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/* -*- mia-c++ -*-
*
* This file is part of MIA - a toolbox for medical image analysis
* Copyright (c) Leipzig, Madrid 1999-2016 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/>.
*
*/
#include <iomanip>
#include <ostream>
#include <fstream>
#include <map>
#include <mia/core.hh>
#include <queue>
#include <mia/internal/main.hh>
#include <mia/2d/imageio.hh>
#include <mia/2d/filter.hh>
#include <mia/2d/ica.hh>
NS_MIA_USE;
using namespace std;
const SProgramDescription g_description = {
{pdi_group, "Tools for Myocardial Perfusion Analysis"},
{pdi_short, "Evaluate the time-intensity correlation in a series of images."},
{pdi_description, "Given a set of images of temporal sucession, evaluates images that represent "
"the time-intensity correlation in horizontal and vertical direction as "
"well as average correlation of each pixel with its neighbors. "
"All input images must be of the same pixel type and size."},
{pdi_example_descr,"Evaluate the time-intensity correaltions for an image series "
"imageXXXX.png starting at image 2 and stop at image 30. "
"Store the results in horizontal.exr, and vertical.exr."},
{pdi_example_code, "-i image0000.png -k 2 -e 30 -z horizontal.exr -t vertical.exr"}
};
struct FCorrelationAccumulator : public TFilter<bool> {
FCorrelationAccumulator(const C2DBounds & size);
template <typename T>
bool operator ()(const T2DImage<T>& image);
P2DImage get_horizontal_corr() const;
P2DImage get_vertical_corr() const;
P2DImage get_avg_corr() const;
private:
void evaluate_ver()const;
void evaluate_hor()const;
C2DDImage sx2;
C2DDImage sxy_horizontal;
C2DDImage sxy_vertical;
C2DDImage sx;
C2DDImage sy;
C2DBounds size;
mutable C2DFImage *corr_hor;
mutable P2DImage pcorr_hor;
mutable C2DFImage *corr_ver;
mutable P2DImage pcorr_ver;
size_t len;
};
int do_main( int argc, char *argv[] )
{
string src_name("data0000.exr");
string out_name("output.v");
string out_hor_name("horizontal.v");
string out_ver_name("vertical.v");
size_t first = 2;
size_t last = 60;
const auto& image2dio = C2DImageIOPluginHandler::instance();
CCmdOptionList options(g_description);
options.add(make_opt( src_name, "in-base", 'i', "input file name base", CCmdOptionFlags::required_input, &image2dio));
options.add(make_opt( src_name, "outname", 'o', "output file name to save the avarage per-pixel correlation",
CCmdOptionFlags::required_output, &image2dio));
options.add(make_opt( out_hor_name, "horizontal", 'z', "horiZontal correlation output file name",
CCmdOptionFlags::output, &image2dio));
options.add(make_opt( out_ver_name, "vertical", 't', "verTical correlation output file name",
CCmdOptionFlags::output, &image2dio));
options.add(make_opt( first, "skip", 'k', "skip images at beginning of series"));
options.add(make_opt( last, "end", 'e', "last image in series"));
if (options.parse(argc, argv) != CCmdOptionList::hr_no)
return EXIT_SUCCESS;
size_t start_filenum = 0;
size_t end_filenum = 0;
size_t format_width = 0;
string src_basename = get_filename_pattern_and_range(src_name, start_filenum, end_filenum, format_width);
if (start_filenum < first)
start_filenum = first;
if (end_filenum > last)
end_filenum = last;
// load images
vector<P2DImage> series;
for (size_t i = start_filenum; i < end_filenum; ++i) {
string src_name = create_filename(src_basename.c_str(), i);
P2DImage image = load_image<P2DImage>(src_name);
series.push_back(image );
}
// evaluate all series correlation coefficients
cvmsg()<< "Got series of " << series.size() << " images\n";
FCorrelationAccumulator acc(series[0]->get_size());
for (auto i = series.begin(); i != series.end(); ++i)
::mia::accumulate(acc, **i);
P2DImage hor = acc.get_horizontal_corr();
P2DImage ver = acc.get_vertical_corr();
P2DImage avgcorr = acc.get_avg_corr();
if (!save_image(out_hor_name, hor))
throw create_exception<runtime_error>( "unable to save horizontal correlation to '", out_hor_name,"'");
if (!save_image(out_ver_name, ver))
throw create_exception<runtime_error>( "unable to save vertical correlation to '", out_ver_name, "'");
if (!save_image(out_name, avgcorr))
throw create_exception<runtime_error>( "unable to save average correlation to '", out_name, "'");
return EXIT_SUCCESS;
};
