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ImageSetStatistics.cxx
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/*=========================================================================
Program: Advanced Normalization Tools
Copriyght (c) ConsortiumOfANTS. All rights reserved.
See accompanying COPYING.txt or
https://github.com/stnava/ANTs/blob/master/ANTSCopyright.txt 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.
=========================================================================*/
#include "antsUtilities.h"
#include <algorithm>
#include <vector>
#include <cstdlib>
#include <ctime>
#include <iostream>
#include "ReadWriteData.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkHistogramMatchingImageFilter.h"
#include "itkMinimumMaximumImageFilter.h"
#include "itkConnectedComponentImageFilter.h"
#include "itkRelabelComponentImageFilter.h"
#include "itkLabelStatisticsImageFilter.h"
#include "itkNeighborhoodIterator.h"
// RecursiveAverageImages img1 img2 weight
// We divide the 2nd input image by its mean and add it to the first
// input image with weight 1/n.
// The output overwrites the 1st img with the sum.
// Note: could easily add variance computation
// http://people.revoledu.com/kardi/tutorial/RecursiveStatistic/Time-Variance.htm
#include "itkDiscreteGaussianImageFilter.h"
namespace ants
{
template <class TImageType>
void ReadImage(itk::SmartPointer<TImageType> & target, const char *file, bool copy)
{
// std::cout << " reading b " << std::string(file) << std::endl;
typedef itk::ImageFileReader<TImageType> readertype;
typename readertype::Pointer reader = readertype::New();
reader->SetFileName(file);
reader->Update();
if( !copy )
{
target = (reader->GetOutput() );
}
else
{
typedef itk::ImageRegionIteratorWithIndex<TImageType> Iterator;
Iterator vfIter2( target, target->GetLargestPossibleRegion() );
for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 )
{
vfIter2.Set( reader->GetOutput()->GetPixel(vfIter2.GetIndex() ) );
}
}
}
double TProb(double t, int df)
{
if( t == 0 )
{
return 0;
}
double a = 0.36338023;
double w = atan(t / sqrt( (double)df) );
double s = sin(w);
double c = cos(w);
double t1, t2;
int j1, j2, k2;
if( df % 2 == 0 ) // even
{
t1 = s;
if( df == 2 ) // special case df=2
{
return 0.5 * (1 + t1);
}
t2 = s;
j1 = -1;
j2 = 0;
k2 = (df - 2) / 2;
}
else
{
t1 = w;
if( df == 1 ) // special case df=1
{
return 1 - (0.5 * (1 + (t1 * (1 - a) ) ) );
}
t2 = s * c;
t1 = t1 + t2;
if( df == 3 ) // special case df=3
{
return 1 - (0.5 * (1 + (t1 * (1 - a) ) ) );
}
j1 = 0;
j2 = 1;
k2 = (df - 3) / 2;
}
for( int i = 1; i >= k2; i++ )
{
j1 = j1 + 2;
j2 = j2 + 2;
t2 = t2 * c * c * j1 / j2;
t1 = t1 + t2;
}
return 1 - (0.5 * (1 + (t1 * (1 - a * (df % 2) ) ) ) );
}
template <class TImage>
typename TImage::Pointer
SmoothImage(typename TImage::Pointer image, float sig)
{
typedef itk::DiscreteGaussianImageFilter<TImage, TImage> dgf;
typename dgf::Pointer filter = dgf::New();
filter->SetVariance(sig);
filter->SetUseImageSpacingOn();
filter->SetMaximumError(.01f);
filter->SetInput(image);
filter->Update();
return filter->GetOutput();
}
template <class TInputImage>
// typename TInputImage::Pointer
void
