void Filter()
{
XSum += X_Axis - XBuffer[index]; // Subtract out the oldest value in samples from sums
YSum += Y_Axis - YBuffer[index]; // and add in the new value to sum
ZSum += Z_Axis - ZBuffer[index];
XBuffer[index] = X_Axis; // Set oldest value to the new current reading
YBuffer[index] = Y_Axis;
ZBuffer[index] = Z_Axis;
index++; // Increment the positition of the oldest value in samples
if (index == NUM_SAMPLES) // Reset the index to 0 if it equals NUM_SAMPLES
index = 0;
ComputeAverage(); // Update averages
}
/* use this function to compute one associative measurement */
float NuclearAssociationRules::ComputeOneAssocMeasurement(itk::SmartPointer<TargImageType> trgIm, int ruleID, int objID)
{
//fisrt, get the bounding box around the object
//The bounding box is defined by the area around the object such that the object+outerdistance are included
int imBounds[6] = {0,x_Size,0,y_Size,0,z_Size};
LabelGeometryType::BoundingBoxType bbox = labGeometryFilter->GetBoundingBox(objID);
//make sure the object exists
int valid = 0;
for(int dim=0; dim < imDim*2; ++dim)
{
if(bbox[dim] > 0)
valid = 1;
}
if(valid == 0)
return -1;
std::vector< int > retBbox(0);
int dist, val;
dist = assocRulesList[ruleID].GetOutDistance();
for(int dim=0; dim < imDim*2; ++dim)
{
if(dim%2 == 0) //even
{
val = int(bbox[dim])-dist-1;
if(val<imBounds[dim])
val=imBounds[dim];
}
else //odd
{
val = int(bbox[dim])+dist+1;
if(val>=imBounds[dim])
val=imBounds[dim]-1;
}
retBbox.push_back( val );
}
//the bounding box defines the region of interest we need (from both segmentation and target images)
//so, use it to get sub images
DistImageType::Pointer subSegImg = DistImageType::New();
LabImageType::IndexType start;
start[0] = 0; // first index on X
start[1] = 0; // first index on Y
start[2] = 0; // first index on Z
LabImageType::SizeType size;
size[0] = retBbox[1]-retBbox[0]+1; // size along X
size[1] = retBbox[3]-retBbox[2]+1; // size along Y
if(imDim == 3)
size[2] = retBbox[5]-retBbox[4]+1; // size along Z
else
size[2] = 1;
LabImageType::RegionType region;
region.SetSize( size );
region.SetIndex( start );
subSegImg->SetRegions( region );
subSegImg->Allocate();
subSegImg->FillBuffer(0);
subSegImg->Update();
LabImageType::IndexType start2;
start2[0] = retBbox[0]; // first index on X
start2[1] = retBbox[2]; // first index on Y
if(imDim == 3)
start2[2] = retBbox[4]; // first index on Z
else
start2[2] = 0;
LabImageType::RegionType region2;
region2.SetSize( size );
region2.SetIndex( start2 );
labImage->SetRequestedRegion(region2);
trgIm->SetRequestedRegion(region2);
typedef itk::ImageRegionIteratorWithIndex< LabImageType > IteratorType;
IteratorType iterator1(labImage, labImage->GetRequestedRegion());
typedef itk::ImageRegionIteratorWithIndex< DistImageType > IteratorType2;
IteratorType2 iterator2(subSegImg, subSegImg->GetRequestedRegion());
//in the sub-segmentation image, we need to mask out any pixel from another object
int counter = 0;
while ( ! iterator1.IsAtEnd())
{
int V = iterator1.Get();
if(V == objID)
{
iterator2.Set(255.0);
counter++; //just for debuging purposes,, will be removed
}
else
iterator2.Set(0.0);
++iterator1;
++iterator2;
}
//Let's try this (for debugging): save the binary mask
/* typedef itk::Image< unsigned short, 3 > OutputImageType;
typedef itk::CastImageFilter< DistImageType, OutputImageType > CastType;
CastType::Pointer cast = CastType::New();
cast->SetInput( subSegImg );
cast->Update();
typedef itk::ImageFileWriter< OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New( );
writer->SetInput( cast->GetOutput() );
writer->SetFileName( "c:/bin_mask.tif" );
writer->Update(); */
//Compute the distance transform in the sub-segmentation image region
DTFilter::Pointer dt_obj= DTFilter::New() ;
dt_obj->SetInput(subSegImg) ;
//dt_obj->SetInsideValue(255.0);
//dt_obj->SetOutsideValue(0.0);
try{
dt_obj->Update() ;
}
catch( itk::ExceptionObject & err ){
std::cout << "Error in Distance Transform: " << err << std::endl;
return 0;
}
//Let's try this (for debugging): save the distance map
////typedef itk::Image< unsigned short, 3 > OutputImageType;
////typedef itk::CastImageFilter< DistImageType, OutputImageType > CastType;
////CastType::Pointer cast = CastType::New();
//cast->SetInput( dt_obj->GetOutput() );
//cast->Update();
////typedef itk::ImageFileWriter< OutputImageType > WriterType;
////WriterType::Pointer writer = WriterType::New( );
//writer->SetInput( cast->GetOutput() );
//writer->SetFileName( "c:/dist_map.tif" );
//writer->Update();
//now, mask out all the pixels (in the sub-seg image) that are not in the region of interest as defined by the association rule and get the intensities of the needed pixels from the target image. The intensities are saved into an std vector
IteratorType2 iterator3(dt_obj->GetOutput(), dt_obj->GetOutput()->GetRequestedRegion());
IteratorType iterator4(trgIm, trgIm->GetRequestedRegion());
std::vector<int> trgInt;
int counter_in = 0;
int counter_at = 0;
int counter_ot = 0;
while ( ! iterator3.IsAtEnd())
{
int V = (int)iterator3.Get();
if(V<0)
counter_in++;
if(V==0)
counter_at++;
if(V>0)
counter_ot++;
//if it is outside with distance less than outDistance away
if(V>0 && V<=assocRulesList[ruleID].GetOutDistance())
trgInt.push_back(iterator4.Get());
//if it is inside and the whole cell is used
else if(V<=0 && assocRulesList[ruleID].IsUseWholeObject())
trgInt.push_back(iterator4.Get());
//if it is inside with distance less than in Distance
else if(V<=0 && abs(V)<=assocRulesList[ruleID].GetInDistance())
trgInt.push_back(iterator4.Get());
++iterator3;
++iterator4;
}
if(!trgInt.size())
return 0;
//Finally, given the list of intensities compute the associative measrement based on the type defined by the association rule
switch(assocRulesList[ruleID].GetAssocType())
{
case ASSOC_MIN:
return FindMin(trgInt);
break;
case ASSOC_MAX:
return FindMax(trgInt);
break;
case ASSOC_TOTAL:
return ComputeTotal(trgInt);
break;
case ASSOC_AVERAGE:
return ComputeAverage(trgInt);
break;
default:
return ComputeAverage(trgInt);
}
//we will never go here, just silencing a compiler warning
return 0.0;
}
