[Insight-users] KdTreeBasedKMeansClustering for 3D vector image

Sara Rolfe smrolfe at u.washington.edu
Mon Dec 6 19:00:09 EST 2010 

I have cleared up some problems in adapting the example Examples/ 
Statistics/KdTreeBasedKMeansClustering.cxx for a 3D vector image.   
However, I'm still running into a problem when I try to set the  
classifier membership function.  I'm getting the following error:

error: no matching function for call to  
‘itk 
::Statistics 
::SampleClassifier 
< 
main 
(int 
,char 
**)::AdaptorType 
 >::AddMembershipFunction(itk::Statistics::EuclideanDistance<main(int,  
char**)::MeasurementVectorType>*)’
/opt/local/include/InsightToolkit-3.16/Algorithms/ 
itkClassifierBase.txx:95: note: candidates are: unsigned int  
itk 
::ClassifierBase 
< 
TDataContainer 
 > 
::AddMembershipFunction 
(itk::Statistics::MembershipFunctionBase<typename  
TDataContainer::ValueType>*) [with TDataContainer = main(int,  
char**)::AdaptorType]
/opt/local/include/InsightToolkit-3.16/Numerics/Statistics/ 
itkEuclideanDistance.txx: In member function ‘double  
itk::Statistics::EuclideanDistance<TVector>::Evaluate(const TVector&)  
const [with TVector = main(int, char**)::MeasurementVectorType]’:
/Users/smrolfe/build/test.cxx:142:   instantiated from here
/opt/local/include/InsightToolkit-3.16/Numerics/Statistics/ 
itkEuclideanDistance.txx:65: error: no match for ‘operator-’ in  
‘(+#‘obj_type_ref’ not supported by dump_expr#<expression  
error>((&((const itk::Statistics::EuclideanDistance<main(int,  
char**)::MeasurementVectorType>*)this)- 
 >itk::Statistics::EuclideanDistance<main(int,  
char**)::MeasurementVectorType>::<anonymous>)))- 
 >itk::Array<double>::<anonymous>.vnl_vector<T>::operator[] [with T =  
double](i) - (+ x)->itk::Vector<main(int, char**)::PixelType,  
3u>::<anonymous>.itk::FixedArray<TValueType, VLength>::operator[]  
[with TValueType = main(int, char**)::PixelType, unsigned int VLength  
= 3u](i)’
/opt/local/include/InsightToolkit-3.16/Numerics/Statistics/ 
itkEuclideanDistance.txx: In member function ‘double  
itk::Statistics::EuclideanDistance<TVector>::Evaluate(const TVector&,  
const TVector&) const [with TVector = main(int,  
char**)::MeasurementVectorType]’:
/Users/smrolfe/build/test.cxx:142:   instantiated from here
/opt/local/include/InsightToolkit-3.16/Numerics/Statistics/ 
itkEuclideanDistance.txx:39: error: cannot convert  
‘itk::Vector<unsigned char, 2u>’ to ‘double’ in assignment

My code is attached.  Please let me know if you have any  
recommendations.



On Dec 2, 2010, at 3:55 PM, Sara Rolfe wrote:

