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/* -*- mia-c++ -*-
*
* This file is part of MIA - a toolbox for medical image analysis
* Copyright (c) Leipzig, Madrid 1999-2014 Gert Wollny
*
* MIA 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.
*
* This program 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 MIA; if not, see <http://www.gnu.org/licenses/>.
*
*/
#define VSTREAM_DOMAIN "2dmyoica"
#include <fstream>
#include <libxml++/libxml++.h>
#include <itpp/signal/fastica.h>
#include <boost/filesystem.hpp>
#include <mia/core/msgstream.hh>
#include <mia/core/cmdlineparser.hh>
#include <mia/core/errormacro.hh>
#include <mia/core/minimizer.hh>
#include <mia/core/bfsv23dispatch.hh>
#include <mia/2d/nonrigidregister.hh>
#include <mia/2d/perfusion.hh>
#include <mia/2d/segsetwithimages.hh>
#include <mia/2d/transformfactory.hh>
using namespace std;
using namespace mia;
namespace bfs=boost::filesystem;
const SProgramDescription g_description = {
{pdi_group, "Registration of series of 2D images"},
{pdi_short, "Run a registration of a series of 2D images."},
{pdi_description, "This program runs the non-rigid registration of an perfusion image series."
"In each pass, first an ICA analysis is run to estimate and eliminate "
"the periodic movement and create reference images with intensities similar "
"to the corresponding original image. Then non-rigid registration is run "
"using the an \"ssd + divcurl\" cost model. The B-spline c-rate and the "
"divcurl cost weight are changed in each pass according to given parameters."
"In the first pass a bounding box around the LV myocardium may be extracted"
"to speed up computation\n"
"Special note to this implemnentation: the registration is always run from the "
"original images to avoid the accumulation of interpolation errors."},
{pdi_example_descr, "Register the perfusion series given in 'segment.set' by "
"using automatic ICA estimation. Skip two images at the beginning and otherwiese "
"use the default parameters. Store the result in 'registered.set'."},
{pdi_example_code, " -i segment.set -o registered.set -k 2"}
};
C2DFullCostList create_costs(double imageweight)
{
C2DFullCostList result;
stringstream image_descr;
image_descr << "image:weight=" << imageweight;
result.push(C2DFullCostPluginHandler::instance().produce(image_descr.str()));
return result;
}
P2DTransformationFactory create_transform_creator(size_t c_rate, double divcurlweight)
{
stringstream transf;
transf << "spline:rate=" << c_rate << ",penalty=[divcurl:weight=" << divcurlweight << "]";
return C2DTransformCreatorHandler::instance().produce(transf.str());
}
void segment_and_crop_input(CSegSetWithImages& input_set,
const C2DPerfusionAnalysis& ica,
float box_scale,
C2DPerfusionAnalysis::EBoxSegmentation segmethod,
C2DImageSeries& references,
const string& save_crop_feature)
{
C2DBounds crop_start;
auto cropper = ica.get_crop_filter(box_scale, crop_start,
segmethod, save_crop_feature);
if (!cropper)
throw create_exception<runtime_error>( "Cropping was requested, but segmentation failed");
C2DImageSeries input_images = input_set.get_images();
for(auto i = input_images.begin(); i != input_images.end(); ++i)
*i = cropper->filter(**i);
for (auto i = references.begin(); i != references.end(); ++i)
*i = cropper->filter(**i);
auto tr_creator = C2DTransformCreatorHandler::instance().produce("translate");
P2DTransformation shift = tr_creator->create(C2DBounds(1,1));
auto p = shift->get_parameters();
p[0] = crop_start.x;
p[1] = crop_start.y;
shift->set_parameters(p);
input_set.transform(*shift);
input_set.set_images(input_images);
}
vector<P2DTransformation>
run_registration_pass(CSegSetWithImages& input_set,
C2DImageSeries& registered,
const C2DImageSeries& references,
int skip_images, PMinimizer minimizer, size_t mg_levels,
double c_rate, double divcurlweight, double imageweight)
{
vector<P2DTransformation> result;
C2DImageSeries input_images = input_set.get_images();
