diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp
index 1805ade854..389f5a42f8 100755
--- a/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp
+++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp
@@ -1,746 +1,749 @@
/*===================================================================
The Medical Imaging Interaction Toolkit (MITK)
Copyright (c) German Cancer Research Center,
Division of Medical and Biological Informatics.
All rights reserved.
This software is distributed WITHOUT ANY WARRANTY; without
even the implied warranty of MERCHANTABILITY or FITNESS FOR
A PARTICULAR PURPOSE.
See LICENSE.txt or http://www.mitk.org for details.
===================================================================*/
#include <mitkBaseData.h>
#include <mitkImageCast.h>
#include <mitkImageToItk.h>
#include <metaCommand.h>
#include <mitkCommandLineParser.h>
#include <usAny.h>
#include <mitkIOUtil.h>
#include <boost/lexical_cast.hpp>
#include <itksys/SystemTools.hxx>
#include <itkDirectory.h>
#include <mitkFiberBundle.h>
#include <mitkPreferenceListReaderOptionsFunctor.h>
#include <mitkDiffusionPropertyHelper.h>
#include <vnl/vnl_linear_system.h>
#include <Eigen/Dense>
#include <mitkStickModel.h>
#include <mitkBallModel.h>
#include <vigra/regression.hxx>
#include <itkImageFileWriter.h>
#include <itkImageDuplicator.h>
#include <itkMersenneTwisterRandomVariateGenerator.h>
#include <mitkPeakImage.h>
#include <vnl/algo/vnl_lbfgsb.h>
#include <vnl/vnl_sparse_matrix.h>
#include <vnl/vnl_sparse_matrix_linear_system.h>
#include <vnl/algo/vnl_lsqr.h>
#include <itkImageDuplicator.h>
#include <itkTimeProbe.h>
#include <random>
#include <itkParticleSwarmOptimizer.h>
#include <itkOnePlusOneEvolutionaryOptimizer.h>
#include <itkGradientDescentOptimizer.h>
#include <itkSPSAOptimizer.h>
using namespace std;
typedef itksys::SystemTools ist;
typedef itk::Point<float, 4> PointType4;
typedef itk::Image< float, 4 > PeakImgType;
const double UPSCALE = 1.0;
-vnl_vector_fixed<float,3> GetClosestPeak(itk::Index<4> idx, PeakImgType::Pointer peak_image , vnl_vector_fixed<float,3> fiber_dir, int& id )
+vnl_vector_fixed<float,3> GetClosestPeak(itk::Index<4> idx, PeakImgType::Pointer peak_image , vnl_vector_fixed<float,3> fiber_dir, int& id, double& w )
{
int m_NumDirs = peak_image->GetLargestPossibleRegion().GetSize()[3]/3;
vnl_vector_fixed<float,3> out_dir; out_dir.fill(0);
float angle = 0.8;
for (int i=0; i<m_NumDirs; i++)
{
vnl_vector_fixed<float,3> dir;
idx[3] = i*3;
dir[0] = peak_image->GetPixel(idx);
idx[3] += 1;
dir[1] = peak_image->GetPixel(idx);
idx[3] += 1;
dir[2] = peak_image->GetPixel(idx);
float mag = dir.magnitude();
if (mag<mitk::eps)
continue;
dir.normalize();
float a = dot_product(dir, fiber_dir);
if (fabs(a)>angle)
{
angle = fabs(a);
+ w = angle;
if (a<0)
out_dir = -dir;
else
out_dir = dir;
out_dir *= mag;
id = i;
}
}
return out_dir;
}
class VnlCostFunction : public vnl_cost_function
{
public:
vnl_sparse_matrix_linear_system< double >* S;
vnl_sparse_matrix< double > m_A;
vnl_vector< double > m_b;
double m_Lambda;
