itk/clitkRelativePositionAnalyzerFilter.txx

   1 /*=========================================================================
   2   Program:   vv                     http://www.creatis.insa-lyon.fr/rio/vv
   3
   4   Authors belong to:
   5   - University of LYON              http://www.universite-lyon.fr/
   6   - Léon Bérard cancer center       http://www.centreleonberard.fr
   7   - CREATIS CNRS laboratory         http://www.creatis.insa-lyon.fr
   8
   9   This software is distributed WITHOUT ANY WARRANTY; without even
  10   the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
  11   PURPOSE.  See the copyright notices for more information.
  12
  13   It is distributed under dual licence
  14
  15   - BSD        See included LICENSE.txt file
  16   - CeCILL-B   http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
  17   ===========================================================================**/
  18
  19 //--------------------------------------------------------------------
  20 template <class ImageType>
  21 clitk::RelativePositionAnalyzerFilter<ImageType>::
  22 RelativePositionAnalyzerFilter():
  23   itk::ImageToImageFilter<ImageType, ImageType>()
  24 {
  25   this->SetNumberOfRequiredInputs(3); // Input : support, object, target
  26   SetBackgroundValue(0);
  27   SetForegroundValue(1);
  28   SetNumberOfBins(100);
  29   SetAreaLossTolerance(0.01);
  30   SetSupportSize(0);
  31   SetTargetSize(0);
  32   SetSizeWithThreshold(0);
  33   SetSizeWithReverseThreshold(0);
  34 }
  35 //--------------------------------------------------------------------
  36
  37
  38 //--------------------------------------------------------------------
  39 template <class ImageType>
  40 void
  41 clitk::RelativePositionAnalyzerFilter<ImageType>::
  42 SetInputSupport(const ImageType * image)
  43 {
  44   // Process object is not const-correct so the const casting is required.
  45   this->SetNthInput(0, const_cast<ImageType *>(image));
  46 }
  47 //--------------------------------------------------------------------
  48
  49
  50 //--------------------------------------------------------------------
  51 template <class ImageType>
  52 void
  53 clitk::RelativePositionAnalyzerFilter<ImageType>::
  54 SetInputObject(const ImageType * image)
  55 {
  56   // Process object is not const-correct so the const casting is required.
  57   this->SetNthInput(1, const_cast<ImageType *>(image));
  58 }
  59 //--------------------------------------------------------------------
  60
  61
  62 //--------------------------------------------------------------------
  63 template <class ImageType>
  64 void
  65 clitk::RelativePositionAnalyzerFilter<ImageType>::
  66 SetInputTarget(const ImageType * image)
  67 {
  68   // Process object is not const-correct so the const casting is required.
  69   this->SetNthInput(2, const_cast<ImageType *>(image));
  70 }
  71 //--------------------------------------------------------------------
  72
  73
  74 //--------------------------------------------------------------------
  75 template <class ImageType>
  76 void
  77 clitk::RelativePositionAnalyzerFilter<ImageType>::
  78 PrintOptions()
  79 {
  80   DD("TODO");
  81 }
  82 //--------------------------------------------------------------------
  83
  84
  85 //--------------------------------------------------------------------
  86 template <class ImageType>
  87 void
  88 clitk::RelativePositionAnalyzerFilter<ImageType>::
  89 GenerateOutputInformation()
  90 {
  91   ImagePointer input = dynamic_cast<ImageType*>(itk::ProcessObject::GetInput(0));
  92   ImagePointer outputImage = this->GetOutput(0);
  93   outputImage->SetRegions(outputImage->GetLargestPossibleRegion());
  94 }
  95 //--------------------------------------------------------------------
  96
  97
  98 //--------------------------------------------------------------------
  99 template <class ImageType>
 100 void
 101 clitk::RelativePositionAnalyzerFilter<ImageType>::
 102 GenerateData()
 103 {
 104   ImagePointer temp = dynamic_cast<ImageType*>(itk::ProcessObject::GetInput(0));
 105   m_Object = dynamic_cast<ImageType*>(itk::ProcessObject::GetInput(1));
 106   ImagePointer temp2 = dynamic_cast<ImageType*>(itk::ProcessObject::GetInput(2));
 107
 108   // Remove object from support (keep initial image)
 109   m_Support = clitk::Clone<ImageType>(temp);
 110   clitk::AndNot<ImageType>(m_Support, m_Object, GetBackgroundValue());
 111
 112   // Remove object from target. Important because sometimes, there is
 113   // overlap between target and object.
