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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkSimpleFuzzyConnectednessRGBImageFilter.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 __itkSimpleFuzzyConnectednessRGBImageFilter_h
#define __itkSimpleFuzzyConnectednessRGBImageFilter_h
#include "itkImage.h"
#include "itkImageToImageFilter.h"
#include "itkSimpleFuzzyConnectednessImageFilterBase.h"
#include <vnl/vnl_matrix_fixed.h>
#include <queue>
namespace itk {
/** \class SimpleFuzzyConnectednessRGBImageFilter
* \brief Perform segmentation on RGB images using method of fuzzy connectedness.
*
* Perform the segmentation for three channels (RGB) 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 (in a vectorial fashion).
*
* Usage:
*
* 1. use SetInput to import the input image object
* 2. use SetParameter, SetSeed, SetThreshold to set the parameters
* 3. run ExcuteSegment to perform the segmenation
* 4. threshold can be set 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.
*
* Input Parameters are:
* (1) Input image in the form itkImage<itkVector<Pixeltype,3>,Dimension>
* (2) Seed points
* (3) Threshold value.
*
* The fuzzy scene can also be extracted with the GetFuzzyScene method.
*
* 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 SimpleFuzzyConnectednessRGBImageFilter:
public SimpleFuzzyConnectednessImageFilterBase<TInputImage,TOutputImage>
{
public:
/** Standard class typedefs. */
typedef SimpleFuzzyConnectednessRGBImageFilter 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(SimpleFuzzyConnectednessRGBImageFilter,
SimpleFuzzyConnectednessImageFilterBase);
/** The pixel type. */
typedef typename TInputImage::PixelType PixelType;
/** Setting and getting the segmentation parameters. */
itkSetVectorMacro(Mean,double,3);
void GetMean(double omean[3])
{
omean[0]=m_Mean[0];omean[1]=m_Mean[1];omean[2]=m_Mean[2];
}
void SetVariance(double ivar[3][3])
{
m_Variance[0][0]=ivar[0][0];m_Variance[0][1]=ivar[0][1];m_Variance[0][2]=ivar[0][2];
m_Variance[1][0]=ivar[1][0];m_Variance[1][1]=ivar[1][1];m_Variance[1][2]=ivar[1][2];
m_Variance[2][0]=ivar[2][0];m_Variance[2][1]=ivar[2][2];m_Variance[2][2]=ivar[2][2];
}
void GetVariance(double ovar[3][3])
{
ovar[0][0]=m_Variance[0][0];ovar[0][1]=m_Variance[0][1];ovar[0][2]=m_Variance[0][2];
ovar[1][0]=m_Variance[1][0];ovar[1][1]=m_Variance[1][1];ovar[1][2]=m_Variance[1][2];
ovar[2][0]=m_Variance[2][0];ovar[2][1]=m_Variance[2][1];ovar[2][2]=m_Variance[2][2];
}
itkSetVectorMacro(Diff_Mean,double,3);
void GetDiff_Mean(double odmean[3])
{
odmean[0]=m_Diff_Mean[0];odmean[1]=m_Diff_Mean[1];odmean[2]=m_Diff_Mean[2];
}
void SetDiff_Variance(double idvar[3][3])
{
m_Diff_Variance[0][0]=idvar[0][0];m_Diff_Variance[0][1]=idvar[0][1];m_Diff_Variance[0][2]=idvar[0][2];
m_Diff_Variance[1][0]=idvar[1][0];m_Diff_Variance[1][1]=idvar[1][1];m_Diff_Variance[1][2]=idvar[1][2];
m_Diff_Variance[2][0]=idvar[2][0];m_Diff_Variance[2][1]=idvar[2][1];m_Diff_Variance[2][2]=idvar[2][2];
}
void GetDiff_Variance(double odvar[3][3])
{
odvar[0][0]=m_Diff_Variance[0][0];odvar[0][1]=m_Diff_Variance[0][1];odvar[0][2]=m_Diff_Variance[0][2];
odvar[1][0]=m_Diff_Variance[1][0];odvar[1][1]=m_Diff_Variance[1][1];odvar[1][2]=m_Diff_Variance[1][2];
odvar[2][0]=m_Diff_Variance[2][0];odvar[2][1]=m_Diff_Variance[2][1];odvar[2][2]=m_Diff_Variance[2][2];
}
protected:
SimpleFuzzyConnectednessRGBImageFilter();
~SimpleFuzzyConnectednessRGBImageFilter();
virtual void PrintSelf(std::ostream& os, Indent indent) const;
void GenerateData(void);
private:
SimpleFuzzyConnectednessRGBImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
double m_Mean[3];
double m_Variance[3][3];
double m_Diff_Mean[3];
double m_Diff_Variance[3][3];
double m_VarianceInverse[3][3];
double m_Diff_VarianceInverse[3][3];
double m_VarianceDet;
double m_Diff_VarianceDet;
virtual double FuzzyAffinity(const PixelType f1, const PixelType f2);
};
} // end namespace itk.
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkSimpleFuzzyConnectednessRGBImageFilter.txx"
#endif
#endif
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