void SetProblem(vnl_sparse_matrix< double >& A, vnl_vector<double>& b, double lambda)
{
S = new vnl_sparse_matrix_linear_system<double>(A, b);
m_A = A;
m_b = b;
m_Lambda = lambda;
}
VnlCostFunction(const int NumVars) : vnl_cost_function(NumVars)
{
}
double f_itk(vnl_vector<double> const &x) const
{
double min = x.min_value();
if( min<0 )
return 10000 * -min;
double cost = S->get_rms_error(x);
double regu = m_Lambda*1e8*x.squared_magnitude()/x.size();
cost += regu;
return cost;
}
double f(vnl_vector<double> const &x)
{
// // cost for x (mean squared error)
double cost = S->get_rms_error(x);
cost *= cost;
// double cost = 0;
// vnl_vector<double> d; d.set_size(m_b.size());
// S->multiply(x, d);
// d -= m_b;
// for (int i=0; i<d.size(); i++)
// {
// }
// cost for e^x
// vnl_vector<double> x_exp; x_exp.set_size(x.size());
// for (unsigned int c=0; c<x.size(); c++)
// x_exp[c] = std::exp(x[c]);
// vnl_vector<double> d; d.set_size(x.size());
// S->multiply(x_exp, d);
// d -= m_b;
// double cost = d.squared_magnitude()/x.size();
// // Tikhonov regu
double regu = m_Lambda*1e8*x.squared_magnitude()/x.size();
// aTV regu
// double regu = 0;
// unsigned int N = m_b.size();
// vnl_vector<double> ones; ones.set_size(x.size()); ones.fill(1.0);
// vnl_vector<double> means; means.set_size(N);
// S->multiply(ones, means);
// unsigned int norm = 0;
// for (unsigned int i=0; i<N; ++i)
// {
// if (m_A.get_row(i).empty())
// continue;
// means[i] /= m_A.get_row(i).size();
// for (auto el : m_A.get_row(i))
// {
// float d = 0;
// if (x[el.first]>means[i])
// d = std::exp(x[el.first]) - std::exp(means[i]);
// else
// d = x[el.first] - means[i];
// regu += d*d;
// norm++;
// }
// }
// regu /= norm;
cost += regu;
return cost;
}
void gradf(vnl_vector<double> const &x, vnl_vector<double> &dx)
{
dx.fill(0.0);
unsigned int N = m_b.size();
// vnl_vector<double> x_exp; x_exp.set_size(x.size());
// for (unsigned int c=0; c<x.size(); c++)
// x_exp[c] = std::exp(x[c]);
vnl_vector<double> d; d.set_size(N);
S->multiply(x,d);
d -= m_b;
S->transpose_multiply(d, dx);
dx *= 2.0/N;
// for (unsigned int c=0; c<x.size(); c++)
// dx[c] *= x_exp[c]; // only for e^x weights
dx += m_Lambda*1e8*2.0*x/x.size();
}
};
class ItkCostFunction : public itk::SingleValuedCostFunction
{
public:
/** Standard class typedefs. */
typedef ItkCostFunction Self;
typedef SingleValuedCostFunction Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self)
/** Run-time type information (and related methods). */
itkTypeMacro(ItkCostFunction, SingleValuedCostfunction)
void SetVnlCostFunction(VnlCostFunction& cf) { m_VnlCostFunction = cf; }
unsigned int GetNumberOfParameters(void) const { return m_VnlCostFunction.get_number_of_unknowns(); } // itk::CostFunction
MeasureType GetValue(const ParametersType & parameters) const
{
return m_VnlCostFunction.f_itk(parameters);
}
void GetDerivative(const ParametersType &, DerivativeType & ) const {
throw itk::ExceptionObject( __FILE__, __LINE__, "No derivative is available for this cost function.");
}
protected:
ItkCostFunction(){}
~ItkCostFunction(){}