HistogramMatch(typename TInputImage::Pointer m_InputFixedImage, typename TInputImage::Pointer m_InputMovingImage)
{
std::cout << " MATCHING INTENSITIES " << std::endl;
typedef itk::HistogramMatchingImageFilter<TInputImage, TInputImage> FilterType;
typename FilterType::Pointer filter = FilterType::New();
filter->SetInput( m_InputMovingImage );
filter->SetReferenceImage( m_InputFixedImage );
filter->SetNumberOfHistogramLevels( 256 );
filter->SetNumberOfMatchPoints( 10 );
filter->ThresholdAtMeanIntensityOn();
filter->ThresholdAtMeanIntensityOff();
filter->Update();
typename TInputImage::Pointer img = filter->GetOutput();
typedef itk::ImageRegionIteratorWithIndex<TInputImage> Iterator;
Iterator vfIter( img, img->GetLargestPossibleRegion() );
for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )
{
m_InputMovingImage->SetPixel(vfIter.GetIndex(), vfIter.Get() );
}
return;
}
template <class TImage>
void
LocalMean(typename TImage::Pointer image, unsigned int nhood, typename TImage::Pointer meanimage )
{
typename TImage::Pointer localmean = MakeNewImage<TImage>(image, 0);
typedef itk::ImageRegionIteratorWithIndex<TImage> Iterator;
Iterator outIter(image, image->GetLargestPossibleRegion() );
typename TImage::SizeType imagesize = image->GetLargestPossibleRegion().GetSize();
const unsigned int ImageDimension = 3;
typedef itk::NeighborhoodIterator<TImage> iteratorType;
typename iteratorType::RadiusType rad;
for( unsigned int j = 0; j < ImageDimension; j++ )
{
rad[j] = nhood;
}
for( outIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter )
{
itk::NeighborhoodIterator<TImage> hoodIt( rad, image, image->GetLargestPossibleRegion() );
typename TImage::IndexType oindex = outIter.GetIndex();
hoodIt.SetLocation(oindex);
double fixedMean = 0;
// double movingMean=0;
unsigned int hoodlen = hoodIt.Size();
// unsigned int inct=0;
bool takesample = true;
// double sumj=0;
// double sumi=0;
if( takesample )
{
// double sumj=0;
double sumi = 0;
unsigned int cter = 0;
for( unsigned int indct = 0; indct < hoodlen; indct++ )
{
typename TImage::IndexType index = hoodIt.GetIndex(indct);
bool inimage = true;
for( unsigned int dd = 0; dd < ImageDimension; dd++ )
{
if( index[dd] < 0 || index[dd] > static_cast<typename TImage::IndexType::IndexValueType>(imagesize[dd] - 1) )
{
inimage = false;
}
}
if( inimage )
{
sumi += image->GetPixel(index);
cter++;
}
}
if( cter > 0 )
{
fixedMean = sumi / (float)cter;
}
}
float val = image->GetPixel(oindex) - fixedMean;
meanimage->SetPixel( oindex, meanimage->GetPixel(oindex) + fixedMean);
localmean->SetPixel( oindex, val );
}
typedef itk::ImageRegionIteratorWithIndex<TImage> Iterator;
Iterator vfIter( image, image->GetLargestPossibleRegion() );
for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )
{
vfIter.Set( localmean->GetPixel( vfIter.GetIndex() ) );
}
return; // localmean;
}
template <class TImage>
// std::vector<unsigned int>
float
GetClusterStat(typename TImage::Pointer image, float Tthreshold, unsigned int minSize, unsigned int whichstat,
std::string outfn, bool TRUTH)
{
typedef float InternalPixelType;
typedef TImage InternalImageType;
typedef TImage OutputImageType;
typedef itk::BinaryThresholdImageFilter<InternalImageType, InternalImageType> ThresholdFilterType;