 114   m_Target = clitk::Clone<ImageType>(temp2);
 115   clitk::AndNot<ImageType>(m_Target, m_Object, GetBackgroundValue());
 116
 117   // Define filter to compute statics on mask image
 118   typedef itk::LabelStatisticsImageFilter<ImageType, ImageType> StatFilterType;
 119   typename StatFilterType::Pointer statFilter = StatFilterType::New();
 120
 121   // Compute the initial support size
 122   statFilter->SetInput(m_Support);
 123   statFilter->SetLabelInput(m_Support);
 124   statFilter->Update();
 125   SetSupportSize(statFilter->GetCount(GetForegroundValue()));
 126   // DD(GetSupportSize());
 127
 128   // Compute the initial target size
 129   ImagePointer s = clitk::ResizeImageLike<ImageType>(m_Support, m_Target, GetBackgroundValue());
 130   statFilter->SetInput(s);
 131   statFilter->SetLabelInput(m_Target);
 132   statFilter->Update();
 133   SetTargetSize(statFilter->GetCount(GetForegroundValue()));
 134   // DD(GetTargetSize());
 135
 136   //
 137   int bins = GetNumberOfBins();
 138   double tolerance = GetAreaLossTolerance();
 139
 140   // Compute Fuzzy map
 141   double angle = GetDirection().angle1;
 142   typename FloatImageType::Pointer map = ComputeFuzzyMap(m_Object, m_Target, m_Support, angle);
 143   writeImage<FloatImageType>(map, "fuzzy_"+toString(clitk::rad2deg(angle))+".mha");
 144
 145   // Compute the optimal thresholds (direct and inverse)
 146   double mThreshold=0.0;
 147   double mReverseThreshold=1.0;
 148   ComputeOptimalThresholds(map, m_Target, bins, tolerance, mThreshold, mReverseThreshold);
 149
 150   // DD(mThreshold);
 151   // DD(mReverseThreshold);
 152
 153   // Use the threshold to compute new support
 154   int s1 = GetSupportSize();
 155   if (mThreshold > 0.0) {
 156     ImagePointer support1 =
 157       clitk::SliceBySliceRelativePosition<ImageType>(m_Support, m_Object, 2,
 158                                                      mThreshold,
 159                                                      angle,false, // inverseFlag
 160                                                      false,  // uniqueConnectedComponent
 161                                                      -1, true,
 162                                                      false);//singleObjectCCL
 163     // Compute the new support size
 164     statFilter->SetInput(support1);
 165     statFilter->SetLabelInput(support1);
 166     statFilter->Update();
 167     s1 = statFilter->GetCount(GetForegroundValue());
 168   }
 169
 170   int s2 = GetSupportSize();
 171   if (mReverseThreshold < 1.0) {
 172     ImagePointer support2 =
 173       clitk::SliceBySliceRelativePosition<ImageType>(m_Support, m_Object, 2,
 174                                                      mReverseThreshold,
 175                                                      angle,true,// inverseFlag
 176                                                      false, // uniqueConnectedComponent
 177                                                      -1, true,
 178                                                      false); //singleObjectCCL
 179     // Compute the new support size
 180     statFilter = StatFilterType::New();
 181     statFilter->SetInput(support2);
 182     statFilter->SetLabelInput(support2);
 183     statFilter->Update();
 184     s2 = statFilter->GetCount(GetForegroundValue());
 185   }
 186
 187   // Check threshold, if we gain nothing, we force to max/min thresholds
 188   // DD(GetSupportSize());
 189   // DD(s1);
 190   // DD(s2);
 191   if (s1 >= GetSupportSize()) mThreshold = 0.0;
 192   if (s2 >= GetSupportSize()) mReverseThreshold = 1.0;
 193
 194   // Set results values
 195   m_Info.threshold = mThreshold;
 196   m_Info.sizeAfterThreshold = s1;
 197   m_Info.sizeBeforeThreshold = GetSupportSize();
 198   m_Info.sizeReference = GetTargetSize();
 199   m_InfoReverse.threshold = mReverseThreshold;
 200   m_InfoReverse.sizeAfterThreshold = s2;
 201   m_InfoReverse.sizeBeforeThreshold = GetSupportSize();
 202   m_InfoReverse.sizeReference = GetTargetSize();
 203 }
 204 //--------------------------------------------------------------------
 205
 206
 207 //--------------------------------------------------------------------
 208 template <class ImageType>
 209 typename clitk::RelativePositionAnalyzerFilter<ImageType>::FloatImageType::Pointer
 210 clitk::RelativePositionAnalyzerFilter<ImageType>::
 211 ComputeFuzzyMap(ImageType * object, ImageType * target, ImageType * support, double angle)
 212 {
 213   typedef clitk::SliceBySliceRelativePositionFilter<ImageType> SliceRelPosFilterType;
 214   typedef typename SliceRelPosFilterType::FloatImageType FloatImageType;
 215   typename SliceRelPosFilterType::Pointer sliceRelPosFilter = SliceRelPosFilterType::New();
 216   sliceRelPosFilter->VerboseStepFlagOff();
 217   sliceRelPosFilter->WriteStepFlagOff();
