//#################### PRIVATE METHODS ####################
void SpleenIdentifier3D::execute_impl()
{
set_status("Identifying spleen...");
VolumeIPFMultiFeatureSelection_Ptr multiFeatureSelection = get_multi_feature_selection();
// Step 1: Filter for spleen candidates.
std::list<PFNodeID> candidates = filter_branch_nodes(boost::bind(&SpleenIdentifier3D::is_candidate, this, _1, _2));
// Step 2: Pick the best candidate (namely, the one which stretches furthest to the right of the image).
PFNodeID bestCandidate;
int bestXMax = INT_MIN;
for(std::list<PFNodeID>::const_iterator it=candidates.begin(), iend=candidates.end(); it!=iend; ++it)
{
const BranchProperties& properties = volume_ipf()->branch_properties(*it);
if(properties.x_max() > bestXMax)
{
bestCandidate = *it;
bestXMax = properties.x_max();
}
}
// If we can't find a candidate spleen, exit.
if(bestCandidate == PFNodeID::invalid()) return;
// Step 3: (TEMPORARY) Mark the best candidate as spleen (and unmark it as liver if necessary).
multiFeatureSelection->identify_node(bestCandidate, AbdominalFeature::SPLEEN);
multiFeatureSelection->unidentify_node(bestCandidate, AbdominalFeature::LIVER);
}
//#################### PRIVATE METHODS ####################
void SpineIdentifier3D::execute_impl()
{
set_status("Identifying the spine...");
VolumeIPFMultiFeatureSelection_Ptr multiFeatureSelection = get_multi_feature_selection();
// Step 1: Filter for spine.
std::list<PFNodeID> nodes = filter_branch_nodes(boost::bind(&SpineIdentifier3D::is_spine, this, _1, _2));
// Step 2: Convert the partition forest candidate nodes to positions in the volume.
std::set<int> leaves;
for(std::list<PFNodeID>::const_iterator it=nodes.begin(), iend=nodes.end(); it!=iend; ++it)
{
std::deque<int> leafIndices = volume_ipf()->receptive_region_of(*it);
for(std::deque<int>::const_iterator jt=leafIndices.begin(), jend=leafIndices.end(); jt!=jend; ++jt)
{
leaves.insert(*jt);
}
}
std::list<itk::Index<3> > positions;
for(std::set<int>::const_iterator it=leaves.begin(), iend=leaves.end(); it!=iend; ++it)
{
if(210 < volume_ipf()->leaf_properties(*it).grey_value())
{
positions.push_back(volume_ipf()->position_of_leaf(*it));
}
}
increment_progress();
// Step 3: Use the Fast Marching Method.
FastMarching<3> fm(volume_ipf()->leaf_layer()->gradient_magnitude_image(), positions);
positions = fm.get_shape_at_time(100.0);
increment_progress();
// Step 4: Convert the positions in the volume to partition forest selection.
leaves.clear();
for(std::list<itk::Index<3> >::const_iterator it=positions.begin(), iend=positions.end(); it!=iend; ++it)
{
leaves.insert(volume_ipf()->leaf_of_position(*it));
}
PartitionForestSelection_Ptr region(new PartitionForestSelectionT(volume_ipf(), leaves));
// Step 5: Mark the results as spine.
multiFeatureSelection->identify_selection(region, AbdominalFeature::VERTEBRA);
}