typedef itk::ConnectedComponentImageFilter<InternalImageType, OutputImageType> FilterType;
typedef itk::RelabelComponentImageFilter<OutputImageType, OutputImageType> RelabelType;
typename ThresholdFilterType::Pointer threshold = ThresholdFilterType::New();
typename FilterType::Pointer filter = FilterType::New();
typename RelabelType::Pointer relabel = RelabelType::New();
InternalPixelType threshold_low, threshold_hi;
threshold_low = Tthreshold;
threshold_hi = 1.e9;
threshold->SetInput(image);
threshold->SetInsideValue(itk::NumericTraits<InternalPixelType>::OneValue());
threshold->SetOutsideValue(itk::NumericTraits<InternalPixelType>::ZeroValue());
threshold->SetLowerThreshold(threshold_low);
threshold->SetUpperThreshold(threshold_hi);
threshold->Update();
filter->SetInput(threshold->GetOutput() );
// if (argc > 5)
{
int fullyConnected = 1; // atoi( argv[5] );
filter->SetFullyConnected( fullyConnected );
}
relabel->SetInput( filter->GetOutput() );
relabel->SetMinimumObjectSize( minSize );
// relabel->SetUseHistograms(true);
try
{
relabel->Update();
}
catch( itk::ExceptionObject & excep )
{
std::cout << "Relabel: exception caught !" << std::endl;
std::cout << excep << std::endl;
}
typename TImage::Pointer Clusters = MakeNewImage<TImage>(relabel->GetOutput(), 0);
// typename TImage::Pointer Clusters=relabel->GetOutput();
typedef itk::ImageRegionIteratorWithIndex<TImage> Iterator;
Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() );
/*
typename itk::LabelStatisticsImageFilter<TImage,TImage>::Pointer labstat=
itk::LabelStatisticsImageFilter<TImage,TImage>::New();
labstat->SetInput(image);
labstat->SetLabelImage(Clusters);
labstat->SetUseHistograms(true);
labstat->Update();
typedef itk::ImageRegionIteratorWithIndex<TImage> Iterator;
Iterator vfIter( Clusters, Clusters->GetLargestPossibleRegion() );
float maximum=0;
// Relabel the Clusters image with the right statistic
for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )
{
if (relabel->GetOutput()->GetPixel(vfIter.GetIndex()) > 0 )
{
float pix = relabel->GetOutput()->GetPixel(vfIter.GetIndex());
float val;
if (whichstat == 0) val=pix;
else if (whichstat == 1) val = labstat->GetSum(pix);
else if (whichstat == 2) val = labstat->GetMean(pix);
else if (whichstat == 3) val = labstat->GetMaximum(pix);
if (val > maximum) maximum=val;
vfIter.Set(val);
}
}
*/
float maximum = relabel->GetNumberOfObjects();
float maxtstat = 0;
std::vector<unsigned int> histogram( (int)maximum + 1);
std::vector<float> clustersum( (int)maximum + 1);
for( int i = 0; i <= maximum; i++ )
{
histogram[i] = 0;
clustersum[i] = 0;
}
for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )
{
if( vfIter.Get() > 0 )
{
float vox = image->GetPixel(vfIter.GetIndex() );
histogram[(unsigned int)vfIter.Get()] = histogram[(unsigned int)vfIter.Get()] + 1;
clustersum[(unsigned int)vfIter.Get()] += vox;
if( vox > maxtstat )
{
maxtstat = vox;
}
}
}
for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )
{
if( vfIter.Get() > 0 )
{
if( whichstat == 0 ) // size
{
Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned int)vfIter.Get()] );
}
if( whichstat == 1 ) // sum
{
Clusters->SetPixel( vfIter.GetIndex(), clustersum[(unsigned int)vfIter.Get()] );
}
if( whichstat == 2 ) // mean
{