registered.resize(input_images.size());
auto costs = create_costs(imageweight);
auto transform_creator = create_transform_creator(c_rate, divcurlweight);
C2DNonrigidRegister nrr(costs, minimizer, transform_creator, mg_levels);
// this loop could be parallized
for (size_t i = skip_images; i < input_images.size(); ++i) {
cvmsg() << "Register frame " << i << "\n";
P2DTransformation transform = nrr.run(input_images[i],
references[i - skip_images]);
registered[i] = (*transform)(*input_images[i]);
result.push_back(transform);
}
return result;
}
int do_main( int argc, char *argv[] )
{
// IO parameters
string in_filename;
string out_filename;
string registered_filebase("reg");
// debug options: save some intermediate steps
string cropped_filename;
string save_crop_feature;
// this parameter is currently not exported - reading the image data is
// therefore done from the path given in the segmentation set
bool override_src_imagepath = true;
// registration parameters
PMinimizer minimizer;
double c_rate = 32;
double c_rate_divider = 4;
double divcurlweight = 20.0;
double divcurlweight_divider = 4.0;
double imageweight = 1.0;
PSplineKernel interpolator_kernel;
size_t mg_levels = 3;
// ICA parameters
size_t components = 0;
bool normalize = false;
bool no_meanstrip = false;
float box_scale = 0.0;
size_t skip_images = 0;
size_t max_ica_iterations = 400;
C2DPerfusionAnalysis::EBoxSegmentation
segmethod=C2DPerfusionAnalysis::bs_features;
size_t current_pass = 0;
size_t pass = 3;
CCmdOptionList options(g_description);
options.set_group("\nFile-IO");
options.add(make_opt( in_filename, "in-file", 'i',
"input perfusion data set", CCmdOptionFlags::required_input));
options.add(make_opt( out_filename, "out-file", 'o',
"output perfusion data set", CCmdOptionFlags::required_output));
options.add(make_opt( registered_filebase, "registered", 'r',
"file name base for registered fiels"));
options.add(make_opt( cropped_filename, "save-cropped", 0,
"save cropped set to this file"));
options.add(make_opt( save_crop_feature, "save-feature", 0,
"save segmentation feature images"
" and initial ICA mixing matrix"));
options.set_group("\nRegistration");
options.add(make_opt( minimizer, "gsl:opt=gd,step=0.1", "optimizer", 'O', "Optimizer used for minimization"));
options.add(make_opt( c_rate, "start-c-rate", 'a',
"start coefficinet rate in spines,"
" gets divided by --c-rate-divider with every pass"));
options.add(make_opt( c_rate_divider, "c-rate-divider", 0,
"cofficient rate divider for each pass"));
options.add(make_opt( divcurlweight, "start-divcurl", 'd',
"start divcurl weight, gets divided by"
" --divcurl-divider with every pass"));
options.add(make_opt( divcurlweight_divider, "divcurl-divider", 0,
"divcurl weight scaling with each new pass"));
options.add(make_opt( imageweight, "imageweight", 'w',
"image cost weight"));
options.add(make_opt( interpolator_kernel, "bspline:d=3", "interpolator", 'p', "image interpolator kernel"));
options.add(make_opt( mg_levels, "mg-levels", 'l', "multi-resolution levels"));
options.add(make_opt( pass, "passes", 'P', "registration passes"));
options.set_group("\nICA");
options.add(make_opt( components, "components", 'C', "ICA components 0 = automatic estimation"));
options.add(make_opt( normalize, "normalize", 0, "don't normalized ICs"));
options.add(make_opt( no_meanstrip, "no-meanstrip", 0,
"don't strip the mean from the mixing curves"));
options.add(make_opt( box_scale, "segscale", 's',
"segment and scale the crop box around the LV (0=no segmentation)"));
options.add(make_opt( skip_images, "skip", 'k', "skip images at the beginning of the series "
"e.g. because as they are of other modalities"));
options.add(make_opt( max_ica_iterations, "max-ica-iter", 'm', "maximum number of iterations in ICA"));
options.add(make_opt(segmethod , C2DPerfusionAnalysis::segmethod_dict, "segmethod", 'E',
"Segmentation method"));
if (options.parse(argc, argv) != CCmdOptionList::hr_no)