VnlCostFunction m_VnlCostFunction = VnlCostFunction(1);
private:
ItkCostFunction(const Self &); //purposely not implemented
void operator = (const Self &); //purposely not implemented
};
void OptimizeItk(VnlCostFunction& cf, vnl_vector<double>& x, int iter, double lb, double ub)
{
ItkCostFunction::ParametersType p; p.SetData(x.data_block(), x.size());
ItkCostFunction::Pointer itk_cf = ItkCostFunction::New();
itk_cf->SetVnlCostFunction(cf);
std::pair< double, double > bounds; bounds.first = lb; bounds.second = ub;
MITK_INFO << bounds;
// itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer randGen = itk::Statistics::MersenneTwisterRandomVariateGenerator::New();
// itk::OnePlusOneEvolutionaryOptimizer::Pointer opt = itk::OnePlusOneEvolutionaryOptimizer::New();
// opt->SetCostFunction(itk_cf);
// opt->MinimizeOn();
// opt->SetInitialPosition(p);
// opt->SetNormalVariateGenerator(randGen);
// opt->SetMaximumIteration(iter);
// opt->StartOptimization();
// itk::ParticleSwarmOptimizer::Pointer opt = itk::ParticleSwarmOptimizer::New();
// opt->SetCostFunction(itk_cf);
// opt->SetInitialPosition(p);
// opt->SetParameterBounds(bounds, x.size());
// opt->SetMaximalNumberOfIterations(iter);
// opt->SetNumberOfParticles(100);
// opt->SetParametersConvergenceTolerance(0.01, x.size());
// opt->SetNumberOfGenerationsWithMinimalImprovement(3);
// opt->StartOptimization();
// itk::GradientDescentOptimizer::Pointer opt = itk::GradientDescentOptimizer::New();
// opt->SetCostFunction(itk_cf);
// opt->SetInitialPosition(p);
// opt->SetMinimize(true);
// opt->SetNumberOfIterations(iter);
// opt->StartOptimization();
itk::SPSAOptimizer::Pointer opt = itk::SPSAOptimizer::New();
opt->SetCostFunction(itk_cf);
opt->SetInitialPosition(p);
opt->SetMinimize(true);
opt->SetNumberOfPerturbations(iter);
opt->StartOptimization();
x.copy_in(opt->GetCurrentPosition().data_block());
for (unsigned int i=0; i<x.size(); i++)
MITK_INFO << opt->GetCurrentPosition()[i];
// MITK_INFO << "Cost: " << opt->GetCurrentCost();
}
std::vector<float> FitFibers( std::string , std::vector< mitk::FiberBundle::Pointer > input_tracts, mitk::Image::Pointer inputImage, bool single_fiber_fit, int max_iter, float g_tol, float lambda )
{
typedef mitk::ImageToItk< PeakImgType > CasterType;
CasterType::Pointer caster = CasterType::New();
caster->SetInput(inputImage);
caster->Update();
PeakImgType::Pointer itkImage = caster->GetOutput();
unsigned int* image_size = inputImage->GetDimensions();
int sz_x = image_size[0];
int sz_y = image_size[1];
int sz_z = image_size[2];
int sz_peaks = image_size[3]/3;
int num_voxels = sz_x*sz_y*sz_z;
unsigned int num_unknowns = input_tracts.size();
if (single_fiber_fit)
{
num_unknowns = 0;
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
num_unknowns += input_tracts.at(bundle)->GetNumFibers();
}
unsigned int number_of_residuals = num_voxels * sz_peaks;
// create linear system
MITK_INFO << "Num. unknowns: " << num_unknowns;
MITK_INFO << "Num. residuals: " << number_of_residuals;
MITK_INFO << "Creating system ...";