Clusters->SetPixel( vfIter.GetIndex(), clustersum[(unsigned int)vfIter.Get()]
/ (float)histogram[(unsigned int)vfIter.Get()] );
}
if( whichstat == 3 ) // max
{
Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned int)vfIter.Get()] );
}
}
else
{
Clusters->SetPixel(vfIter.GetIndex(), 0);
}
}
// for (int i=0; i<=maximum; i++)
// std::cout << " label " << i << " ct is: " << histogram[i] << std::endl;
if( TRUTH )
{
typedef itk::ImageFileWriter<InternalImageType> writertype;
typename writertype::Pointer writer = writertype::New();
writer->SetFileName( (outfn + std::string("Clusters.nii") ).c_str() );
writer->SetInput( Clusters );
writer->Write();
}
if( whichstat == 0 )
{
return histogram[1];
}
else if( whichstat == 1 )
{
float mx = 0;
for( int i = 1; i <= maximum; i++ )
{
if( clustersum[i] > mx )
{
mx = clustersum[i];
}
}
return mx;
}
else if( whichstat == 2 )
{
float mx = 0;
for( int i = 1; i <= maximum; i++ )
{
if( clustersum[i] / (float)histogram[i] > mx )
{
mx = clustersum[i] / (float)histogram[i] * 1000.0;
}
}
return mx;
}
else if( whichstat == 3 )
{
return maxtstat * 1000.0;
}
else
{
return histogram[1];
}
}
float median(std::vector<float> vec)
{
typedef std::vector<float>::size_type vec_sz;
vec_sz size = vec.size();
if( size == 0 )
{
return 0;
}
// throw domain_error("median of an empty vector");
sort(vec.begin(), vec.end() );
vec_sz mid = size / 2;
return size % 2 == 0
? (vec[mid] + vec[mid - 1]) / 2
: vec[mid];
}
float npdf(std::vector<float> vec, bool opt, float www)
{
typedef std::vector<float>::size_type vec_sz;
vec_sz size = vec.size();
if( size == 0 )
{
return 0;
}
// throw domain_error("median of an empty vector");
float mean = 0, var = 0;
float max = -1.e9, min = 1.e9;
for( unsigned int i = 0; i < size; i++ )
{
float val = vec[i];
if( val > max )
{
max = val;
}
else if( val < min )
{
min = val;
}
float n = (float) (i + 1);
float wt1 = 1.0 / (float)n;
float wt2 = 1.0 - wt1;
mean = mean * wt2 + val * wt1;
if( i > 0 )
{
float wt3 = 1.0 / ( (float) n - 1.0 );
var = var * wt2 + ( val - mean ) * ( val - mean) * wt3;
}
}
if( var == 0 )
{
return mean;
}
// else std::cout << " Mean " << mean << " var " << var << std::endl;
// eval parzen probability
std::vector<float> prob(size);
float maxprob = 0;
// float maxprobval=0;
float weightedmean = 0;
float weighttotal = 0;
unsigned int maxprobind = 0;
// float sample=0.0;
float width;
if( www > 0 )
{
width = www;
}
else
{
width = sqrt(var) / 2.0;
}
// std::cout << " using width " << width << std::endl;
// float N=(float)size;
for( unsigned int j = 0; j < size; j++ )
{
float sample = vec[j];
float total = 0.0;
for( unsigned int i = 0; i < size; i++ )
{
float delt = vec[i] - sample;
delt *= delt;
prob[i] = 1.0 / (2.0 * 3.1214 * width) * exp(-0.5 * delt / (width * width) );
total += prob[i];
// maxprobval+=prob[i]
}
if( total > maxprob )
{
maxprob = total; maxprobind = j;
}
weightedmean += sample * total;
weighttotal += total;
// for (unsigned int i=0; i<size; i++) prob[i]=prob[i]/total;
}
weightedmean /= weighttotal;
// pxa = 1./N * total ( gaussian )
maxprob = vec[maxprobind];
if( opt )
{
return maxprob;
}
else
{
return weightedmean; // vec[maxprobind];
}
}
float trimmean(std::vector<float> vec)
{
typedef std::vector<float>::size_type vec_sz;
