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/************************************************************************
*
* Copyright (C) 2018-2025 IRCAD France
* Copyright (C) 2018-2021 IHU Strasbourg
*
* This file is part of Sight.
*
* Sight is free software: you can redistribute it and/or modify it under
* the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Sight 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 Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with Sight. If not, see <https://www.gnu.org/licenses/>.
*
***********************************************************************/
#if HAS_ITK_REVIEW_MODULE
#include "filter/image/spheroid_extraction.hpp"
#include <core/tools/dispatcher.hpp>
#include <io/itk/itk.hpp>
#include <itkBinaryThresholdImageFilter.h>
#include <itkConnectedComponentImageFilter.h>
#include <itkImage.h>
#include <itkLabelGeometryImageFilter.h>
namespace sight::filter::image
{
struct spheroid_extractor
{
struct parameters
{
data::image::csptr input_image;
data::point_list::sptr output_point_list;
double threshold {};
double radius_min {};
double radius_max {};
double elongation_min {};
double elongation_max {};
};
//------------------------------------------------------------------------------
template<class PIXELTYPE>
void operator()(parameters& _params)
{
using image_t = typename itk::Image<PIXELTYPE, 3>;
using binary_image_t = typename itk::Image<std::uint16_t, 3>;
auto input_image = io::itk::move_to_itk<image_t>(_params.input_image);
auto threshold_filter = itk::BinaryThresholdImageFilter<image_t, binary_image_t>::New();
auto threshold = PIXELTYPE(_params.threshold);
threshold_filter->SetLowerThreshold(threshold);
threshold_filter->SetUpperThreshold(threshold * PIXELTYPE(10));
threshold_filter->SetInsideValue(255);
threshold_filter->SetOutsideValue(0);
threshold_filter->SetInput(input_image);
threshold_filter->Update();
binary_image_t::Pointer binary_image =
threshold_filter->GetOutput();
itk::ConnectedComponentImageFilter<binary_image_t, binary_image_t>::Pointer cc =
itk::ConnectedComponentImageFilter<binary_image_t, binary_image_t>::New();
cc->SetInput(threshold_filter->GetOutput());
cc->FullyConnectedOn();
cc->Update();
using label_stats_filter_t = itk::LabelGeometryImageFilter<binary_image_t, image_t>;
auto label_geometry_filter = label_stats_filter_t::New();
label_geometry_filter->SetInput(cc->GetOutput());
label_geometry_filter->SetIntensityInput(input_image);
label_geometry_filter->Update();
auto labels = label_geometry_filter->GetLabels();
SIGHT_DEBUG("Number of labels : " << label_geometry_filter->GetNumberOfLabels());
for(auto labels_it = labels.begin() ; labels_it != labels.end() ; ++labels_it)
{
auto label_value = *labels_it;
const double radius_mean = (_params.radius_max + _params.radius_min) / 2.;
const double mean_elongation = (_params.elongation_max + _params.elongation_min) / 2.;
const double radius_std = (_params.radius_max - _params.radius_min) / 6.;
const double elongation_std = (_params.elongation_max - _params.elongation_min) / 6.;
const double e = label_geometry_filter->GetElongation(label_value);
const auto axes_length = label_geometry_filter->GetAxesLength(label_value);
SIGHT_DEBUG("Elongation" << e);
SIGHT_DEBUG("Axes length" << axes_length);
const double ge = std::pow(e - mean_elongation, 2.) / std::pow(elongation_std, 2.);
const double gr0 = std::pow(axes_length[0] - radius_mean, 2.) / std::pow(radius_std, 2.);
const double gr1 = std::pow(axes_length[1] - radius_mean, 2.) / std::pow(radius_std, 2.);
const double gr2 = std::pow(axes_length[2] - radius_mean, 2.) / std::pow(radius_std, 2.);
const double mahalanobis_distance = std::sqrt(ge + gr0 + gr1 + gr2);
SIGHT_DEBUG("Mahalanobis distance : " << mahalanobis_distance);
// Empirical value. You shouldn't have to change it.
// If you believe the detection wrongly failed/succeeded, please consider changing your radius
// and elongation ranges before modifying this threshold.
const double criteria_threshold = 15.;
if(mahalanobis_distance < criteria_threshold)
{
const auto centroid_point = label_geometry_filter->GetCentroid(label_value);
std::array<double, 3> real_point_coords {};
for(std::uint8_t i = 0 ; i < 3 ; ++i)
{
real_point_coords[i] = double(centroid_point[i]) * input_image->GetSpacing()[i]
+ input_image->GetOrigin()[i];
}
data::point::sptr point = std::make_shared<data::point>(real_point_coords);
_params.output_point_list->get_points().push_back(point);
}
}
}
};
//------------------------------------------------------------------------------
data::point_list::sptr spheroid_extraction::extract(
const data::image::csptr& _image,
const double _threshold,
const double _radius_min,
const double _radius_max,
const double _elongation_min,
const double _elongation_max
)
{
data::point_list::sptr output_point_list = std::make_shared<data::point_list>();
spheroid_extractor::parameters params;
params.input_image = _image;
params.output_point_list = output_point_list;
params.threshold = _threshold;
params.radius_min = _radius_min;
params.radius_max = _radius_max;
params.elongation_min = _elongation_min;
params.elongation_max = _elongation_max;
core::tools::dispatcher<core::tools::supported_dispatcher_types, spheroid_extractor>::invoke(
_image->type(),
params
);
return output_point_list;
}
} // namespace sight::filter::image.
#endif // ITK_VERSION_MAJOR == 4 || (ITK_VERSION_MAJOR >= 5 && ITK_VERSION_MINOR >= 3)
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