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imageAndMask2Report.cxx
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#include "itkGDCMImageIO.h"
#include "itkGDCMSeriesFileNames.h"
#include "itkImageFileWriter.h"
#include "itkImageSeriesReader.h"
#include "itkMetaDataObject.h"
#include "itkBinaryBallStructuringElement.h"
#include "itkBinaryDilateImageFilter.h"
#include "itkBinaryErodeImageFilter.h"
#include "itkImageAdaptor.h"
#include "itkImageRegionConstIterator.h"
#include "itkImageRegionIterator.h"
#include "itkRGBPixel.h"
#include "itkMinimumMaximumImageCalculator.h"
#include "itkScalarImageToHistogramGenerator.h"
#include "itkConnectedComponentImageFilter.h"
#include "itkLabelImageToShapeLabelMapFilter.h"
#include "itkLabelShapeKeepNObjectsImageFilter.h"
#include "itkDiscreteGaussianImageFilter.h"
#include "gdcmAnonymizer.h"
#include "gdcmAttribute.h"
#include "gdcmDataSetHelper.h"
#include "gdcmDirectoryHelper.h"
#include "gdcmFileDerivation.h"
#include "gdcmFileExplicitFilter.h"
#include "gdcmGlobal.h"
#include "gdcmImageApplyLookupTable.h"
#include "gdcmImageChangePlanarConfiguration.h"
#include "gdcmImageChangeTransferSyntax.h"
#include "gdcmImageHelper.h"
#include "gdcmImageReader.h"
#include "gdcmImageWriter.h"
#include "gdcmMediaStorage.h"
#include "gdcmReader.h"
#include "gdcmRescaler.h"
#include "gdcmStringFilter.h"
#include "gdcmUIDGenerator.h"
#include "itkConstantPadImageFilter.h"
#include "itkShrinkImageFilter.h"
#include "itkAddImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkDenseFrequencyContainer2.h"
#include "itkHistogramToTextureFeaturesFilter.h"
#include "itkMultiplyImageFilter.h"
#include "itkRegionOfInterestImageFilter.h"
#include "itkScalarImageToCooccurrenceMatrixFilter.h"
#include "itkScalarImageToRunLengthMatrixFilter.h"
#include "itkContinuousIndex.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkGDCMImageIO.h"
#include "itkMetaDataDictionary.h"
#include "json.hpp"
#include "metaCommand.h"
#include <boost/algorithm/string.hpp>
#include <boost/date_time.hpp>
#include <boost/filesystem.hpp>
#include <boost/math/interpolators/catmull_rom.hpp>
#include <codecvt>
#include <locale> // wstring_convert
#include <map>
#include <zip.h>
#include "mytypes.h"
#include "report.h"
using json = nlohmann::json;
using namespace boost::filesystem;
json resultJSON;
bool verbose = false;
using ImageType2D = itk::Image<PixelType, 2>;
using MaskImageType2D = itk::Image<PixelType, 2>;
using CPixelType = itk::RGBPixel<unsigned char>;
using CImageType = itk::Image<CPixelType, 2>;
using LabelType = unsigned short;
using ShapeLabelObjectType = itk::ShapeLabelObject<LabelType, 3>;
using LabelMapType = itk::LabelMap<ShapeLabelObjectType>;
typedef itk::Image<PixelType, 3> ImageType3D;
struct generateImageReturn
{
CImageType::Pointer keyImage;
std::vector< std::array<int, 2> > pos;
std::vector< std::string > text;
std::vector<int> roi_order;
};
// generate a key image based on an input image mask and the ground truth image
// This key image generator shall generate a mosaic of images, one for each lesion.
generateImageReturn generateKeyImageMosaic(ImageType3D::Pointer image, LabelMapType *labelMap, std::vector<int> resolution, float lowerT, float upperT) {
if (verbose) {
fprintf(stdout, "Start generating a key image...\n");
}
std::vector<std::vector<float>> labelColors2 = {{0, 0, 0}, {166,206,227}, {31,120,180}, {178,223,138}, {51,160,44}, {251,154,153}, {227,26,28}, {253,191,111}, {255,127,0}, {202,178,214}, {106,61,154}, {255,255,153}, {177,89,40}};
generateImageReturn returns; // store keyImage and the location and text that should be presented ontop of the image
// based on the number of labels we will create an image mosaic (MPR for each region of interest)
int numObjects = labelMap->GetNumberOfLabelObjects();
if (numObjects < 1) {
fprintf(stderr, "Error: no object found, refuse to create a key image\n");
return(returns);
}
// lets use the base image resolution of
int base_image_sizeLW = resolution[0]/1.618; // we will put an axial square image next to coronal and sagittal on top of each other
int base_image_sizeLH = base_image_sizeLW;
int base_image_sizeRW = floor(base_image_sizeLW / 1.618);
int base_image_sizeRH = floor(base_image_sizeLH / 2.0);
// the total size of the image is now
resolution[0] = base_image_sizeLW + base_image_sizeRW;
resolution[1] = numObjects * base_image_sizeLH;
// create a new RGB image
CImageType::Pointer keyImage = CImageType::New();
returns.keyImage = keyImage;
using RegionType = itk::ImageRegion<2>;
RegionType::SizeType size;
size[0] = resolution[0]; // 512
size[1] = resolution[1]; // 512
RegionType::IndexType index;
index.Fill(0);
RegionType region(index, size);
keyImage->SetRegions(region);
keyImage->Allocate();
keyImage->FillBuffer(itk::NumericTraits<CPixelType>::Zero);
RegionType outputRegion = keyImage->GetLargestPossibleRegion();
//itk::ImageRegionIteratorWithIndex<CImageType> outputRGBIterator(keyImage, outputRegion);
//
// Compute the independently blurred channels for the fused image and all colors in 3D
//
//
// compute optimal window level for whole image, we will use the values for the curved slice
//
