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/*
//
// Copyright 1997-2009 Torsten Rohlfing
//
// Copyright 2004-2011, 2014 SRI International
//
// This file is part of the Computational Morphometry Toolkit.
//
// http://www.nitrc.org/projects/cmtk/
//
// The Computational Morphometry Toolkit is free software: you can
// redistribute it and/or modify it under the terms of the GNU General Public
// License as published by the Free Software Foundation, either version 3 of
// the License, or (at your option) any later version.
//
// The Computational Morphometry Toolkit is distributed in the hope that it
// will be useful, but WITHOUT ANY WARRANTY; without even the implied
// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License along
// with the Computational Morphometry Toolkit. If not, see
// <http://www.gnu.org/licenses/>.
//
// $Revision: 5436 $
//
// $LastChangedDate: 2018-12-10 19:01:20 -0800 (Mon, 10 Dec 2018) $
//
// $LastChangedBy: torstenrohlfing $
//
*/
#include "cmtkLabelCombinationSTAPLE.h"
#ifdef CMTK_USE_GCD
# include <System/cmtkThreads.h>
# include <dispatch/dispatch.h>
#endif
namespace
cmtk
{
/** \addtogroup Segmentation */
//@{
LabelCombinationSTAPLE::LabelCombinationSTAPLE( const std::vector<TypedArray::SmartPtr>& data, const int maxIterations, const ScalarDataType resultType )
{
const size_t numberOfInputs = data.size();
const size_t numberOfPixels = data[ 0 ]->GetDataSize();
this->m_Result = TypedArray::SmartPtr( TypedArray::Create( resultType, numberOfPixels ) );
this->m_Result->SetDataClass( DATACLASS_LABEL );
// compute initial estimate as the average of all inputs;
// this is also the first E-step with all p/q equal to 0.5
double totalSum = 0;
// The following is currently broken due to Apple bug:
// http://forums.macrumors.com/showthread.php?t=952857
// http://lists.apple.com/archives/perfoptimization-dev/2009/Sep/msg00043.html
//#ifdef CMTK_USE_GCD
// const cmtk::Threads::Stride stride( numberOfPixels );
// float* threadSum = new float[stride.NBlocks()];
// dispatch_apply( stride.NBlocks(), dispatch_get_global_queue(0, 0), ^(size_t b)
// { for ( size_t numberOfPixels = stride.From( b ); numberOfPixels < stride.To( b ); ++numberOfPixels )
//#else
#pragma omp parallel for reduction(+:totalSum)
for ( int n = 0; n < static_cast<int>( numberOfPixels ); ++n )
//#endif
{
Types::DataItem w = 0;
for ( size_t i = 0; i < numberOfInputs; ++i )
{
Types::DataItem value;
if ( data[i]->Get( value, n ) )
{
w += value;
totalSum += value;
}
}
this->m_Result->Set( w / numberOfInputs, n );
}
//#ifdef CMTK_USE_GCD
// });
//#endif
// global prior probability
const double globalPrior = totalSum / (numberOfInputs * numberOfPixels );
// expert parameters
this->m_VecP.resize( numberOfInputs );
this->m_VecQ.resize( numberOfInputs );
// iterate
for ( int it = 0; it < maxIterations; ++it )
{
// M-step
for ( size_t i = 0; i < numberOfInputs; ++i )
{
this->m_VecP[i] = this->m_VecQ[i] = 0;
}
double sumW = 0;
for ( size_t n = 0; n < numberOfPixels; ++n )
{
Types::DataItem w;
this->m_Result->Get( w, n );
sumW += w;
for ( size_t i = 0; i < numberOfInputs; ++i )
{
Types::DataItem value;
data[i]->Get( value, n );
this->m_VecP[i] += w * value;
this->m_VecQ[i] += (1.0 - w) * (1.0 - value);
}
}
for ( size_t i = 0; i < numberOfInputs; ++i )
{
this->m_VecP[i] /= sumW;
this->m_VecQ[i] /= (numberOfPixels - sumW);
}
// E-step
#pragma omp parallel for
for ( int n = 0; n < static_cast<int>( numberOfPixels ); ++n )
{
double alpha = globalPrior;
double beta = (1.0-globalPrior);
Types::DataItem w = 0;
for ( size_t i = 0; i < numberOfInputs; ++i )
{
if ( data[i]->Get( w, n ) )
{
alpha *= (1.0-w-m_VecP[i]);
beta *= (w-m_VecQ[i]);
}
}
this->m_Result->Set( alpha / (alpha+beta), n );
}
}
}
} // namespace cmtk
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