FCorrelationAccumulator::FCorrelationAccumulator(const C2DBounds & _size):
sx2(_size),
sxy_horizontal(_size),
sxy_vertical(_size),
sx(_size),
size(_size),
corr_hor(nullptr),
corr_ver(nullptr),
len(0)
{
}
template <typename T>
bool FCorrelationAccumulator::operator ()(const T2DImage<T>& image)
{
if (image.get_size() != size)
throw create_exception<invalid_argument>( "Input image size ", size, " expected, but got ", image.get_size());
// sum x
transform(image.begin(), image.end(), sx.begin(), sx.begin(),
[](T x, double y){return x + y;});
// sum x^2
transform(image.begin(), image.end(), sx2.begin(), sx2.begin(),
[](double x, double y){return x*x + y;});
// sum horizontal
for (size_t y = 0; y < size.y; ++y) {
auto irow = image.begin_at(0,y);
auto orow = sxy_horizontal.begin_at(0,y);
for (size_t x = 0; x < size.x-1; ++x, ++irow, ++orow) {
*orow += irow[0] * irow[1];
}
}
// sum vertical
for (size_t y = 1; y < size.y; ++y) {
auto irow0 = image.begin_at(0,y-1);
auto irow1 = image.begin_at(0,y);
auto orow = sxy_vertical.begin_at(0,y-1);
for (size_t x = 0; x < size.x; ++x, ++irow0, ++irow1,++orow) {
*orow += *irow0 * *irow1;
}
}
++len;
return true;
}
P2DImage FCorrelationAccumulator::get_horizontal_corr() const
{
if (!pcorr_hor)
evaluate_hor();
return pcorr_hor;
}
void FCorrelationAccumulator::evaluate_hor()const
{
if (!len)
throw create_exception<invalid_argument>( "No input images");
corr_hor = new C2DFImage(C2DBounds(size.x-1, size.y));
pcorr_hor.reset(corr_hor);
for (size_t y = 0; y < size.y; ++y) {
auto irow_xy = sxy_horizontal.begin_at(0,y);
auto irow_xx = sx2.begin_at(0,y);
auto irow_yy = sx2.begin_at(1,y);
auto irow_x = sx.begin_at(0,y);
auto irow_y = sx.begin_at(1,y);
auto orow = corr_hor->begin_at(0,y);
for (size_t x = 1; x < size.x;
++x, ++irow_xy, ++irow_xx, ++irow_yy, ++irow_x, ++irow_y, ++orow) {
const float ssxy = *irow_xy - *irow_x * *irow_y / len;
const float ssxx = *irow_xx - *irow_x * *irow_x / len;
const float ssyy = *irow_yy - *irow_y * *irow_y / len;
if (fabs(ssxx) < 1e-10 && fabs(ssyy) < 1e-10)
*orow = 1.0;
else if (fabs(ssxx) < 1e-10 || fabs(ssyy) < 1e-10)
*orow = 0.0;
else
*orow = (ssxy * ssxy) / (ssxx * ssyy);
}
++irow_xy; ++irow_xx; ++irow_yy; ++irow_x; ++irow_y;
}
}
P2DImage FCorrelationAccumulator::get_vertical_corr() const
{
if (!pcorr_ver)
evaluate_ver();
return pcorr_ver;
}
void FCorrelationAccumulator::evaluate_ver()const
{
if (!len)
throw create_exception<invalid_argument>( "No input images");
corr_ver = new C2DFImage(C2DBounds(size.x, size.y-1));
pcorr_ver.reset(corr_ver);
for (size_t y = 0; y < size.y-1; ++y) {
auto irow_xy = sxy_vertical.begin_at(0,y);
auto irow_xx = sx2.begin_at(0,y);
auto irow_yy = sx2.begin_at(0,y+1);
auto irow_x = sx.begin_at(0,y);
auto irow_y = sx.begin_at(0,y+1);
auto orow = corr_ver->begin_at(0,y);
for (size_t x = 0; x < size.x;
++x, ++irow_xy, ++irow_xx, ++irow_yy, ++irow_x, ++irow_y, ++orow) {
const float ssxy = *irow_xy - *irow_x * *irow_y / len;
const float ssxx = *irow_xx - *irow_x * *irow_x / len;
const float ssyy = *irow_yy - *irow_y * *irow_y / len;
if (ssxx == 0 && ssyy == 0)
*orow = 1.0;
else if (ssxx == 0 || ssyy == 0)
*orow = 0.0;
else
*orow = (ssxy * ssxy) / (ssxx * ssyy);
}
}
}
P2DImage FCorrelationAccumulator::get_avg_corr() const
{
if (!pcorr_ver)
evaluate_ver();
if (!pcorr_hor)
evaluate_hor();
C2DFImage *result= new C2DFImage(C2DBounds(size.x, size.y));
P2DImage presult(result);
const C2DFImage& h = *corr_hor;
const C2DFImage& v = *corr_ver;
auto r = result->begin();
auto ch = h.begin();
auto cv = v.begin();
*r++ = (*ch + *cv++) * 0.5f;
for (size_t x = 1; x < size.x-1; ++x, ++r, ++ch, ++cv) {
*r = (*ch + ch[1] + *cv) * 1.0f/3.0f;
}
*r++ = (*ch++ + *cv++) * 0.5f;
auto cvm = corr_ver->begin();
for (size_t y = 1; y < size.y-1; ++y) {
*r++ = (*ch + *cv++ + *cvm++) * 1.0f/3.0f;
for (size_t x = 1; x < size.x-1; ++x, ++r, ++ch, ++cv, ++cvm)
*r = (*ch + ch[1] + *cv + *cvm) * 0.25f;
*r++ = (*ch++ + *cv++ + *cvm++) * 1.0f/3.0f;
}
assert(cv == v.end());
*r++ = (*ch + *cvm++) * 0.5f;
for (size_t x = 1; x < size.x-1; ++x, ++r, ++ch, ++cvm) {
*r = (*ch + ch[1] + *cvm) * 1.0f/3.0f;
}
*r++ = (*ch++ + *cvm++) * 0.5f;
assert(ch == h.end());
assert(cvm == v.end());
return presult;
}
MIA_MAIN(do_main);
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