using ImageCalculatorFilterType = itk::MinimumMaximumImageCalculator<ImageType3D>;
ImageCalculatorFilterType::Pointer imageCalculatorFilter = ImageCalculatorFilterType::New();
imageCalculatorFilter->SetImage(image);
imageCalculatorFilter->Compute();
int minGray = imageCalculatorFilter->GetMinimum();
int maxGray = imageCalculatorFilter->GetMaximum();
using HistogramGeneratorType = itk::Statistics::ScalarImageToHistogramGenerator<ImageType3D>;
HistogramGeneratorType::Pointer histogramGenerator = HistogramGeneratorType::New();
histogramGenerator->SetInput(image);
int histogramSize = 1024;
histogramGenerator->SetNumberOfBins(histogramSize);
histogramGenerator->SetHistogramMin(minGray);
histogramGenerator->SetHistogramMax(maxGray);
histogramGenerator->Compute();
using HistogramType = HistogramGeneratorType::HistogramType;
const HistogramType *histogram = histogramGenerator->GetOutput();
// lowerT and upperT in percentages 0..1 are set in calling function
double t1 = -1;
double t2 = -1;
double sum = 0;
double total = 0;
for (unsigned int bin = 0; bin < histogramSize; bin++) {
total += histogram->GetFrequency(bin, 0);
}
for (unsigned int bin = 0; bin < histogramSize; bin++) {
double f = histogram->GetFrequency(bin, 0) / total;
// fprintf(stdout, "bin %d, value is %f\n", bin, f);
sum += f;
if (t1 == -1 && sum > lowerT) {
t1 = minGray + (maxGray - minGray) * (bin / (float)histogramSize);
}
if (t2 == -1 && sum > upperT) {
t2 = minGray + (maxGray - minGray) * (bin / (float)histogramSize);
break;
}
}
//if (verbose) {
// fprintf(stdout, "calculated best threshold low: %f, high: %f\n", t1, t2);
//}
// we need three 3D images for the red green and blue channel
// so we can blurr them before using them in the output image
typedef float FPixelType;
// typedef itk::Image<FPixelType, 2> FloatImageType;
using FloatImageType = itk::Image<FPixelType, 3>;
FloatImageType::Pointer red_channel = FloatImageType::New();
FloatImageType::Pointer green_channel = FloatImageType::New();
FloatImageType::Pointer blue_channel = FloatImageType::New();
ImageType3D::RegionType fusedRegion = image->GetLargestPossibleRegion();
red_channel->SetRegions(fusedRegion);
red_channel->Allocate();
red_channel->FillBuffer(itk::NumericTraits<FPixelType>::Zero);
red_channel->SetOrigin(image->GetOrigin());
red_channel->SetSpacing(image->GetSpacing());
red_channel->SetDirection(image->GetDirection());
green_channel->SetRegions(fusedRegion);
green_channel->Allocate();
green_channel->FillBuffer(itk::NumericTraits<FPixelType>::Zero);
green_channel->SetOrigin(image->GetOrigin());
green_channel->SetSpacing(image->GetSpacing());
green_channel->SetDirection(image->GetDirection());
blue_channel->SetRegions(fusedRegion);
blue_channel->Allocate();
blue_channel->FillBuffer(itk::NumericTraits<FPixelType>::Zero);
blue_channel->SetOrigin(image->GetOrigin());
blue_channel->SetSpacing(image->GetSpacing());
blue_channel->SetDirection(image->GetDirection());
std::vector<std::pair<int,int>> sizeByROI;
for (unsigned int roi = 0; roi < labelMap->GetNumberOfLabelObjects(); ++roi) {
ShapeLabelObjectType *labelObject = labelMap->GetNthLabelObject(roi); // the label number is the connected component number - not the one label as mask
sizeByROI.push_back(std::make_pair(labelObject->GetNumberOfPixels(), roi));
}
std::sort(sizeByROI.begin(), sizeByROI.end(), [](auto &left, auto &right) {
return left.first > right.first; // largest roi first
});
// add info as roi_order for later
//for (int roi_idx = 0; roi_idx < labelMap->GetNumberOfLabelObjects(); ++roi_idx) {
// returns.roi_order.push_back(sizeByROI[roi_idx].second);
//}
// here we need to compute using the label order, later when we look at the results we will use the returned roi_order?
// but we don't need both!!
for (unsigned int n_idx = 0; n_idx < labelMap->GetNumberOfLabelObjects(); n_idx++) {
unsigned int n = sizeByROI[n_idx].second;
ShapeLabelObjectType *labelObject = labelMap->GetNthLabelObject(n); // the label number is the connected component number - not the one label as mask
int label = labelObject->GetLabel(); // it might be that all regions have the same label and only n is a good choice for the color
// color is
std::vector<float> col = labelColors2[ (label % (labelColors2.size()-1)) +1 ];
itk::Index<3U> index;
for (unsigned int pixelId = 0; pixelId < labelObject->Size(); pixelId++) {
index = labelObject->GetIndex(pixelId);
// set this position in all three color images, values are between 0 and 1
red_channel->SetPixel(index, col[0]/255.0);
green_channel->SetPixel(index, col[1]/255.0);
blue_channel->SetPixel(index, col[2]/255.0);
}
}
// smooth all three channels independently from each other
using GFilterType = itk::DiscreteGaussianImageFilter<FloatImageType, FloatImageType>;
auto gaussFilterR = GFilterType::New();
gaussFilterR->SetInput(red_channel);
gaussFilterR->SetVariance(1.5f);
gaussFilterR->Update();
FloatImageType::Pointer smoothRed = gaussFilterR->GetOutput();
auto gaussFilterG = GFilterType::New();
gaussFilterG->SetInput(green_channel);
gaussFilterG->SetVariance(1.5f);
gaussFilterG->Update();
FloatImageType::Pointer smoothGreen = gaussFilterG->GetOutput();
auto gaussFilterB = GFilterType::New();
gaussFilterB->SetInput(blue_channel);
