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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkSimpleFuzzyConnectednessScalarImageFilter.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 __itkSimpleFuzzyConnectednessScalarImageFilter_h
#define __itkSimpleFuzzyConnectednessScalarImageFilter_h
#include "itkImage.h"
#include "itkImageToImageFilter.h"
#include "itkSimpleFuzzyConnectednessImageFilterBase.h"
#include <queue>
namespace itk {
/** \class SimpleFuzzyConnectednessScalarImageFilter
* \brief Perform segmentation on grayscale images using method of fuzzy connectedness.
*
* Perform the segmentation for a single channel (Grayscale) image
* via thresholding of a fuzzy connectedness scene.
* Used as a node of the segmentation toolkit.
* Fuzzy affinity is defined between two neighboor pixels, to reflect
* their similarity and assign a probability that these two pixels belong to the
* same object. A "path" between two pixels is a list of pixels that connect
* them, the strength of a particular path is defined as the weakest affinity
* between the neighboor pixels that form the path. The fuzzy connectedness
* between two pixels is defined as the strongest path strength between these
* two pixels. The segmentation based on fuzzy connectedness assumes that
* the fuzzy connectedness between any two pixels from a single object
* is significantly higher than those for pixels belonging to different objects.
* A fuzzy connectedness scene is first computed for a set of input seed
* points selected inside the object of interest. A threshold is then
* applied to the fuzzy scene to extract the binary segmented object.
* The fuzzy affinity here was defined as a gaussian function of the pixel difference
* and the difference of the estimated object mean and the mean of the two input
* pixels.
*
* Input Parameters are:
* (1) Input image in the form itkImage
* (2) Seed points
* (3) Threshold value.
*
* Usage:
* 1. use SetInput to import the input image object
* 2. use SetParameter, SetSeed, SetThreshold to set the parameters
* 3. run GenerateData() to perform the segmenation
* 4. threshold can be set using UpdateThreshold after the segmentation, and no computation
* will be redo. no need to run GenerateData. But if SetThreshold was used. MakeSegmentObject()
* should be called to get the updated result.
* 5. use GetOutput to obtain the resulted binary image Object.
* 6. GetFuzzyScene gives the pointer of Image<unsigned short> for the fuzzy scene.
*
* Detailed information about this algorithm can be found in:
* "Fuzzy Connectedness and Object Definition: Theory, Algorithms,
* and Applications in Image Segmentation", J. Udupa and S. Samarasekera
* Graphical Models and Image Processing, Vol.58, No.3. pp 246-261, 1996.
*
*
* \ingroup FuzzyConnectednessSegmentation */
template <class TInputImage, class TOutputImage>
class ITK_EXPORT SimpleFuzzyConnectednessScalarImageFilter:
public SimpleFuzzyConnectednessImageFilterBase<TInputImage,TOutputImage>
{
public:
/** Standard class typedefs. */
typedef SimpleFuzzyConnectednessScalarImageFilter Self;
typedef SimpleFuzzyConnectednessImageFilterBase<TInputImage,TOutputImage>
Superclass;
typedef SmartPointer <Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(SimpleFuzzyConnectednessScalarImageFilter,
SimpleFuzzyConnectednessImageFilterBase);
/** Region, size, and pixel types. */
typedef typename Superclass::IndexType IndexType;
typedef typename Superclass::SizeType SizeType;
typedef typename Superclass::PixelType PixelType;
/** Set the Estimation of the mean difference between neighbor pixels for
* the object. */
itkSetMacro(Diff_Mean, double);
/** Get the Estimation of the mean difference between neighbor pixels for
* the object. */
itkGetMacro(Diff_Mean, double);
/** Set the Estimation of the variance of the difference between pixels for
* the object. */
itkSetMacro(Diff_Variance, double);
/** Get the Estimation of the variance of the difference between pixels for
* the object. */
itkGetMacro(Diff_Variance, double);
/** Set the Estimation of the mean difference between neighbor pixels for
* the object. */
itkSetMacro(Mean, double);
/** Get the Estimation of the mean difference between neighbor pixels for
* the object. */
itkGetMacro(Mean, double);
/** Set the Estimation of the variance of the difference between pixels for
* the object. */
itkSetMacro(Variance, double);
/** Get the Estimation of the variance of the difference between pixels for
* the object. */
itkGetMacro(Variance, double);
/** Setting the parameters for segmentation. */
void SetParameters(const double inmean,const double invar,
const double indifmean,const double indifvar,const double inweight);
protected:
SimpleFuzzyConnectednessScalarImageFilter();
~SimpleFuzzyConnectednessScalarImageFilter();
virtual void PrintSelf(std::ostream& os, Indent indent) const;
double m_Mean;
double m_Variance; //estimation of the Variance.
double m_Diff_Mean;
double m_Diff_Variance;
virtual double FuzzyAffinity(const PixelType f1, const PixelType f2);
private:
SimpleFuzzyConnectednessScalarImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkSimpleFuzzyConnectednessScalarImageFilter.txx"
#endif
#endif
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