vnl_sparse_matrix< double > A; A.set_size(number_of_residuals, num_unknowns);
vnl_vector<double> b; b.set_size(number_of_residuals); b.fill(0.0);
double TD = 0;
double FD = 0;
unsigned int dir_count = 0;
unsigned int fiber_count = 0;
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
vtkSmartPointer<vtkPolyData> polydata = input_tracts.at(bundle)->GetFiberPolyData();
for (int i=0; i<input_tracts.at(bundle)->GetNumFibers(); ++i)
{
vtkCell* cell = polydata->GetCell(i);
int numPoints = cell->GetNumberOfPoints();
vtkPoints* points = cell->GetPoints();
if (numPoints<2)
MITK_INFO << "FIBER WITH ONLY ONE POINT ENCOUNTERED!";
for (int j=0; j<numPoints-1; ++j)
{
double* p1 = points->GetPoint(j);
PointType4 p;
p[0]=p1[0];
p[1]=p1[1];
p[2]=p1[2];
p[3]=0;
itk::Index<4> idx4;
itkImage->TransformPhysicalPointToIndex(p, idx4);
if (!itkImage->GetLargestPossibleRegion().IsInside(idx4))
continue;
double* p2 = points->GetPoint(j+1);
vnl_vector_fixed<float,3> fiber_dir;
fiber_dir[0] = p[0]-p2[0];
fiber_dir[1] = p[1]-p2[1];
fiber_dir[2] = p[2]-p2[2];
fiber_dir.normalize();
+ double w = 0;
int peak_id = -1;
- vnl_vector_fixed<float,3> odf_peak = GetClosestPeak(idx4, itkImage, fiber_dir, peak_id);
- float peak_mag = odf_peak.magnitude();
+ vnl_vector_fixed<float,3> odf_peak = GetClosestPeak(idx4, itkImage, fiber_dir, peak_id, w);
if (peak_id<0)
continue;
+ float peak_mag = odf_peak.magnitude();
+
int x = idx4[0];
int y = idx4[1];
int z = idx4[2];
unsigned int linear_index = sz_peaks*(x + sz_x*y + sz_x*sz_y*z);
if (b[linear_index + peak_id] == 0)
{
dir_count++;
FD += peak_mag;
}
- TD += 1;
+ TD += w;
if (single_fiber_fit)
{
b[linear_index + peak_id] = peak_mag;
- A.put(linear_index + peak_id, fiber_count, A.get(linear_index + peak_id, fiber_count) + 1);
+ A.put(linear_index + peak_id, fiber_count, A.get(linear_index + peak_id, fiber_count) + w);
}
else
{
b[linear_index + peak_id] = peak_mag;
- A.put(linear_index + peak_id, bundle, A.get(linear_index + peak_id, bundle) + 1);
+ A.put(linear_index + peak_id, bundle, A.get(linear_index + peak_id, bundle) + w);
}
}
++fiber_count;
}
}
TD /= (dir_count*fiber_count);
FD /= dir_count;
A *= 1.0/TD;
b *= 100.0*UPSCALE/FD; // times 100 because we want to avoid too small weights
MITK_INFO << "TD: " << TD;
MITK_INFO << "FD: " << FD;
MITK_INFO << "Regularization: " << lambda; // pretty large regularization needed --> internally upscale by 1e8
itk::TimeProbe clock;
clock.Start();
MITK_INFO << "Fitting fibers";
VnlCostFunction cost(num_unknowns);
cost.SetProblem(A, b, lambda);
vnl_vector<double> x; x.set_size(num_unknowns); x.fill( TD/100.0 * FD/2.0 );
vnl_lbfgsb minimizer(cost);
vnl_vector<double> l; l.set_size(num_unknowns); l.fill(0);
vnl_vector<long> bound_selection; bound_selection.set_size(num_unknowns); bound_selection.fill(1);
minimizer.set_bound_selection(bound_selection);
minimizer.set_lower_bound(l);
minimizer.set_trace(true);
minimizer.set_projected_gradient_tolerance(g_tol);
if (max_iter>0)
minimizer.set_max_function_evals(max_iter);
minimizer.minimize(x);
// SECOND RUN // USE QUARTILE AS LIMIT???