gaussFilterB->SetVariance(1.5f);
gaussFilterB->Update();
FloatImageType::Pointer smoothBlue = gaussFilterB->GetOutput();
itk::ImageRegionIterator<FloatImageType> redSIterator(smoothRed, fusedRegion);
itk::ImageRegionIterator<FloatImageType> greenSIterator(smoothGreen, fusedRegion);
itk::ImageRegionIterator<FloatImageType> blueSIterator(smoothBlue, fusedRegion);
itk::LinearInterpolateImageFunction<ImageType3D, double>::Pointer interpolator =
itk::LinearInterpolateImageFunction<ImageType3D, double>::New();
interpolator->SetInputImage(image);
itk::LinearInterpolateImageFunction<FloatImageType, double>::Pointer interpolatorRed =
itk::LinearInterpolateImageFunction<FloatImageType, double>::New();
interpolatorRed->SetInputImage(smoothRed);
itk::LinearInterpolateImageFunction<FloatImageType, double>::Pointer interpolatorGreen =
itk::LinearInterpolateImageFunction<FloatImageType, double>::New();
interpolatorGreen->SetInputImage(smoothGreen);
itk::LinearInterpolateImageFunction<FloatImageType, double>::Pointer interpolatorBlue =
itk::LinearInterpolateImageFunction<FloatImageType, double>::New();
interpolatorBlue->SetInputImage(smoothBlue);
float f = 0.4; // weight of the underlay, 0.1 is mostly mask visible
// TODO: sort roi's by size and start with the largest region of interest (top of the image)
for (unsigned int roi_idx = 0; roi_idx < sizeByROI.size(); ++roi_idx) {
unsigned int roi = sizeByROI[roi_idx].second;
ShapeLabelObjectType *labelObject = labelMap->GetNthLabelObject(roi); // the label number is the connected component number - not the one label as mask
int label = labelObject->GetLabel();
// do this for the left side
int targetLocationStart[2];
targetLocationStart[0] = 0;
targetLocationStart[1] = roi_idx * base_image_sizeLH;
int targetLocationEnd[2];
targetLocationEnd[0] = targetLocationStart[0] + base_image_sizeLW;
targetLocationEnd[1] = targetLocationStart[1] + base_image_sizeLH;
RegionType::SizeType targetSize;
targetSize[0] = targetLocationEnd[0] - targetLocationStart[0];
targetSize[1] = targetLocationEnd[1] - targetLocationStart[1];
RegionType::IndexType targetIndex;
targetIndex[0] = targetLocationStart[0];
targetIndex[1] = targetLocationStart[1];
// compute the bounding box for this object in all three orientations
std::vector<double> boundingBox(6); // three coordinates and one label value (in physical coordinates)
using PT = typename ImageType3D::PointType;
PT floatIndexA; // a single point's coordinate (in world coordinates)
itk::Index<3U> index;
for (unsigned int pixelId = 0; pixelId < labelObject->Size(); pixelId++) {
index = labelObject->GetIndex(pixelId);
// get the position in floating point from the index
image->TransformIndexToPhysicalPoint(index, floatIndexA);
if (pixelId == 0) {
boundingBox[0] = floatIndexA[0];
boundingBox[1] = floatIndexA[1];
boundingBox[2] = floatIndexA[2];
boundingBox[3] = floatIndexA[0];
boundingBox[4] = floatIndexA[1];
boundingBox[5] = floatIndexA[2];
}
if (boundingBox[0] > floatIndexA[0])
boundingBox[0] = floatIndexA[0];
if (boundingBox[1] > floatIndexA[1])
boundingBox[1] = floatIndexA[1];
if (boundingBox[2] > floatIndexA[2])
boundingBox[2] = floatIndexA[2];
if (boundingBox[3] < floatIndexA[0])
boundingBox[3] = floatIndexA[0];
if (boundingBox[4] < floatIndexA[1])
boundingBox[4] = floatIndexA[1];
if (boundingBox[5] < floatIndexA[2])
boundingBox[5] = floatIndexA[2];
}
// now make the bounding box bigger
std::vector<double> biggerBB(6);
// A bounding box can be too small if the label is tiny, we should enlarge the
// bounding box suffiently to reach a minimum of maybe 30% of the space in the volume.
float enlarge[3];
enlarge[0] = (boundingBox[3]-boundingBox[0]);
enlarge[1] = (boundingBox[4]-boundingBox[1]);
enlarge[2] = (boundingBox[5]-boundingBox[2]);
// if total volume still too small make it even larger, boundingBox size is in mm
if (enlarge[0]*3 < 100)
enlarge[0] = 100 - enlarge[0];
if (enlarge[1]*3 < 100)
enlarge[1] = 100 - enlarge[1];
if (enlarge[2]*3 < 100)
enlarge[2] = 100 - enlarge[2];
biggerBB[0] = boundingBox[0] - enlarge[0];
biggerBB[1] = boundingBox[1] - enlarge[1];
biggerBB[2] = boundingBox[2] - enlarge[2];
biggerBB[3] = boundingBox[3] + enlarge[0];
biggerBB[4] = boundingBox[4] + enlarge[1];
biggerBB[5] = boundingBox[5] + enlarge[2];
for (int counter = 0; counter < 6; counter++)
boundingBox[counter] = biggerBB[counter];
// by definition our label is in the center of the picture
returns.pos.push_back(std::array<int, 2>{(int)floor(targetLocationStart[0] + (targetLocationEnd[0]-targetLocationStart[0])/2.0), (int)floor(targetLocationStart[1] + (targetLocationEnd[1]-targetLocationStart[1])/2.0)});
returns.text.push_back(std::to_string(label));
// do the next steps 3 times, start with left image
{
RegionType targetRegion(targetIndex, targetSize); // the region we want to fill in the output key image
itk::ImageRegionIteratorWithIndex<CImageType> targetRGBIterator(keyImage, targetRegion);
// finished computing the maximum enclosing bounding box for this region of interest
// do we need that? We could zoom in, but that is costly... with a zoom factor.
// We would want to see the region of interest and double the space around the region.