> I did clear this error up, I needed to define the sample classifier  
> as:
>
> 	typedef itk::Statistics::SampleClassifier< AdaptorType >  
> ClassifierType;
>
> instead of:
> 		
> 	typedef itk::Statistics::SampleClassifier< SampleType >  
> ClassifierType;
>
> Since I'm using the ImageToListAdaptor to get the samples from my  
> image.
>
> On Dec 2, 2010, at 2:30 PM, Sara Rolfe wrote:
>
>> Thanks for the additional example.  I have not yet gotten it to  
>> run, since I think I'm dealing with some version issues (I'm using  
>> itk-3.14).  RIght now it's not recognizing itkMinimumDecisionRule2,  
>> itkSampleClassifierFilter, etc.  I will let you know when I'm able  
>> to get this working.  Is it possible that using older versions of  
>> these filters is part of my problem?
>>
>> In the meantime, with my code, the estimated mean vectors are  
>> calculated correctly, but when I add the classification I getting  
>> the following error:
>>
>> error: no matching function for call to  
>> ‘itk 
>> ::Statistics 
>> ::SampleClassifier 
>> < 
>> main 
>> (int 
>> ,char 
>> **)::SampleType 
>> > 
>> ::SetSample 
>> (itk::SmartPointer<itk::Statistics::ImageToListAdaptor<main(int,  
>> char**)::ImageType, main(int, char**)::PixelType> >&)’
>>
>> ...itkSampleClassifier.txx:56: note: candidates are: void  
>> itk::Statistics::SampleClassifier<TSample>::SetSample(const  
>> TSample*) [with TSample = main(int, char**)::SampleType]
>>
>>
>> So there's a problem when I set my classifier sample.  My full code  
>> is below.
>>
>> #include "itkKdTree.h"
>> #include "itkKdTreeBasedKmeansEstimator.h"
>> #include "itkWeightedCentroidKdTreeGenerator.h"
>>
>> #include "itkImageToListAdaptor.h"
>> #include "itkImageFileReader.h"
>> #include "itkImage.h"
>>
>> #include "itkMinimumDecisionRule.h"
>> #include "itkEuclideanDistance.h"
>> #include "itkSampleClassifier.h"
>>
>> #include "itkVector.h"
>> #include "itkListSample.h"
>> #include "itkDistanceToCentroidMembershipFunction.h"
>>
>>
>>
>> int main(  int argc,  char * argv[]  )
>> {
>> 	if( argc < 5 )
>>     {
>> 		std::cerr << "Usage: " << std::endl;
>> 		std::cerr << argv[0];
>> 		std::cerr << " inputVectorImage.vtk outputLabeledImage.vtk";
>> 		std::cerr << " numberOfClasses numberOfComponents " << std::endl;
>> 		return EXIT_FAILURE;
>>     }
>> 	
>> 	typedef itk::Vector< unsigned char, 2 >	PixelType;
>> 	typedef itk::Image< PixelType, 3 > ImageType;
>> 	typedef itk::ImageFileReader< ImageType > ReaderType;
>> 	typedef itk::Statistics::ImageToListAdaptor< ImageType >  
>> AdaptorType;
>> 	typedef  
>> itk::Statistics::WeightedCentroidKdTreeGenerator<AdaptorType >  
>> TreeGeneratorType;
>> 	typedef TreeGeneratorType::KdTreeType TreeType;
>> 	typedef itk:: Statistics:: KdTreeBasedKmeansEstimator< TreeType >  
>> EstimatorType;
>> 	typedef itk::Vector< PixelType, 3 > MeasurementVectorType;
>> 	typedef itk::Statistics::EuclideanDistance< MeasurementVectorType  
>> > MembershipFunctionType;
>> 	typedef itk::MinimumDecisionRule DecisionRuleType;
>> 	typedef itk::Statistics::ListSample< MeasurementVectorType >  
>> SampleType;
>> 	typedef itk::Statistics::SampleClassifier< SampleType >  
>> ClassifierType;
>> 	
>> 	const char * inputImageFileName = argv[1];
>> 	const char * outputImageFileName = argv[2];
>> 	int numberOfClasses = atoi( argv[3] );
>> 	int numberOfComponents = atoi( argv[4] );
>> 	