return EXIT_SUCCESS;
// this cost will always be used
// load input data set
CSegSetWithImages input_set(in_filename, override_src_imagepath);
C2DImageSeries input_images = input_set.get_images();
cvmsg() << "skipping " << skip_images << " images\n";
vector<C2DFImage> series(input_images.size() - skip_images);
transform(input_images.begin() + skip_images, input_images.end(),
series.begin(), FCopy2DImageToFloatRepn());
// run ICA
unique_ptr<C2DPerfusionAnalysis> ica(new C2DPerfusionAnalysis(components, normalize, !no_meanstrip));
if (max_ica_iterations)
ica->set_max_ica_iterations(max_ica_iterations);
ica->set_approach(FICA_APPROACH_DEFL);
if (!ica->run(series)) {
ica.reset(new C2DPerfusionAnalysis(components, normalize, !no_meanstrip));
ica->set_approach(FICA_APPROACH_SYMM);
if (!ica->run(series))
box_scale = false;
}
vector<C2DFImage> references_float = ica->get_references();
C2DImageSeries references(references_float.size());
transform(references_float.begin(), references_float.end(), references.begin(),
FWrapStaticDataInSharedPointer<C2DImage>());
// crop if requested
if (box_scale) {
segment_and_crop_input(input_set, *ica, box_scale, segmethod, references, save_crop_feature);
input_images = input_set.get_images();
}
// save cropped images if requested
if (!cropped_filename.empty()) {
bfs::path cf(cropped_filename);
cf.replace_extension();
input_set.rename_base(__bfs_get_filename(cf));
input_set.save_images(cropped_filename);
unique_ptr<xmlpp::Document> test_cropset(input_set.write());
ofstream outfile(cropped_filename, ios_base::out );
if (outfile.good())
outfile << test_cropset->write_to_string_formatted();
else
throw create_exception<runtime_error>( "unable to save to '", cropped_filename, "'");
}
vector<P2DTransformation> transformations;
C2DImageSeries registered;
bool do_continue=true;
bool lastpass = false;
while (do_continue || lastpass){
++current_pass;
cvmsg() << "Registration pass " << current_pass << "\n";
transformations =
run_registration_pass(input_set, registered, references, skip_images, minimizer,
mg_levels, c_rate, divcurlweight, imageweight);
if (lastpass)
break;
C2DPerfusionAnalysis ica2(components, normalize, !no_meanstrip);
if (max_ica_iterations)
ica2.set_max_ica_iterations(max_ica_iterations);
transform(registered.begin() + skip_images,
registered.end(), series.begin(), FCopy2DImageToFloatRepn());
if (!ica2.run(series))
ica2.set_approach(FICA_APPROACH_SYMM);
ica2.run(series);
divcurlweight /= divcurlweight_divider;
if (c_rate > 1)
c_rate /= c_rate_divider;
references_float = ica2.get_references();
transform(references_float.begin(), references_float.end(),
references.begin(), FWrapStaticDataInSharedPointer<C2DImage>());
const bool can_one_more_pass = !pass || current_pass < pass;
do_continue = can_one_more_pass && ica2.has_movement();
if (!do_continue && !save_crop_feature.empty()) {
stringstream cfile;
cfile << save_crop_feature << "-final.txt";
ica2.save_coefs(cfile.str());
stringstream new_base;
new_base << save_crop_feature << "-p"<< pass << "-final";
ica2.save_feature_images(new_base.str());
}
// run one more pass if the limit is not reached and no movement identified
lastpass = (!do_continue && can_one_more_pass);
}
CSegSetWithImages::Frames& frames = input_set.get_frames();
for (size_t i = skip_images; i < input_images.size(); ++i)
frames[i].inv_transform(*transformations[i-skip_images]);
C2DImageSeries iimages = input_set.get_images();
copy(iimages.begin(), iimages.begin() + skip_images, registered.begin());
input_set.set_images(registered);
input_set.rename_base(registered_filebase);
input_set.save_images(out_filename);
unique_ptr<xmlpp::Document> outset(input_set.write());
ofstream outfile(out_filename.c_str(), ios_base::out );
if (outfile.good())
outfile << outset->write_to_string_formatted();
return outfile.good() ? EXIT_SUCCESS : EXIT_FAILURE;
}
#include <mia/internal/main.hh>
MIA_MAIN(do_main);
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