targetRGBIterator.GoToBegin(); // 2D Volume of curved slice
while (!targetRGBIterator.IsAtEnd() ) {
CPixelType value = targetRGBIterator.Value(); // we ignore this value, will be filled in with correct one at the end
CImageType::IndexType idx = targetRGBIterator.GetIndex();
// the current output pixel (idx) in percentage of output image (index coordinates)
float idx_percent[2];
idx_percent[0] = ((float)idx[0] - (float)targetLocationStart[0])/((float)targetLocationEnd[0]-(float)targetLocationStart[0]);
idx_percent[1] = ((float)idx[1] - (float)targetLocationStart[1])/((float)targetLocationEnd[1]-(float)targetLocationStart[1]);
// the above should be between 0.0 and 1.0, test here
if (idx_percent[0] < 0.0 || idx_percent[0]>1.0) {
fprintf(stderr, "Error: idx_percent is not 0..1 but %f. Should not happen\n", idx_percent[0]);
}
if (idx_percent[1] < 0.0 || idx_percent[1]>1.0) {
fprintf(stderr, "Error: idx_percent is not 0..1 but %f. Should not happen\n", idx_percent[1]);
}
itk::ContinuousIndex<double, 3> pixel;
itk::ContinuousIndex<double, 3> floatIndexA;
// use x as fastest running index and y as second fast running, third dimension is at mid-point
// TODO: scaling of voxel size is not taken care of. We need to move differently based on pixel size
//auto voxelSize = image->GetSpacing();
float start0 = boundingBox[0];
float start1 = boundingBox[1];
float start2 = boundingBox[2];
float sx = (boundingBox[3]-boundingBox[0]);
float sy = (boundingBox[4]-boundingBox[1]);
float sz = (boundingBox[5]-boundingBox[2]);
if (sx > sy) {
// adjust the other bounding box to the same size, and center it
float midy = start1 + sy/2.0;
start1 = midy - (sx/2.0);
sy = sx;
} else {
// adjust the other bounding box to the same size, and center it
float midx = start0 + sx/2.0;
start0 = midx - (sy/2.0);
sx = sy;
}
// aspect ratio of output image is 1, so bounding box needs to be adjusted in the smaller dimension to have
// same aspect ratio based on larger dimension sx or sy
pixel[0] = (float)start0 + idx_percent[0]*sx; // in pixel coordinates of image, based on boundingBox of one dimension
pixel[1] = (float)start1 + idx_percent[1]*sy;
pixel[2] = (float)start2 + sz/2.0f; // middle
image->TransformPhysicalPointToContinuousIndex(pixel, floatIndexA);
// std::cout << "Value at 1.3: " << interpolator->EvaluateAtContinuousIndex(pixel) << std::endl;
float grayValue = 0;
float redValue = 0;
float blueValue = 0;
float greenValue = 0;
if (interpolator->IsInsideBuffer(floatIndexA)) {
grayValue = interpolator->EvaluateAtContinuousIndex(floatIndexA);
redValue = interpolatorRed->EvaluateAtContinuousIndex(floatIndexA);
greenValue = interpolatorGreen->EvaluateAtContinuousIndex(floatIndexA);
blueValue = interpolatorBlue->EvaluateAtContinuousIndex(floatIndexA);
} else {
//fprintf(stdout, "OUTSIDE region element at location %f %f %f\n", pixel[0], pixel[1], pixel[2]);
//fflush(stdout);
}
float scaledGrayValue = (grayValue - t1) / (t2-t1);
float red = f * scaledGrayValue + (1 - f) * redValue;
float green = f * scaledGrayValue + (1 - f) * greenValue;
float blue = f * scaledGrayValue + (1 - f) * blueValue;
//fprintf(stdout, "before clipping red, green blue: %f %f %f\n", red, green, blue);
red = std::min<float>(1, std::max<float>(0,red));
green = std::min<float>(1, std::max<float>(0,green));
blue = std::min<float>(1, std::max<float>(0,blue));
//fprintf(stdout, "red, green blue: %f %f %f\n", red, green, blue);
//fflush(stdout);
value.SetRed((int)(red * 255));
value.SetGreen((int)(green * 255));
value.SetBlue((int)(blue * 255));
targetRGBIterator.Set(value);
++targetRGBIterator;
}
} // Left image done
{ // Right top image (flip 180)
targetLocationStart[0] = base_image_sizeLW;
targetLocationStart[1] = roi_idx * base_image_sizeLH;
targetLocationEnd[0] = targetLocationStart[0] + base_image_sizeRW;
targetLocationEnd[1] = targetLocationStart[1] + base_image_sizeRH;
RegionType::SizeType targetSize;
targetSize[0] = targetLocationEnd[0] - targetLocationStart[0];
targetSize[1] = targetLocationEnd[1] - targetLocationStart[1];
RegionType::IndexType targetIndex;
targetIndex[0] = targetLocationStart[0];
targetIndex[1] = targetLocationStart[1];
RegionType targetRegion(targetIndex, targetSize); // the region we want to fill in the output key image
itk::ImageRegionIteratorWithIndex<CImageType> targetRGBIterator(keyImage, targetRegion);
// finished computing the maximum inclosing bounding box for this region of interest
// do we need that? We could zoom in, but that is costly... with a zoom factor.
// We would want to see the region of interest and double the space around the region.
targetRGBIterator.GoToBegin(); // 2D Volume of curved slice
while (!targetRGBIterator.IsAtEnd() ) {
CPixelType value = targetRGBIterator.Value(); // we ignore this value, will be filled in with correct one at the end
CImageType::IndexType idx = targetRGBIterator.GetIndex();
// the current output pixel (idx) in percentage of output image (index coordinates)
float idx_percent[2];
idx_percent[0] = ((float)idx[0] - (float)targetLocationStart[0])/((float)targetLocationEnd[0]-(float)targetLocationStart[0]);
idx_percent[1] = ((float)idx[1] - (float)targetLocationStart[1])/((float)targetLocationEnd[1]-(float)targetLocationStart[1]);
idx_percent[1] = 1.0f - idx_percent[1];
// the above should be between 0.0 and 1.0, test here
if (idx_percent[0] < 0.0 || idx_percent[0]>1.0) {
fprintf(stderr, "Error: idx_percent is not 0..1 but %f. Should not happen\n", idx_percent[0]);
}
if (idx_percent[1] < 0.0 || idx_percent[1]>1.0) {
fprintf(stderr, "Error: idx_percent is not 0..1 but %f. Should not happen\n", idx_percent[1]);
