80th));
featureList.push_back(std::make_pair(params.prefix + "85th Percentile", p85th));
featureList.push_back(std::make_pair(params.prefix + "90th Percentile", p90th));
featureList.push_back(std::make_pair(params.prefix + "95th Percentile", p95th));
featureList.push_back(std::make_pair(params.prefix + "Interquartile Range", (p75th - p25th)));
featureList.push_back(std::make_pair(params.prefix + "Image Dimension", VImageDimension));
featureList.push_back(std::make_pair(params.prefix + "Voxel Space", voxelSpace));
featureList.push_back(std::make_pair(params.prefix + "Voxel Volume", voxelVolume));
}