vec_sz size = vec.size();
if( size == 0 )
{
return 0;
}
// throw domain_error("median of an empty vector");
sort(vec.begin(), vec.end() );
const unsigned int lo = 0;
const unsigned int hi = size;
const unsigned int ct = hi - lo;
float total = 0;
for( unsigned int i = lo; i < hi; i++ )
{
total += vec[i];
}
return total / (float)ct;
}
float myantsmax(std::vector<float> vec)
{
typedef std::vector<float>::size_type vec_sz;
vec_sz size = vec.size();
if( size == 0 )
{
return 0;
}
float max = -1.e9;
for( unsigned int i = 0; i < size; i++ )
{
float val = vec[i];
if( val > max )
{
max = val;
}
}
return max;
}
float myantssimilaritymaxlabel(std::vector<float> labelvec, std::vector<float> similarityvec, bool opt)
{
typedef std::vector<float>::size_type vec_sz;
vec_sz size = labelvec.size();
if( size == 0 )
{
return 0;
}
unsigned int max = 0;
float maxsim = -1.e9;
float totalsim = 0;
for( unsigned int i = 0; i < size; i++ )
{
totalsim += similarityvec[i];
}
if( fabs(totalsim) <= 0 )
{
return 0;
}
for( unsigned int i = 0; i < size; i++ )
{
float simval = similarityvec[i];
if( simval > maxsim )
{
maxsim = simval; max = i;
}
}
if( opt == true )
{
return labelvec[max];
}
else
{
return max;
}
}
template <unsigned int ImageDimension>
int ImageSetStatistics(int argc, char *argv[])
{
typedef float PixelType;
typedef itk::Image<PixelType, ImageDimension> ImageType;
typedef itk::ImageFileReader<ImageType> readertype;
typedef typename ImageType::IndexType IndexType;
typedef itk::ImageRegionIteratorWithIndex<ImageType> Iterator;
unsigned int mch = 0;
int argct = 2;
std::string fn1 = std::string(argv[argct]); argct++;
std::string outfn = std::string(argv[argct]); argct++;
unsigned int whichstat = atoi(argv[argct]); argct++;
std::string roifn = "";
if( argc > argct )
{
roifn = std::string(argv[argct]); argct++;
}
std::string simimagelist = std::string("");
if( argc > argct )
{
simimagelist = std::string(argv[argct]); argct++;
}
float www = 0;
// if (argc > argct) { www=atof(argv[argct]);argct++;}
// unsigned int mchmax= 0;
// if (argc > argct) { mchmax=atoi(argv[argct]); argct++;}
unsigned int localmeanrad = 0;
// if (argc > argct) { localmeanrad=atoi(argv[argct]);argct++;}
// std::cout <<" roifn " << roifn << " fn1 " << fn1 << " whichstat " << whichstat << std::endl;
typename ImageType::Pointer outimage = ITK_NULLPTR;
typename ImageType::Pointer ROIimg = ITK_NULLPTR;
if( roifn.length() > 4 )
{
std::cout << " reading roi image " << roifn << std::endl;
typename readertype::Pointer reader2 = readertype::New();
reader2->SetFileName(roifn.c_str() );
reader2->UpdateLargestPossibleRegion();
try
{
ROIimg = reader2->GetOutput();
}
catch( ... )
{
ROIimg = ITK_NULLPTR;
std::cout << " Error reading ROI image " << std::endl;
// return 0;
}
}
// now do the recursive average
const unsigned int maxChar = 512;
char lineBuffer[maxChar];
char filenm[maxChar];
unsigned int filecount1 = 0;
{
std::ifstream inputStreamA( fn1.c_str(), std::ios::in );
if( !inputStreamA.is_open() )
{
std::cout << "Can't open parameter file: " << fn1 << std::endl;
return EXIT_FAILURE;
}
while( !inputStreamA.eof() )
{
inputStreamA.getline( lineBuffer, maxChar, '\n' );
if( sscanf( lineBuffer, "%s ", filenm) != 1 )
{