}
itk::ContinuousIndex<double, 3> pixel;
itk::ContinuousIndex<double, 3> floatIndexA;
float start0 = boundingBox[0];
float start1 = boundingBox[1];
float start2 = boundingBox[2];
float sx = (boundingBox[3]-boundingBox[0]);
float sy = (boundingBox[4]-boundingBox[1]);
float sz = (boundingBox[5]-boundingBox[2]);
if (sz > sx) {
// adjust the other bounding box to the same size, and center it
float midx = start0 + sx/2.0;
start0 = midx - (sz/2.0);
sx = sz;
} else {
// adjust the other bounding box to the same size, and center it
float midz = start2 + sz/2.0;
start2 = midz - (sx/2.0);
sz = sx;
}
// aspect ratio of output image is 1, so bounding box needs to be adjusted in the smaller dimension to have
// same aspect ratio based on larger dimension sx or sy
pixel[0] = (float)start0 + idx_percent[0]*sx; // in pixel coordinates of image, based on boundingBox of one dimension
pixel[1] = (float)start1 + sy/2.0;
pixel[2] = (float)start2 + idx_percent[1]*sz; // middle
// use x as fastest running index and y as second fast running, third dimension is at mid-point
//pixel[0] = (float)boundingBox[0] + idx_percent[0]*(boundingBox[3]-boundingBox[0]); // in pixel coordinates of image, based on boundingBox of one dimension
//pixel[1] = (float)boundingBox[1] + (boundingBox[4]-boundingBox[1])/2.0f; // middle
//pixel[2] = (float)boundingBox[2] + idx_percent[1]*(boundingBox[5]-boundingBox[2]);
image->TransformPhysicalPointToContinuousIndex(pixel, floatIndexA);
// std::cout << "Value at 1.3: " << interpolator->EvaluateAtContinuousIndex(pixel) << std::endl;
float grayValue = 0;
float redValue = 0;
float blueValue = 0;
float greenValue = 0;
if (interpolator->IsInsideBuffer(floatIndexA)) {
grayValue = interpolator->EvaluateAtContinuousIndex(floatIndexA);
redValue = interpolatorRed->EvaluateAtContinuousIndex(floatIndexA);
greenValue = interpolatorGreen->EvaluateAtContinuousIndex(floatIndexA);
blueValue = interpolatorBlue->EvaluateAtContinuousIndex(floatIndexA);
} else {
//fprintf(stdout, "OUTSIDE region element at location %f %f %f\n", pixel[0], pixel[1], pixel[2]);
//fflush(stdout);
}
float scaledGrayValue = (grayValue - t1) / (t2-t1);
float red = f * scaledGrayValue + (1 - f) * redValue;
float green = f * scaledGrayValue + (1 - f) * greenValue;
float blue = f * scaledGrayValue + (1 - f) * blueValue;
//fprintf(stdout, "before clipping red, green blue: %f %f %f\n", red, green, blue);
red = std::min<float>(1, std::max<float>(0,red));
green = std::min<float>(1, std::max<float>(0,green));
blue = std::min<float>(1, std::max<float>(0,blue));
//fprintf(stdout, "red, green blue: %f %f %f\n", red, green, blue);
//fflush(stdout);
value.SetRed((int)(red * 255));
value.SetGreen((int)(green * 255));
value.SetBlue((int)(blue * 255));
targetRGBIterator.Set(value);
++targetRGBIterator;
}
} // Right top image done
{ // Right bottom image
targetLocationStart[0] = base_image_sizeLW;
targetLocationStart[1] = roi_idx * base_image_sizeLH + base_image_sizeRH;
targetLocationEnd[0] = targetLocationStart[0] + base_image_sizeRW;
targetLocationEnd[1] = targetLocationStart[1] + base_image_sizeRH;
RegionType::SizeType targetSize;
targetSize[0] = targetLocationEnd[0] - targetLocationStart[0];
targetSize[1] = targetLocationEnd[1] - targetLocationStart[1];
RegionType::IndexType targetIndex;
targetIndex[0] = targetLocationStart[0];
targetIndex[1] = targetLocationStart[1];
RegionType targetRegion(targetIndex, targetSize); // the region we want to fill in the output key image
itk::ImageRegionIteratorWithIndex<CImageType> targetRGBIterator(keyImage, targetRegion);
// finished computing the maximum inclosing bounding box for this region of interest
// do we need that? We could zoom in, but that is costly... with a zoom factor.
// We would want to see the region of interest and double the space around the region.
targetRGBIterator.GoToBegin(); // 2D Volume of curved slice
while (!targetRGBIterator.IsAtEnd() ) {
CPixelType value = targetRGBIterator.Value(); // we ignore this value, will be filled in with correct one at the end
CImageType::IndexType idx = targetRGBIterator.GetIndex();
// the current output pixel (idx) in percentage of output image (index coordinates)
float idx_percent[2];
idx_percent[0] = ((float)idx[0] - (float)targetLocationStart[0])/((float)targetLocationEnd[0]-(float)targetLocationStart[0]);
idx_percent[1] = ((float)idx[1] - (float)targetLocationStart[1])/((float)targetLocationEnd[1]-(float)targetLocationStart[1]);
// the above should be between 0.0 and 1.0, test here
idx_percent[1] = 1.0f - idx_percent[1];
if (idx_percent[0] < 0.0 || idx_percent[0]>1.0) {
fprintf(stderr, "Error: idx_percent is not 0..1 but %f. Should not happen\n", idx_percent[0]);
}
if (idx_percent[1] < 0.0 || idx_percent[1]>1.0) {
fprintf(stderr, "Error: idx_percent is not 0..1 but %f. Should not happen\n", idx_percent[1]);
}
itk::ContinuousIndex<double, 3> pixel;
itk::ContinuousIndex<double, 3> floatIndexA;
float start0 = boundingBox[0];
float start1 = boundingBox[1];
float start2 = boundingBox[2];
float sx = (boundingBox[3]-boundingBox[0]);
float sy = (boundingBox[4]-boundingBox[1]);
float sz = (boundingBox[5]-boundingBox[2]);
if (sz > sy) {
// adjust the other bounding box to the same size, and center it
float midy = start1 + sy/2.0;
start1 = midy - (sz/2.0);
sy = sz;
} else {
// adjust the other bounding box to the same size, and center it
float midz = start2 + sz/2.0;
start2 = midz - (sy/2.0);
sz = sy;
}
// aspect ratio of output image is 1, so bounding box needs to be adjusted in the smaller dimension to have
// same aspect ratio based on larger dimension sx or sy
