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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkHessianToObjectnessMeasureImageFilter.h
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkHessianToObjectnessMeasureImageFilter_h
#define __itkHessianToObjectnessMeasureImageFilter_h
#include "itkSymmetricSecondRankTensor.h"
#include "itkImageToImageFilter.h"
namespace itk
{
/** \class HessianToObjectnessMeasureImageFilter
* \brief A filter to enhance M-dimensional objects in N-dimensional images
*
* The objectness measure is a generalization of Frangi's vesselness measure,
* which is based on the analysis of the the Hessian eigen system. The filter
* can enhance blob-like structures (M=0), vessel-like structures (M=1), 2D
* plate-like structures (M=2), hyper-plate-like structures (M=3) in N-dimensional
* images, with M<N.
* The filter takes an image of a Hessian pixels ( SymmetricSecondRankTensor pixels
* pixels ) and produces an enhanced image. The Hessian input image can be produced
* using itk::HessianRecursiveGaussianImageFilter.
*
*
* \par References
* Frangi, AF, Niessen, WJ, Vincken, KL, & Viergever, MA (1998). Multiscale Vessel
* Enhancement Filtering. In Wells, WM, Colchester, A, & Delp, S, Editors, MICCAI '98
* Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer
* Science, pages 130-137, Springer Verlag, 1998.
*
* Additional information can be from in the Insight Journal:
* http://hdl.handle.net/1926/576
*
* \author Luca Antiga Ph.D. Medical Imaging Unit,
* Bioengineering Deparment, Mario Negri Institute, Italy.
*
* \sa MultiScaleHessianBasedMeasureImageFilter
* \sa Hessian3DToVesselnessMeasureImageFilter
* \sa HessianSmoothedRecursiveGaussianImageFilter
* \sa SymmetricEigenAnalysisImageFilter
* \sa SymmetricSecondRankTensor
*
* \ingroup IntensityImageFilters TensorObjects
*
*/
template < typename TInputImage, typename TOutputImage >
class ITK_EXPORT HessianToObjectnessMeasureImageFilter : public
ImageToImageFilter< TInputImage,TOutputImage>
{
public:
/** Standard class typedefs. */
typedef HessianToObjectnessMeasureImageFilter Self;
typedef ImageToImageFilter< TInputImage, TOutputImage > Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
typedef typename Superclass::InputImageType InputImageType;
typedef typename Superclass::OutputImageType OutputImageType;
typedef typename InputImageType::PixelType InputPixelType;
typedef typename OutputImageType::PixelType OutputPixelType;
typedef typename OutputImageType::RegionType OutputImageRegionType;
/** Image dimension */
itkStaticConstMacro(ImageDimension, unsigned int, ::itk::GetImageDimension<InputImageType>::ImageDimension);
typedef double EigenValueType;
typedef itk::FixedArray< EigenValueType, itkGetStaticConstMacro( ImageDimension ) > EigenValueArrayType;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Runtime information support. */
itkTypeMacro( HessianToObjectnessMeasureImageFilter, ImageToImageFilter );
/** Set/Get Alpha, the weight corresponding to R_A
* (the ratio of the smallest eigenvalue that has to be large to the larger ones).
* Smaller values lead to increased sensitivity to the object dimensionality. */
itkSetMacro(Alpha,double);
itkGetConstMacro(Alpha,double);
/** Set/Get Beta, the weight corresponding to R_B
* (the ratio of the largest eigenvalue that has to be small to the larger ones).
* Smaller values lead to increased sensitivity to the object dimensionality. */
itkSetMacro(Beta,double);
itkGetConstMacro(Beta,double);
/** Set/Get Gamma, the weight corresponding to S
* (the Frobenius norm of the Hessian matrix, or second-order structureness) */
itkSetMacro(Gamma,double);
itkGetConstMacro(Gamma,double);
/** Toggle scaling the objectness measure with the magnitude of the largest absolute eigenvalue */
itkSetMacro(ScaleObjectnessMeasure,bool);
itkGetConstMacro(ScaleObjectnessMeasure,bool);
itkBooleanMacro(ScaleObjectnessMeasure);
/** Set/Get the dimensionality of the object (0: points (blobs),
* 1: lines (vessels), 2: planes (plate-like structures), 3: hyper-planes.
* ObjectDimension must be smaller than ImageDimension. */
itkSetMacro(ObjectDimension,unsigned int);
itkGetConstMacro(ObjectDimension,unsigned int);
/** Enhance bright structures on a dark background if true, the opposite if false. */
itkSetMacro(BrightObject,bool);
itkGetConstMacro(BrightObject,bool);
itkBooleanMacro(BrightObject);
#ifdef ITK_USE_CONCEPT_CHECKING
/** Begin concept checking */
itkConceptMacro(DoubleConvertibleToOutputCheck,(Concept::Convertible<double, OutputPixelType>));
/** End concept checking */
#endif
protected:
HessianToObjectnessMeasureImageFilter();
~HessianToObjectnessMeasureImageFilter() {};
void PrintSelf(std::ostream& os, Indent indent) const;
void BeforeThreadedGenerateData( void );
void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId);
private:
HessianToObjectnessMeasureImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
// functor used to sort the eigenvalues are to be sorted
// |e1|<=|e2|<=...<=|eN|
struct AbsLessEqualCompare
{
bool operator()( EigenValueType a, EigenValueType b )
{
return vnl_math_abs(a) <= vnl_math_abs(b);
}
};
double m_Alpha;
double m_Beta;
double m_Gamma;
unsigned int m_ObjectDimension;
bool m_BrightObject;
bool m_ScaleObjectnessMeasure;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkHessianToObjectnessMeasureImageFilter.txx"
#endif
#endif
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