// std::cout << "Done. read " << lineBuffer << " n " << ct1 << " files " << std::endl;
// std::cout << std::endl;
continue;
}
else
{
filecount1++;
}
}
inputStreamA.close();
}
std::cout << " NFiles1 " << filecount1 << std::endl;
unsigned int filecount2 = 0;
if( simimagelist.length() > 2 && ( whichstat == 5 || whichstat == 6 ) )
{
std::ifstream inputStreamA( simimagelist.c_str(), std::ios::in );
if( !inputStreamA.is_open() )
{
std::cout << "Can't open parameter file: " << fn1 << std::endl;
return EXIT_FAILURE;
}
while( !inputStreamA.eof() )
{
inputStreamA.getline( lineBuffer, maxChar, '\n' );
if( sscanf( lineBuffer, "%s ", filenm) != 1 )
{
// std::cout << "Done. read " << lineBuffer << " n " << ct1 << " files " << std::endl;
// std::cout << std::endl;
continue;
}
else
{
filecount2++;
}
}
inputStreamA.close();
if( filecount1 != filecount2 )
{
std::cout
<<
" the number of similarity images does not match the number of label images --- thus, we have to get out of here !! i.e. something's wrong. "
<< std::endl;
return EXIT_FAILURE;
}
} // fi simimagelist
std::cout << " NFiles2 " << filecount2 << std::endl;
typename ImageType::Pointer meanimage;
std::vector<typename ImageType::Pointer> imagestack;
imagestack.resize(filecount1);
// imagestack.fill(NULL);
std::vector<std::string> filenames(filecount1);
typename ImageType::Pointer StatImage;
unsigned int ct = 0;
std::ifstream inputStreamA( fn1.c_str(), std::ios::in );
if( !inputStreamA.is_open() )
{
std::cout << "Can't open parameter file: " << fn1 << std::endl;
return EXIT_FAILURE;
}
while( !inputStreamA.eof() )
{
inputStreamA.getline( lineBuffer, maxChar, '\n' );
if( sscanf( lineBuffer, "%s ", filenm) != 1 )
{
// std::cout << "Done. read " << lineBuffer << " n " << ct1 << " files " << std::endl;
// std::cout << std::endl;
continue;
}
else
{
filenames[ct] = std::string(filenm);
ReadImage<ImageType>(imagestack[ct], filenm, false);
if( ct == 0 )
{
meanimage = MakeNewImage<ImageType>(imagestack[ct], 0);
}
if( localmeanrad > 0 )
{
LocalMean<ImageType>(imagestack[ct], localmeanrad, meanimage);
}
std::cout << " done reading " << (float) ct / (float ) filecount1 << std::endl;
ct++;
}
}
inputStreamA.close();
// read similarity images, if needed
std::vector<typename ImageType::Pointer> simimagestack;
simimagestack.resize(filecount2);
ct = 0;
if( simimagelist.length() > 2 && ( whichstat == 5 || whichstat == 6 ) )
{
inputStreamA.open( simimagelist.c_str() );
if( !inputStreamA.is_open() )
{
std::cout << "Can't open parameter file: " << fn1 << std::endl;
return -1;
}
while( !inputStreamA.eof() )
{
inputStreamA.getline( lineBuffer, maxChar, '\n' );
if( sscanf( lineBuffer, "%s ", filenm) != 1 )
{
continue;
}
else
{
ReadImage<ImageType>(simimagestack[ct], filenm, false);
ct++;
}
}
inputStreamA.close();
} // fi read similarity images
ReadImage<ImageType>( StatImage, filenames[0].c_str(), false);
Iterator vfIter(StatImage, StatImage->GetLargestPossibleRegion() );
std::vector<float> voxels(filecount1);
std::vector<float> similarities(filecount2);
unsigned long nvox = 1;
for( unsigned int i = 0; i < ImageDimension; i++ )
{
nvox *= StatImage->GetLargestPossibleRegion().GetSize()[i];
}
ct = 0;
unsigned long prog = nvox / 15;
for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )
{
if( ct % prog == 0 )
{
std::cout << " % " << (float) ct / (float) nvox << std::endl;
}
ct++;
IndexType ind = vfIter.GetIndex();
unsigned int maxval = 0;
bool takesample = true;
if( ROIimg )
{
if( ROIimg->GetPixel(ind) < 0.5 )
{
takesample = false;
}
else
{
maxval = (unsigned int)(ROIimg->GetPixel(ind) - 1);
}
}
if( takesample )
{
if( mch == 0 )
{
meanimage->SetPixel(ind, meanimage->GetPixel(ind) / filecount1 );
}
for( unsigned int j = 0; j < filecount1; j++ )
{
voxels[j] = imagestack[j]->GetPixel(ind);
}
for( unsigned int j = 0; j < filecount2; j++ )
{
similarities[j] = simimagestack[j]->GetPixel(ind);
}
float stat = 0;
switch( whichstat )
{
case 1:
{
stat = npdf(voxels, true, www);
if( ct == 1 )
{
std::cout << "the max prob appearance \n";
}
}
break;
case 2:
{
stat = npdf(voxels, false, www);
if( ct == 1 )
{
std::cout << "the probabilistically weighted appearance " << www << " \n";
}
}
break;
case 3:
{
stat = trimmean(voxels);
if( ct == 1 )
{
std::cout << "the trimmed mean appearance \n";
}
}
break;
case 4:
{
stat = myantsmax(voxels);
if( ct == 1 )
{
std::cout << "the maximum appearance \n";
}
}
break;
case 5:
{
stat = myantssimilaritymaxlabel(voxels, similarities, true);
if( ct == 1 )
{
std::cout << "the maximum similarity-based label \n";
}
}
break;
case 6:
{
stat = myantssimilaritymaxlabel(voxels, similarities, false);
if( ct == 1 )
{
std::cout << "which image provides the maximum similarity-based label \n";
}
}
break;
case 7:
{
stat = voxels[maxval];
if( ct == 1 )
{
std::cout << "which image provides the maximum similarity-based label \n";
}
}
break;
default:
{
stat = median(voxels);
if( ct == 1 )
{
std::cout << "the median appearance \n";
}
}
break;
}
float sval = stat;
if( localmeanrad > 0 )
{
sval += meanimage->GetPixel(ind);
}
StatImage->SetPixel(ind, sval);
}
else
{
StatImage->SetPixel(ind, 0);
}
}
WriteImage<ImageType>(StatImage, outfn.c_str() );
std::cout << " Done " << std::endl;
return EXIT_SUCCESS;
}
// entry point for the library; parameter 'args' is equivalent to 'argv' in (argc,argv) of commandline parameters to
// 'main()'
int ImageSetStatistics( std::vector<std::string> args, std::ostream* /*out_stream = NULL */ )
{
// put the arguments coming in as 'args' into standard (argc,argv) format;
// 'args' doesn't have the command name as first, argument, so add it manually;
// 'args' may have adjacent arguments concatenated into one argument,
// which the parser should handle
args.insert( args.begin(), "ImageSetStatistics" );
const int argc = args.size();
char* * argv = new char *[args.size() + 1];
for( unsigned int i = 0; i < args.size(); ++i )
{
// allocate space for the string plus a null character
argv[i] = new char[args[i].length() + 1];
std::strncpy( argv[i], args[i].c_str(), args[i].length() );
// place the null character in the end
argv[i][args[i].length()] = '\0';
}
argv[argc] = ITK_NULLPTR;
// class to automatically cleanup argv upon destruction
class Cleanup_argv
{
public:
Cleanup_argv( char* * argv_, int argc_plus_one_ ) : argv( argv_ ), argc_plus_one( argc_plus_one_ )
{
}
~Cleanup_argv()
{
for( unsigned int i = 0; i < argc_plus_one; ++i )
{
delete[] argv[i];
}
delete[] argv;
}
private:
char* * argv;
unsigned int argc_plus_one;