 218   sliceRelPosFilter->SetInput(support);
 219   sliceRelPosFilter->SetInputObject(object);
 220   sliceRelPosFilter->SetDirection(2);
 221   sliceRelPosFilter->SetIntermediateSpacingFlag(false);
 222   //sliceRelPosFilter->AddOrientationTypeString(orientation);
 223   sliceRelPosFilter->AddAnglesInRad(angle, 0.0);
 224   sliceRelPosFilter->FuzzyMapOnlyFlagOn(); // do not threshold, only compute the fuzzy map
 225   // sliceRelPosFilter->PrintOptions();
 226   sliceRelPosFilter->Update();
 227   typename FloatImageType::Pointer map = sliceRelPosFilter->GetFuzzyMap();
 228   writeImage<FloatImageType>(map, "fuzzy_0_"+toString(clitk::rad2deg(angle))+".mha");
 229
 230   // Resize object like map to allow SetBackground
 231   ImagePointer temp = clitk::ResizeImageLike<ImageType>(object, map, GetBackgroundValue());
 232   //  writeImage<FloatImageType>(map, "fuzzy_1_"+toString(clitk::rad2deg(angle))+".mha");
 233
 234   // Remove initial object from the fuzzy map
 235   map = clitk::SetBackground<FloatImageType, ImageType>(map, temp, GetForegroundValue(), 0.0, true);
 236   writeImage<FloatImageType>(map, "fuzzy_2_"+toString(clitk::rad2deg(angle))+".mha");
 237
 238   // Resize the fuzzy map like the target, put 2.0 when outside
 239   map = clitk::ResizeImageLike<FloatImageType>(map, target, 2.0);  // Put 2.0 when out of initial map
 240   writeImage<FloatImageType>(map, "fuzzy_3_"+toString(clitk::rad2deg(angle))+".mha");
 241
 242   // end
 243   return map;
 244 }
 245 //--------------------------------------------------------------------
 246
 247
 248 //--------------------------------------------------------------------
 249 template <class ImageType>
 250 void
 251 clitk::RelativePositionAnalyzerFilter<ImageType>::
 252 ComputeOptimalThresholds(FloatImageType * map, ImageType * target, int bins, double tolerance,
 253                          double & threshold, double & reverseThreshold)
 254 {
 255   // Get the histogram of fuzzy values inside the target image
 256   typedef itk::LabelStatisticsImageFilter<FloatImageType, ImageType> FloatStatFilterType;
 257   typename FloatStatFilterType::Pointer f = FloatStatFilterType::New();
 258   f->SetInput(map);
 259   f->SetLabelInput(target);
 260   f->UseHistogramsOn();
 261   f->SetHistogramParameters(bins, 0.0-(1.0/bins), 1.0+(1.0/bins));
 262   f->Update();
 263   int count = f->GetCount(GetForegroundValue());
 264   // DD(count);
 265   typename FloatStatFilterType::HistogramPointer h = f->GetHistogram(GetForegroundValue());
 266
 267   // Debug : dump histogram
 268   static int i=0;
 269   std::ofstream histogramFile(std::string("fuzzy_histo_"+toString(i)+".txt").c_str());
 270   for(int j=0; j<bins; j++) {
 271     histogramFile << h->GetMeasurement(j,0)
 272                   << "\t" << h->GetFrequency(j)
 273                   << "\t" << (double)h->GetFrequency(j)/(double)count << std::endl;
 274   }
 275   histogramFile.close();
 276   std::ofstream histogramFile2(std::string("fuzzy_histo_R_"+toString(i)+".txt").c_str());
 277   for(int j=bins-1; j>=0; j--) {
 278     histogramFile2 << h->GetMeasurement(j,0)
 279                   << "\t" << h->GetFrequency(j)
 280                   << "\t" << (double)h->GetFrequency(j)/(double)count << std::endl;
 281   }
 282   histogramFile2.close();
 283   i++;
 284
 285   // Analyze the histogram (direct)
 286   double sum = 0.0;
 287   bool found = false;
 288   threshold = 0.0;
 289   for(int j=0; j<bins-1; j++) {
 290     sum += ((double)h->GetFrequency(j)/(double)count);
 291      // DD(j);
 292      // DD(sum);
 293      // DD(threshold);
 294      // DD(h->GetBinMin(0,j));
 295      // DD(h->GetBinMax(0,j));
 296     if ((!found) && (sum > tolerance)) {
 297       // We consider as threshold the laste before current, because
 298       if (j==0)
 299         threshold = h->GetBinMin(0,j);
 300       else threshold = h->GetBinMin(0,j-1); // FIXME  ? the last before reaching the threshold
 301       // DD(threshold);
 302       found = true;
 303       j = bins;
 304     }
 305   }
 306
 307   // Analyze the histogram (reverse)
 308   sum = 0.0;
 309   found = false;
 310   reverseThreshold = 1.0;
 311   for(int j=bins-1; j>0; j--) {
 312     sum += ((double)h->GetFrequency(j)/(double)count);
 313      // DD(j);
 314      // DD(sum);
 315      // DD(reverseThreshold);
 316      // DD(h->GetBinMin(0,j));
 317      // DD(h->GetBinMax(0,j));
 318     if ((!found) && (sum > tolerance)) {
 319       if (j==bins-1)
 320       reverseThreshold = h->GetBinMax(0,j);
 321       else reverseThreshold = h->GetBinMax(0,j-1);// FIXME  ? the last before reaching the threshold
 322       // DD(reverseThreshold);
 323       found = true;
 324       j = -1;
 325     }
 326   }
 327
 328 }
 329 //--------------------------------------------------------------------
 330