// This function is designed to compute the optimal threshold using OTSU method;
// this algoritm is implemented by xiao liang based on ITK's OTSU algorithm
double VolumeProcess::getXiaoLiangOtsuThreshold(ImageType::Pointer img)
{
if(!img)
return 0;
double threshold = 0;
unsigned char m_min = 255;
unsigned char m_max = 0;
double m_mean = 0.0;
double m_variance = 0.0;
ImageType::RegionType region = img->GetBufferedRegion();
double numPix = region.GetSize(2)*region.GetSize(1)*region.GetSize(0);
//Get min, max, and mean:
itk::ImageRegionIterator< ImageType > itr( img, region );
for(itr.GoToBegin(); !itr.IsAtEnd(); ++itr)
{
double val = itr.Get();
if(val > m_max) m_max = val;
if(val < m_min) m_min = val;
m_mean += itr.Get();
}
m_mean = m_mean/numPix;
if(debug)
std::cerr << "Max = " << (int)m_max << ", Min = " << (int)m_min << std::endl;
//Do a sanity check
if ( m_min >= m_max)
{
threshold=m_min;
return threshold;
}
//Get the variance:
for(itr.GoToBegin(); !itr.IsAtEnd(); ++itr)
{
double val = (double)itr.Get();
m_variance += (val-m_mean)*(val-m_mean);
}
//These were not Xiao Liang's version:
//m_variance = m_variance / numPix;
//m_variance = sqrt(m_variance);
threshold = m_mean - (m_variance/30);
// this step is only initialized a good experimental value for m_Threshold, because the 3D image
// is sparse, there are lots of zero values;
//Create a histogram & init to zero
double relativeFrequency[m_NumberOfHistogramBins];
for ( unsigned char j = 0; j < m_NumberOfHistogramBins; j++ )
{
relativeFrequency[j] = 0.0;
}
double binMultiplier = (double)m_NumberOfHistogramBins/(double)(m_max-m_min);
if(debug)
std::cerr << "binMultiplier = " << binMultiplier << std::endl;
unsigned int binNumber;
for(itr.GoToBegin(); !itr.IsAtEnd(); ++itr)
{
double val = itr.Get();
if ( val == m_min )
{
binNumber = 0;
}
else
{
binNumber = (unsigned int)(((val-m_min)*binMultiplier) - 1);
if ( binNumber == m_NumberOfHistogramBins ) // in case of rounding errors
{
binNumber -= 1;
}
}
relativeFrequency[binNumber] += 1.0;
}
// normalize the frequencies
double totalMean = 0.0;
for ( unsigned char j = 0; j < m_NumberOfHistogramBins; j++ )
{
relativeFrequency[j] /= numPix;
totalMean += (j+1) * relativeFrequency[j];
}
// compute Otsu's threshold by maximizing the between-class variance
double freqLeft = relativeFrequency[0];
double meanLeft = 1.0;
double meanRight = ( totalMean - freqLeft ) / ( 1.0 - freqLeft );
double maxVarBetween = freqLeft * ( 1.0 - freqLeft ) * sqrt( meanLeft - meanRight );
int maxBinNumber = 0;
double freqLeftOld = freqLeft;
double meanLeftOld = meanLeft;
for ( unsigned char j = 1; j < m_NumberOfHistogramBins; j++ )
{
freqLeft += relativeFrequency[j];
meanLeft = ( ((meanLeftOld * freqLeftOld)+(j+1)) * relativeFrequency[j] ) / freqLeft;
if (freqLeft == 1.0)
{
meanRight = 0.0;
}
else
{
meanRight = ( totalMean - meanLeft * freqLeft ) / ( 1.0 - freqLeft );
}
double varBetween = freqLeft * ( 1.0 - freqLeft ) * sqrt( meanLeft - meanRight );
if ( varBetween > maxVarBetween )
{
maxVarBetween = varBetween;
maxBinNumber = j;
}
// cache old values
freqLeftOld = freqLeft;
meanLeftOld = meanLeft;
}
threshold = double( m_min + ( maxBinNumber + 1 ) / binMultiplier );
return threshold;
}
bool VolumeProcess::DialateImage(int iterations)
{
int border = 2;
ImageType::Pointer tempImg = ImageType::New();
tempImg->SetRegions( m_outputImage->GetLargestPossibleRegion() );
tempImg->Allocate();
tempImg->FillBuffer(0);
ImageType::RegionType fullRegion = m_outputImage->GetBufferedRegion();
ImageType::RegionType borderRegion;
borderRegion.SetIndex(0, fullRegion.GetIndex(0)+border);
borderRegion.SetIndex(1, fullRegion.GetIndex(1)+border);
borderRegion.SetIndex(2, fullRegion.GetIndex(2)+border);
borderRegion.SetSize(0, fullRegion.GetSize(0)-(2*border));
borderRegion.SetSize(1, fullRegion.GetSize(1)-(2*border));
borderRegion.SetSize(2, fullRegion.GetSize(2)-(2*border));
itk::ImageRegionIteratorWithIndex< ImageType > itr( m_outputImage, borderRegion );
while(iterations > 0)
{
for(itr.GoToBegin(); !itr.IsAtEnd(); ++itr)
{
double blockMax = itr.Get();
for(int k=-1; k<=1; k++)
{
for(int j=-1; j<=1; j++)
{
for(int i=-1; i<=1; i++)
{
ImageType::IndexType index = itr.GetIndex();
index[0] += i;
index[1] += j;
index[2] += k;
ImageType::PixelType pix = m_outputImage->GetPixel(index);
if((double)pix > blockMax)
{
blockMax = (double)pix;
}
}
}
}
// Keep the peak of the original intensity
if (blockMax == itr.Get() && blockMax != 0)
{
blockMax = blockMax + 1;
}
tempImg->SetPixel(itr.GetIndex(), blockMax);
}
//Copy temp img back to image for next dialation
itk::ImageRegionIterator< ImageType > itr1( tempImg, tempImg->GetLargestPossibleRegion() );
itk::ImageRegionIterator< ImageType > itr2( m_outputImage, m_outputImage->GetLargestPossibleRegion() );
for(itr1.GoToBegin(), itr2.GoToBegin() ; !itr1.IsAtEnd(); ++itr1, ++itr2)
{
itr2.Set( itr1.Get() );
}
iterations--;
}
if(debug)
std::cerr << "Dialation Done" << std::endl;
return true;
}