float mean_w = x.mean();
x.fill(mean_w);
MITK_INFO << "Mean weight: " << mean_w;
vnl_vector<double> u; u.set_size(num_unknowns); u.fill(mean_w * 3);
minimizer.set_upper_bound(u);
bound_selection.fill(2);
minimizer.set_bound_selection(bound_selection);
minimizer.minimize(x);
MITK_INFO << "Residual error: " << minimizer.get_end_error();
MITK_INFO << "NumEvals: " << minimizer.get_num_evaluations();
MITK_INFO << "NumIterations: " << minimizer.get_num_iterations();
clock.Stop();
int h = clock.GetTotal()/3600;
int m = ((int)clock.GetTotal()%3600)/60;
int s = (int)clock.GetTotal()%60;
MITK_INFO << "Optimization took " << h << "h, " << m << "m and " << s << "s";
std::vector<float> weights;
for (unsigned int i=0; i<num_unknowns; ++i)
{
// MITK_INFO << x[i];
weights.push_back(FD*x[i]);
// weights.push_back(x[i]);
}
return weights;
}
std::vector< string > get_file_list(const std::string& path)
{
std::vector< string > file_list;
itk::Directory::Pointer dir = itk::Directory::New();
if (dir->Load(path.c_str()))
{
int n = dir->GetNumberOfFiles();
for (int r = 0; r < n; r++)
{
const char *filename = dir->GetFile(r);
std::string ext = ist::GetFilenameExtension(filename);
if (ext==".fib" || ext==".trk")
file_list.push_back(path + '/' + filename);
}
}
return file_list;
}
int main(int argc, char* argv[])
{
mitkCommandLineParser parser;
parser.setTitle("Fit Fibers To Image");
parser.setCategory("Fiber Tracking Evaluation");
parser.setDescription("");
parser.setContributor("MIC");
parser.setArgumentPrefix("--", "-");
parser.addArgument("", "i1", mitkCommandLineParser::StringList, "Input tractograms:", "input tractograms (.fib, vtk ascii file format)", us::Any(), false);
parser.addArgument("", "i2", mitkCommandLineParser::InputFile, "Input peaks:", "input peak image", us::Any(), false);
parser.addArgument("", "it", mitkCommandLineParser::Int, "", "");
parser.addArgument("", "s", mitkCommandLineParser::Bool, "", "");
parser.addArgument("", "g", mitkCommandLineParser::Float, "", "");
parser.addArgument("", "l", mitkCommandLineParser::Float, "", "");
parser.addArgument("", "o", mitkCommandLineParser::OutputDirectory, "Output:", "output root", us::Any(), false);
map<string, us::Any> parsedArgs = parser.parseArguments(argc, argv);
if (parsedArgs.size()==0)
return EXIT_FAILURE;
mitkCommandLineParser::StringContainerType fib_files = us::any_cast<mitkCommandLineParser::StringContainerType>(parsedArgs["i1"]);
string dwiFile = us::any_cast<string>(parsedArgs["i2"]);
string outRoot = us::any_cast<string>(parsedArgs["o"]);
bool single_fib = false;
if (parsedArgs.count("s"))
single_fib = us::any_cast<bool>(parsedArgs["s"]);
int max_iter = 0;
if (parsedArgs.count("it"))
max_iter = us::any_cast<int>(parsedArgs["it"]);
float g_tol = 1e-5;
if (parsedArgs.count("g"))
g_tol = us::any_cast<float>(parsedArgs["g"]);
float lambda = 1.0;
if (parsedArgs.count("l"))
lambda = us::any_cast<float>(parsedArgs["l"]);
try
{
std::vector< mitk::FiberBundle::Pointer > input_tracts;
mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image", "Fiberbundles"}, {});