pixel[0] = (float)start0 + sx/2.0;; // in pixel coordinates of image, based on boundingBox of one dimension
pixel[1] = (float)start1 + idx_percent[0]*sy;
pixel[2] = (float)start2 + idx_percent[1]*sz; // middle
// use x as fastest running index and y as second fast running, third dimension is at mid-point
//pixel[0] = (float)boundingBox[0] + (boundingBox[3]-boundingBox[0])/2.0f; // middle
//pixel[1] = (float)boundingBox[1] + idx_percent[0]*(boundingBox[4]-boundingBox[1]); // in pixel coordinates of image, based on boundingBox of one dimension
//pixel[2] = (float)boundingBox[2] + idx_percent[1]*(boundingBox[5]-boundingBox[2]);
image->TransformPhysicalPointToContinuousIndex(pixel, floatIndexA);
// std::cout << "Value at 1.3: " << interpolator->EvaluateAtContinuousIndex(pixel) << std::endl;
float grayValue = 0;
float redValue = 0;
float blueValue = 0;
float greenValue = 0;
if (interpolator->IsInsideBuffer(floatIndexA)) {
grayValue = interpolator->EvaluateAtContinuousIndex(floatIndexA);
redValue = interpolatorRed->EvaluateAtContinuousIndex(floatIndexA);
greenValue = interpolatorGreen->EvaluateAtContinuousIndex(floatIndexA);
blueValue = interpolatorBlue->EvaluateAtContinuousIndex(floatIndexA);
} else {
//fprintf(stdout, "OUTSIDE region element at location %f %f %f\n", pixel[0], pixel[1], pixel[2]);
//fflush(stdout);
}
float scaledGrayValue = (grayValue - t1) / (t2-t1);
float red = f * scaledGrayValue + (1 - f) * redValue;
float green = f * scaledGrayValue + (1 - f) * greenValue;
float blue = f * scaledGrayValue + (1 - f) * blueValue;
//fprintf(stdout, "before clipping red, green blue: %f %f %f\n", red, green, blue);
red = std::min<float>(1, std::max<float>(0,red));
green = std::min<float>(1, std::max<float>(0,green));
blue = std::min<float>(1, std::max<float>(0,blue));
//fprintf(stdout, "red, green blue: %f %f %f\n", red, green, blue);
//fflush(stdout);
value.SetRed((int)(red * 255));
value.SetGreen((int)(green * 255));
value.SetBlue((int)(blue * 255));
targetRGBIterator.Set(value);
++targetRGBIterator;
}
} // Right bottom image done
}
return returns;
}
// generate a key image based on the input image and a mask (fused with names)
// test:
// ./imageAndMask2Report data/ror_trigger_run_Wednesday_980595789/ror_trigger_run_Wednesday_980595789/input data/ror_trigger_run_Wednesday_980595789/ror_trigger_run_Wednesday_980595789_output/labels/508bc8c54546f0c3383f4325ec6fa70e310328932af7bffcf812079391445.1/ /tmp/bla -u | less
generateImageReturn generateKeyImage(ImageType3D::Pointer image, LabelMapType *labelMap, std::vector<int> resolution, float lowerT, float upperT) {
if (verbose) {
fprintf(stdout, "Start generating a key image...\n");
}
std::vector<std::vector<float>> labelColors2 = {{0, 0, 0}, {166,206,227}, {31,120,180}, {178,223,138}, {51,160,44}, {251,154,153}, {227,26,28}, {253,191,111}, {255,127,0}, {202,178,214}, {106,61,154}, {255,255,153}, {177,89,40}};
generateImageReturn returns; // store keyImage and the location and text that should be presented ontop of the image
// create a new RGB image
CImageType::Pointer keyImage = CImageType::New();
returns.keyImage = keyImage;
using RegionType = itk::ImageRegion<2>;
RegionType::SizeType size;
size[0] = resolution[0]; // 512
size[1] = resolution[1]; // 512
RegionType::IndexType index;
index.Fill(0);
RegionType region(index, size);
keyImage->SetRegions(region);
keyImage->Allocate();
keyImage->FillBuffer(itk::NumericTraits<CPixelType>::Zero);
//
// for each label compute the center of mass for a spline function
//
std::vector< std::array<double, 4> > centers; // three coordinates and one label value (in physical coordinates)
std::array<double, 3> minPos, maxPos;
// keep a map of all pixel coordinates with label
//std::map< std::string, int > coord2Label;
for (unsigned int n = 0; n < labelMap->GetNumberOfLabelObjects(); ++n) {
// these are all in order
returns.roi_order.push_back(n);
ShapeLabelObjectType *labelObject = labelMap->GetNthLabelObject(n); // the label number is the connected component number - not the one label as mask
int label = labelObject->GetLabel();
using PT = typename ImageType3D::PointType;
PT floatIndexA; // a single points coordinates in world coordinates
PT centerHere; // we accumulate positions here to compute the center of mass for this object in 3D (world coordinates)
centerHere[0] = 0.0f;
centerHere[1] = 0.0f;
centerHere[2] = 0.0f;
itk::Index<3U> index;
for (unsigned int pixelId = 0; pixelId < labelObject->Size(); pixelId++) {
index = labelObject->GetIndex(pixelId);
// if we compute the key like this we can get the label for it,
// not being in the map means that we have a background pixel
//std::string key = std::to_string(index[0]) + "_" + std::to_string(index[1]) + "_" + std::to_string(index[2]);
//coord2Label.insert( std::make_pair(key, label) );
// get the position in floating point from the index
image->TransformIndexToPhysicalPoint(index, floatIndexA);
centerHere[0] += floatIndexA[0];
centerHere[1] += floatIndexA[1];
centerHere[2] += floatIndexA[2];
}
centerHere[0] /= labelObject->Size(); // center of mass
centerHere[1] /= labelObject->Size();
centerHere[2] /= labelObject->Size();
centers.push_back(std::array<double, 4>{centerHere[0], centerHere[1], centerHere[2], (double)label}); // keep track of the label (connected component generated int value)
if (n == 0) { // init
minPos[0] = centerHere[0]; minPos[1] = centerHere[1]; minPos[2] = centerHere[2];
maxPos[0] = centerHere[0]; maxPos[1] = centerHere[1]; maxPos[2] = centerHere[2];
} else {
for (int i = 0; i < 3; i++) {
if (centerHere[i] < minPos[i]) {