>> 	ReaderType::Pointer reader = ReaderType::New();
>> 	reader->SetFileName( inputImageFileName );
>> 	reader->Update();
>> 	
>> 	AdaptorType::Pointer adaptor = AdaptorType::New();
>> 	adaptor->SetImage(  reader->GetOutput() );
>> 	
>> 	TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New();
>> 	treeGenerator->SetSample( adaptor );
>> 	treeGenerator->SetBucketSize( 16 );
>> 	treeGenerator->Update();
>>
>> 	EstimatorType::Pointer estimator = EstimatorType::New();
>> 	EstimatorType::ParametersType initialMeans( numberOfClasses *  
>> numberOfComponents );
>> 	estimator->SetParameters( initialMeans );
>> 	estimator->SetKdTree( treeGenerator->GetOutput() );
>> 	estimator->SetMaximumIteration( 200 );
>> 	estimator->SetCentroidPositionChangesThreshold(0.0);
>> 	estimator->StartOptimization();
>> 	
>> 	EstimatorType::ParametersType estimatedMeans = estimator- 
>> >GetParameters();
>>
>> 	for ( int i = 0 ; i < numberOfClasses ; ++i )
>>     {
>> 		std::cout << "cluster[" << i << "] ";
>> 		std::cout << "  estimated mean : ";
>> 		for ( int j = 0 ; j < numberOfComponents ; ++j )
>> 		{
>> 			std::cout << "    " << estimatedMeans[ i * numberOfComponents +  
>> j ];
>> 		}
>> 		std::cout << std::endl;
>>     }
>>
>> 	//classification using estimated means - this is the part that is  
>> not working correctly
>> 	DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
>> 	
>> 	ClassifierType::Pointer classifier = ClassifierType::New();
>> 	classifier->SetDecisionRule( (itk::DecisionRuleBase::Pointer)  
>> decisionRule);
>> 	classifier->SetSample( adaptor );
>> 	classifier->SetNumberOfClasses( 7 );
>> 	
>> 	std::vector< unsigned int > classLabels;
>> 	classLabels.resize( numberOfClasses );
>> 	for ( int i = 0 ; i < numberOfClasses ; ++i )
>>     {
>> 		classLabels[i] = i;
>> 	}
>> 	classifier->SetMembershipFunctionClassLabels( classLabels );
>> 	
>> }
>>
>> On Dec 1, 2010, at 6:32 PM, Luis Ibanez wrote:
>>
>>> Voila !
>>>
>>> http://www.itk.org/Wiki/ITK/Examples/Statistics/KdTreeBasedKMeansClustering_3D
>>>
>>> Thanks for pointing to these examples David.
>>>
>>>
>>> Sara,
>>>
>>> when running the example above,
>>> the code seems to behave correctly.
>>>
>>> Please give it a try and let us know
>>> if you see anything out of order.
>>>
>>>
>>>    Thanks
>>>
>>>
>>>      Luis
>>>
>>>
>>> -----------------------------------------
>>> On Wed, Dec 1, 2010 at 8:31 PM, David Doria <daviddoria at gmail.com>  
>>> wrote:
>>>> On Wed, Dec 1, 2010 at 8:16 PM, Luis Ibanez <luis.ibanez at kitware.com 
>>>> > wrote:
>>>>> Hi Sara,
>>>>>
>>>>> The KdTree should work in N-D.
>>>>>
>>>>> We tend to do 2D test just because they are
>>>>> easier to debug, but we probably should add
>>>>> a 3D one in this case.
>>>>>
>>>>> Could you tell us more about the behavior of
>>>>> this class that lead you to believe that is doing
>>>>> something incorrect ?
>>>>>
>>>>> A minimal example will be greatly appreciated...
>>>>
>>>>
>>>> Are these the examples you were looking at?
>>>>
>>>> http://www.vtk.org/Wiki/ITK/Examples/Statistics/KdTreeBasedKMeansClustering_1D
>>>> http://www.vtk.org/Wiki/ITK/Examples/Statistics/KdTreeBasedKMeansClustering_2D
>>>>
>>>> If not, maybe they will help. If so, please add
>>>> http://www.vtk.org/Wiki/ITK/Examples/Statistics/KdTreeBasedKMeansClustering_3D
>>>>
>>>> and we can work on it there.
>>>>
>>>> David
>>>>
>>
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