mitk::Image::Pointer inputImage = dynamic_cast<mitk::PeakImage*>(mitk::IOUtil::Load(dwiFile, &functor)[0].GetPointer());
float minSpacing = 1;
if(inputImage->GetGeometry()->GetSpacing()[0]<inputImage->GetGeometry()->GetSpacing()[1] && inputImage->GetGeometry()->GetSpacing()[0]<inputImage->GetGeometry()->GetSpacing()[2])
minSpacing = inputImage->GetGeometry()->GetSpacing()[0];
else if (inputImage->GetGeometry()->GetSpacing()[1] < inputImage->GetGeometry()->GetSpacing()[2])
minSpacing = inputImage->GetGeometry()->GetSpacing()[1];
else
minSpacing = inputImage->GetGeometry()->GetSpacing()[2];
std::vector< std::string > fib_names;
for (auto item : fib_files)
{
if ( ist::FileIsDirectory(item) )
{
for ( auto fibFile : get_file_list(item) )
{
mitk::FiberBundle::Pointer inputTractogram = dynamic_cast<mitk::FiberBundle*>(mitk::IOUtil::Load(fibFile)[0].GetPointer());
if (inputTractogram.IsNull())
continue;
inputTractogram->ResampleLinear(minSpacing/4);
input_tracts.push_back(inputTractogram);
fib_names.push_back(fibFile);
}
}
else
{
mitk::FiberBundle::Pointer inputTractogram = dynamic_cast<mitk::FiberBundle*>(mitk::IOUtil::Load(item)[0].GetPointer());
if (inputTractogram.IsNull())
continue;
inputTractogram->ResampleLinear(minSpacing/4);
input_tracts.push_back(inputTractogram);
fib_names.push_back(item);
}
}
std::vector<float> weights = FitFibers(outRoot, input_tracts, inputImage, single_fib, max_iter, g_tol, lambda);
if (single_fib)
{
unsigned int fiber_count = 0;
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
for (int i=0; i<input_tracts.at(bundle)->GetNumFibers(); i++)
{
input_tracts.at(bundle)->SetFiberWeight(i, weights.at(fiber_count));
++fiber_count;
}
}
}
else
{
for (unsigned int i=0; i<fib_names.size(); ++i)
input_tracts.at(i)->SetFiberWeights(weights.at(i));
}
// OUTPUT IMAGES
MITK_INFO << "Generating output images ...";
typedef mitk::ImageToItk< PeakImgType > CasterType;
CasterType::Pointer caster = CasterType::New();
caster->SetInput(inputImage);
caster->Update();
PeakImgType::Pointer itkImage = caster->GetOutput();
itk::ImageDuplicator< PeakImgType >::Pointer duplicator = itk::ImageDuplicator< PeakImgType >::New();
duplicator->SetInputImage(itkImage);
duplicator->Update();
PeakImgType::Pointer unexplained_image = duplicator->GetOutput();
duplicator->SetInputImage(unexplained_image);
duplicator->Update();
PeakImgType::Pointer residual_image = duplicator->GetOutput();
duplicator->SetInputImage(residual_image);
duplicator->Update();
PeakImgType::Pointer explained_image = duplicator->GetOutput();
explained_image->FillBuffer(0.0);
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
vtkSmartPointer<vtkPolyData> polydata = input_tracts.at(bundle)->GetFiberPolyData();
for (int i=0; i<input_tracts.at(bundle)->GetNumFibers(); ++i)
{
vtkCell* cell = polydata->GetCell(i);
int numPoints = cell->GetNumberOfPoints();
vtkPoints* points = cell->GetPoints();
if (numPoints<2)
MITK_INFO << "FIBER WITH ONLY ONE POINT ENCOUNTERED!";
float w = input_tracts.at(bundle)->GetFiberWeight(i);
for (int j=0; j<numPoints-1; ++j)
{