minPos[i] = centerHere[i];
}
if (centerHere[i] > maxPos[i]) {
maxPos[i] = centerHere[i];
}
}
}
}
if (centers.size() == 0) {
// nothing to do, return here
if (verbose)
fprintf(stdout, "nothing to do here, no centers found\n");
return returns;
}
// compute the extend in all three dimensions and the index of the longest axis
std::vector<double> dists{(maxPos[0]-minPos[0]),(maxPos[1]-minPos[1]),(maxPos[2]-minPos[2])};
int directionLongestAxis = std::max_element(dists.begin(), dists.end()) - dists.begin();
if (verbose)
fprintf(stdout, "direction longest axis is: %d\n", directionLongestAxis);
/*if (verbose)
for (int i = 0; i < centers.size(); i++) {
fprintf(stdout, "centers before sort %d is : %f %f %f\n", i, centers[i][0], centers[i][1], centers[i][2]);
}*/
// we want to interpolate between the points in the right order along the directionLongestAxis
// TODO: find out what the correct axis is based on some DICOM tags
std::sort(centers.begin(), centers.end(), [directionLongestAxis](const std::array<double, 4> a, const std::array<double, 4> b) {
return a.at(directionLongestAxis) > b.at(directionLongestAxis);
});
/*if (verbose)
for (int i = 0; i < centers.size(); i++) {
fprintf(stdout, "centers after sort %d is : %f %f %f\n", i, centers[i][0], centers[i][1], centers[i][2]);
}*/
// add two points at the beginning and at the end to continue to the center curve
auto one = centers[0];
// we can guarantee that we have one region here
if (centers.size() > 1) {
auto two = centers[1];
std::array<double, 4> newBeginning = std::array<double, 4>{ one[0] + (one[0] - two[0]), one[1] + (one[1] - two[1]), one[2] + (one[2] - two[2]), -1 };
centers.insert(centers.begin(), newBeginning);
one = centers[centers.size()-1];
two = centers[centers.size()-2];
std::array<double, 4> newEnding = std::array<double, 4>{ one[0] + (one[0] - two[0]), one[1] + (one[1] - two[1]), one[2] + (one[2] - two[2]), (double)(centers.size()+1) };
centers.push_back(newEnding);
} else {
auto two = std::array<double, 4>{ centers[0][0], centers[0][1], centers[0][2], -1 };
two[directionLongestAxis] += 10.0;
std::array<double, 4> newBeginning = std::array<double, 4>{ one[0] + (one[0] - two[0]), one[1] + (one[1] - two[1]), one[2] + (one[2] - two[2]), -1 };
centers.insert(centers.begin(), newBeginning);
one = centers[centers.size()-1];
two = centers[centers.size()-2];
std::array<double, 4> newEnding = std::array<double, 4>{ one[0] + (one[0] - two[0]), one[1] + (one[1] - two[1]), one[2] + (one[2] - two[2]), (double)(centers.size()+1) };
centers.push_back(newEnding);
}
// create an open spline
// Warning: we need at least 4 objects here
// If we have less than 4 but more than 2 regions we can use a linear function from start to end and hope for the best
// we can evaluate the spline
// TODO: use a linear extension in the direction of cr.prime() (tangent vector)
std::vector< std::vector< double > > interpolatedCenterLocations; // should be resolution[1] many, in physical space
if (centers.size() > 4) {
std::vector< std::array<double, 3> > positions; // make a copy of the first 3 dimensions
for (int p = 0; p < centers.size(); p++) {
positions.push_back( std::array<double, 3>{centers[p][0], centers[p][1], centers[p][2]});
}
boost::math::catmull_rom<std::array<double, 3>> cr(std::move(positions));
double dt = cr.max_parameter()/((float)resolution[1]-1.0);
for (int p = 0; p < resolution[1]; p++) {
float s = (double)p * dt;
auto point = cr( (double)p * dt );
interpolatedCenterLocations.push_back(std::vector<double>{point[0], point[1], point[2]});
//fprintf(stdout, "found a point at %d %f here: %f %f %f\n", p, s, point[0], point[1], point[2]);
}
//fflush(stdout);
} else if (centers.size() > 1) {
for (int p = 0; p < resolution[1]; p++) {
std::vector<double> point;
point.push_back(centers[0][0] + p*(centers[centers.size()-1][0] - centers[0][0])/((double)resolution[1]-1.0) );
point.push_back(centers[0][1] + p*(centers[centers.size()-1][1] - centers[0][1])/((double)resolution[1]-1.0) );
point.push_back(centers[0][2] + p*(centers[centers.size()-1][2] - centers[0][2])/((double)resolution[1]-1.0) );
interpolatedCenterLocations.push_back(std::vector<double>{point[0], point[1], point[2]});
}
} else {
fprintf(stderr, "Error: no key image for less than 2 points!\n");
// TODO: do a 3 axis view
}
// now sample the image, instead of normal and binormal use one of the other two dimensions
// sample dimension is directionLongestAxis+1 % 3
std::vector<double> sampleDirection{0,0,0};
int sampleDimension = (directionLongestAxis-1) % 3;
if (sampleDimension < 0 || sampleDimension > 2)
sampleDimension = 0; // fallback
sampleDirection[sampleDimension] = 1.0;
double stepSize = sqrtf( /*(interpolatedCenterLocations[1][0] - interpolatedCenterLocations[2][0]) * (interpolatedCenterLocations[1][0] - interpolatedCenterLocations[2][0]) +
(interpolatedCenterLocations[1][1] - interpolatedCenterLocations[2][1]) * (interpolatedCenterLocations[1][1] - interpolatedCenterLocations[2][1]) + */
(interpolatedCenterLocations[1][directionLongestAxis] - interpolatedCenterLocations[2][directionLongestAxis]) * (interpolatedCenterLocations[1][directionLongestAxis] - interpolatedCenterLocations[2][directionLongestAxis]) );
if (verbose) {
fprintf(stdout, "sample direction: %f %f %f, stepsize: %f\n", sampleDirection[0], sampleDirection[1], sampleDirection[2], stepSize);
fflush(stdout);
}
// now sample the output image using the coordinates system we established above
using IteratorTypeImage = itk::ImageRegionIteratorWithIndex< ImageType3D >;
// we can use coord2Label to lookup the mask values, maybe color is missing?
