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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkStochasticFractalDimensionImageFilter.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 __itkStochasticFractalDimensionImageFilter_h
#define __itkStochasticFractalDimensionImageFilter_h
#include "itkImageToImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
namespace itk {
/** \class StochasticFractalDimensionImageFilter
* \brief This filter computes the stochastic fractal dimension of the input image.
*
* The methodology is based on Madelbrot's fractal theory and the concept of
* fractional Brownian motion and yields images which have been used for
* classification and edge enhancement.
*
* This class which is templated over the input and output images as well as a
* mask image type. The input is a scalar image, an optional neighborhood
* radius (default = 2), and an optional mask. The mask can be specified to
* decrease computation time since, as the authors point out, calculation is
* time-consuming.
*
* This filter was contributed by Nick Tustison and James Gee
* from the PICSL lab, at the University of Pennsylvania
* as an paper to the Insight Journal:
*
* "Stochastic Fractal Dimension Image"
* http://hdl.handle.net/1926/1525
* http://www.insight-journal.org/browse/publication/318
*
* \author Nick Tustison
*
*/
template<class TInputImage, class TMaskImage = Image<unsigned char,
::itk::GetImageDimension<TInputImage>::ImageDimension>,
class TOutputImage = TInputImage>
class ITK_EXPORT StochasticFractalDimensionImageFilter :
public ImageToImageFilter<TInputImage, TOutputImage>
{
public:
/** Standard class typedefs. */
typedef StochasticFractalDimensionImageFilter Self;
typedef ImageToImageFilter<TInputImage, TOutputImage> Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Standard New method. */
itkNewMacro( Self );
/** ImageDimension constants */
itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension );
/** Some convenient typedefs. */
typedef float RealType;
typedef TInputImage InputImageType;
typedef TMaskImage MaskImageType;
typedef TOutputImage OutputImageType;
/** Runtime information support. */
itkTypeMacro( StochasticFractalDimensionImageFilter, ImageToImageFilter );
/** Set/Get the input mask image that will constraint the computation of the
* fractal dimension to pixels that are on in the mask. This is intended to
* reduce the computation time. */
void SetMaskImage( const MaskImageType * mask );
const MaskImageType * GetMaskImage() const;
/** Type of the neighborhood iterator used to evaluate similarity between the
* image pixels. */
typedef ConstNeighborhoodIterator<InputImageType> ConstNeighborhoodIteratorType;
typedef typename ConstNeighborhoodIteratorType::RadiusType RadiusType;
/** Manhattan radius used for evaluating the fractal dimension. */
itkSetMacro( NeighborhoodRadius, RadiusType );
itkGetConstMacro( NeighborhoodRadius, RadiusType );
protected:
StochasticFractalDimensionImageFilter();
~StochasticFractalDimensionImageFilter();
void PrintSelf( std::ostream& os, Indent indent ) const;
void GenerateData();
private:
StochasticFractalDimensionImageFilter( const Self& ); //purposely not implemented
void operator=( const Self& ); //purposely not implemented
RadiusType m_NeighborhoodRadius;
typename MaskImageType::Pointer m_MaskImage;
}; // end of class
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkStochasticFractalDimensionImageFilter.txx"
#endif
#endif
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