double* p1 = points->GetPoint(j);
PointType4 p;
p[0]=p1[0];
p[1]=p1[1];
p[2]=p1[2];
p[3]=0;
itk::Index<4> idx4;
itkImage->TransformPhysicalPointToIndex(p, idx4);
if (!itkImage->GetLargestPossibleRegion().IsInside(idx4))
continue;
double* p2 = points->GetPoint(j+1);
vnl_vector_fixed<float,3> fiber_dir;
fiber_dir[0] = p[0]-p2[0];
fiber_dir[1] = p[1]-p2[1];
fiber_dir[2] = p[2]-p2[2];
fiber_dir.normalize();
int peak_id = -1;
GetClosestPeak(idx4, itkImage, fiber_dir, peak_id);
if (peak_id<0)
continue;
vnl_vector_fixed<float,3> unexplained_dir;
vnl_vector_fixed<float,3> explained_dir;
vnl_vector_fixed<float,3> res_dir;
vnl_vector_fixed<float,3> peak_dir;
idx4[3] = peak_id*3;
unexplained_dir[0] = unexplained_image->GetPixel(idx4);
explained_dir[0] = explained_image->GetPixel(idx4);
res_dir[0] = residual_image->GetPixel(idx4);
peak_dir[0] = itkImage->GetPixel(idx4);
idx4[3] += 1;
unexplained_dir[1] = unexplained_image->GetPixel(idx4);
explained_dir[1] = explained_image->GetPixel(idx4);
res_dir[1] = residual_image->GetPixel(idx4);
peak_dir[1] = itkImage->GetPixel(idx4);
idx4[3] += 1;
unexplained_dir[2] = unexplained_image->GetPixel(idx4);
explained_dir[2] = explained_image->GetPixel(idx4);
res_dir[2] = residual_image->GetPixel(idx4);
peak_dir[2] = itkImage->GetPixel(idx4);
if (dot_product(peak_dir, fiber_dir)<0)
fiber_dir *= -1;
fiber_dir *= w;
idx4[3] = peak_id*3;
residual_image->SetPixel(idx4, res_dir[0] - fiber_dir[0]);
idx4[3] += 1;
residual_image->SetPixel(idx4, res_dir[1] - fiber_dir[1]);
idx4[3] += 1;
residual_image->SetPixel(idx4, res_dir[2] - fiber_dir[2]);
if ( fabs(unexplained_dir[0]) - fabs(fiber_dir[0]) < 0 ) // did we "overexplain" stuff?
fiber_dir = unexplained_dir;
idx4[3] = peak_id*3;
unexplained_image->SetPixel(idx4, unexplained_dir[0] - fiber_dir[0]);
explained_image->SetPixel(idx4, explained_dir[0] + fiber_dir[0]);
idx4[3] += 1;
unexplained_image->SetPixel(idx4, unexplained_dir[1] - fiber_dir[1]);
explained_image->SetPixel(idx4, explained_dir[1] + fiber_dir[1]);
idx4[3] += 1;
unexplained_image->SetPixel(idx4, unexplained_dir[2] - fiber_dir[2]);
explained_image->SetPixel(idx4, explained_dir[2] + fiber_dir[2]);
}
}
}
itk::ImageFileWriter< PeakImgType >::Pointer writer = itk::ImageFileWriter< PeakImgType >::New();
writer->SetInput(unexplained_image);
writer->SetFileName(outRoot + "unexplained_image.nrrd");
writer->Update();
writer->SetInput(explained_image);
writer->SetFileName(outRoot + "explained_image.nrrd");
writer->Update();
writer->SetInput(residual_image);
writer->SetFileName(outRoot + "residual_image.nrrd");
writer->Update();
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
input_tracts.at(bundle)->Compress(0.1);
std::string name = fib_names.at(bundle);
name = ist::GetFilenameWithoutExtension(name);
mitk::IOUtil::Save(input_tracts.at(bundle), outRoot + name + "_fitted.fib");
}
}
catch (itk::ExceptionObject e)
{
std::cout << e;
return EXIT_FAILURE;
}
catch (std::exception e)
{
std::cout << e.what();
return EXIT_FAILURE;
}
catch (...)
{
std::cout << "ERROR!?!";
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}