// we need to go through each pixel of the output image
RegionType outputRegion = keyImage->GetLargestPossibleRegion();
itk::ImageRegionIteratorWithIndex<CImageType> outputRGBIterator(keyImage, outputRegion);
//
// compute optimal window level for whole image, we will use the values for the curved slice
//
using ImageCalculatorFilterType = itk::MinimumMaximumImageCalculator<ImageType3D>;
ImageCalculatorFilterType::Pointer imageCalculatorFilter = ImageCalculatorFilterType::New();
imageCalculatorFilter->SetImage(image);
imageCalculatorFilter->Compute();
int minGray = imageCalculatorFilter->GetMinimum();
int maxGray = imageCalculatorFilter->GetMaximum();
using HistogramGeneratorType = itk::Statistics::ScalarImageToHistogramGenerator<ImageType3D>;
HistogramGeneratorType::Pointer histogramGenerator = HistogramGeneratorType::New();
histogramGenerator->SetInput(image);
int histogramSize = 1024;
histogramGenerator->SetNumberOfBins(histogramSize);
histogramGenerator->SetHistogramMin(minGray);
histogramGenerator->SetHistogramMax(maxGray);
histogramGenerator->Compute();
using HistogramType = HistogramGeneratorType::HistogramType;
const HistogramType *histogram = histogramGenerator->GetOutput();
// set in calling function
//double lowerT = 0.01;
//double upperT = 0.999;
double t1 = -1;
double t2 = -1;
double sum = 0;
double total = 0;
for (unsigned int bin = 0; bin < histogramSize; bin++) {
total += histogram->GetFrequency(bin, 0);
}
for (unsigned int bin = 0; bin < histogramSize; bin++) {
double f = histogram->GetFrequency(bin, 0) / total;
// fprintf(stdout, "bin %d, value is %f\n", bin, f);
sum += f;
if (t1 == -1 && sum > lowerT) {
t1 = minGray + (maxGray - minGray) * (bin / (float)histogramSize);
}
if (t2 == -1 && sum > upperT) {
t2 = minGray + (maxGray - minGray) * (bin / (float)histogramSize);
break;
}
}
//if (verbose) {
// fprintf(stdout, "calculated best threshold low: %f, high: %f\n", t1, t2);
//}
// we need three 3D images for the red green and blue channel
// so we can blurr them before using them in the output image
typedef float FPixelType;
// typedef itk::Image<FPixelType, 2> FloatImageType;
using FloatImageType = itk::Image<FPixelType, 3>;
FloatImageType::Pointer red_channel = FloatImageType::New();
FloatImageType::Pointer green_channel = FloatImageType::New();
FloatImageType::Pointer blue_channel = FloatImageType::New();
ImageType3D::RegionType fusedRegion = image->GetLargestPossibleRegion();
red_channel->SetRegions(fusedRegion);
red_channel->Allocate();
red_channel->FillBuffer(itk::NumericTraits<FPixelType>::Zero);
red_channel->SetOrigin(image->GetOrigin());
red_channel->SetSpacing(image->GetSpacing());
red_channel->SetDirection(image->GetDirection());
green_channel->SetRegions(fusedRegion);
green_channel->Allocate();
green_channel->FillBuffer(itk::NumericTraits<FPixelType>::Zero);
green_channel->SetOrigin(image->GetOrigin());
green_channel->SetSpacing(image->GetSpacing());
green_channel->SetDirection(image->GetDirection());
blue_channel->SetRegions(fusedRegion);
blue_channel->Allocate();
blue_channel->FillBuffer(itk::NumericTraits<FPixelType>::Zero);
blue_channel->SetOrigin(image->GetOrigin());
blue_channel->SetSpacing(image->GetSpacing());
blue_channel->SetDirection(image->GetDirection());
// itk::ImageRegionIterator<FloatImageType> redIterator(red_channel, fusedRegion);
// itk::ImageRegionIterator<FloatImageType> greenIterator(green_channel, fusedRegion);
// itk::ImageRegionIterator<FloatImageType> blueIterator(blue_channel, fusedRegion);
for (unsigned int n = 0; n < labelMap->GetNumberOfLabelObjects(); n++) {
ShapeLabelObjectType *labelObject = labelMap->GetNthLabelObject(n); // the label number is the connected component number - not the one label as mask
int label = labelObject->GetLabel(); // it might be that all regions have the same label and only n is a good choice for the color
// color is
std::vector<float> col = labelColors2[ (label % (labelColors2.size()-1)) +1 ];
itk::Index<3U> index;
for (unsigned int pixelId = 0; pixelId < labelObject->Size(); pixelId++) {
index = labelObject->GetIndex(pixelId);
// set this position in all three color images, values are between 0 and 1
red_channel->SetPixel(index, col[0]/255.0);
green_channel->SetPixel(index, col[1]/255.0);
blue_channel->SetPixel(index, col[2]/255.0);
}
}
// smooth all three channels independently from each other
using GFilterType = itk::DiscreteGaussianImageFilter<FloatImageType, FloatImageType>;
auto gaussFilterR = GFilterType::New();
gaussFilterR->SetInput(red_channel);
gaussFilterR->SetVariance(1.5f);
gaussFilterR->Update();
FloatImageType::Pointer smoothRed = gaussFilterR->GetOutput();
auto gaussFilterG = GFilterType::New();
gaussFilterG->SetInput(green_channel);
gaussFilterG->SetVariance(1.5f);
gaussFilterG->Update();
FloatImageType::Pointer smoothGreen = gaussFilterG->GetOutput();
auto gaussFilterB = GFilterType::New();
gaussFilterB->SetInput(blue_channel);
gaussFilterB->SetVariance(1.5f);
gaussFilterB->Update();
FloatImageType::Pointer smoothBlue = gaussFilterB->GetOutput();
itk::ImageRegionIterator<FloatImageType> redSIterator(smoothRed, fusedRegion);
itk::ImageRegionIterator<FloatImageType> greenSIterator(smoothGreen, fusedRegion);
itk::ImageRegionIterator<FloatImageType> blueSIterator(smoothBlue, fusedRegion);
// redSIterator.GoToBegin(); // 3D Volume iterators
// greenSIterator.GoToBegin();
// blueSIterator.GoToBegin();
// we want to do a tri-linear lookup in the input image
itk::LinearInterpolateImageFunction<ImageType3D, double>::Pointer interpolator =
itk::LinearInterpolateImageFunction<ImageType3D, double>::New();
interpolator->SetInputImage(image);
itk::LinearInterpolateImageFunction<FloatImageType, double>::Pointer interpolatorRed =