1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
|
/*****************************************************************************
* $CAMITK_LICENCE_BEGIN$
*
* CamiTK - Computer Assisted Medical Intervention ToolKit
* (c) 2001-2018 Univ. Grenoble Alpes, CNRS, TIMC-IMAG UMR 5525 (GMCAO)
*
* Visit http://camitk.imag.fr for more information
*
* This file is part of CamiTK.
*
* CamiTK is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License version 3
* only, as published by the Free Software Foundation.
*
* CamiTK is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License version 3 for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* version 3 along with CamiTK. If not, see <http://www.gnu.org/licenses/>.
*
* $CAMITK_LICENCE_END$
****************************************************************************/
#include "GradientMagnitudeRecursiveGaussian.h"
// CamiTK includes
#include <Application.h>
#include <ItkProgressObserver.h>
#include <Property.h>
// ITK includes
#include <itkImageToVTKImageFilter.h>
#include <itkVTKImageToImageFilter.h>
#include <itkGradientMagnitudeRecursiveGaussianImageFilter.h>
using namespace camitk;
// --------------- constructor -------------------
GradientMagnitudeRecursiveGaussian::GradientMagnitudeRecursiveGaussian(ActionExtension* extension) : Action(extension) {
// Setting name, description and input component
setName("Gradient Magnitude With Smoothing");
setDescription("<br>Differentiation is an ill-defined operation over digital data.<br> \
In practice it is convenient to define a scale in which the differentiation should be performed. This is usually done by preprocessing the data with a smoothing filter. <br/><br/> \
It has been shown that a Gaussian kernel is the most choice for performing such smoothing. By choosing a particular value for the standard deviation <i>(sigma)</i> of the Gaussian, an associated scale is selected that ignores high frequency content, commonly considered image noise. <br/><br/> \
This filter computes the magnitude of the image gradient at each pixel location.<br/><br/> \
<b>The computational process is equivalent to first smoothing the image by convolving it with a Gaussian kernel and then applying a differential operator.</b> <br/><br/> \
The user selects the value of <i>sigma</i>. Internally this is done by applying an IIR filter that approximates a convolution with the derivative of the Gaussian kernel. Traditional convolution will produce a more accurate result, but the IIR approach is much faster, especially using large <i>sigma</i>s (Deriche1990,Deriche1993).<br/><br/> \
<i>(source: ITK Developer's Guide)</i><br>");
setComponent("ImageComponent");
// Setting classification family and tags
this->setFamily("ITK Filter");
this->addTag("Gradient");
this->addTag("Derivative");
this->addTag("Edge Detection");
this->addTag("Contours");
this->addTag("Smoothing");
// Setting parameters default values
Property* standardDeviationProperty = new Property(tr("Standard deviation"), 3.0, tr("The standard deviation <i>(sigma)</i> is used as a parameter of the Gaussian convolution kernel. \nThe higher the deviation is, the blurer the resulting image will be."), "");
standardDeviationProperty->setAttribute("minimum", 0);
standardDeviationProperty->setAttribute("maximum", 100);
standardDeviationProperty->setAttribute("singleStep", 0.1);
addParameter(standardDeviationProperty);
}
// --------------- destructor -------------------
GradientMagnitudeRecursiveGaussian::~GradientMagnitudeRecursiveGaussian() {
// do not delete the widget has it might have been used in the ActionViewer (i.e. the ownership might have been taken by the stacked widget)
}
// --------------- apply -------------------
Action::ApplyStatus GradientMagnitudeRecursiveGaussian::apply() {
foreach (Component* comp, getTargets()) {
ImageComponent* input = dynamic_cast<ImageComponent*>(comp);
process(input);
}
return SUCCESS;
}
void GradientMagnitudeRecursiveGaussian::process(ImageComponent* comp) {
// Get the parameters
sigma = property("Standard deviation").toDouble();
// ITK filter implementation using templates
vtkSmartPointer<vtkImageData> inputImage = comp->getImageData();
vtkSmartPointer<vtkImageData> outputImage = implementProcess(inputImage);
ImageComponent* outputComp = new ImageComponent(outputImage, comp->getName() + "_smoothedGradient");
// consider frame policy on new image created
Action::applyTargetPosition(comp, outputComp);
Application::refresh();
}
#include "GradientMagnitudeRecursiveGaussian.impl"
// ITK filter implementation
template <class InputPixelType, class OutputPixelType, const int dim>
vtkSmartPointer<vtkImageData> GradientMagnitudeRecursiveGaussian::itkProcess(vtkSmartPointer<vtkImageData> img) {
vtkSmartPointer<vtkImageData> result = vtkSmartPointer<vtkImageData>::New();
// --------------------- Filters declaration and creation ----------------------
// Define ITK input and output image types with respect to the instantiation
// types of the tamplate.
typedef itk::Image< InputPixelType, dim > InputImageType;
typedef itk::Image< OutputPixelType, dim > OutputImageType;
// Convert the image from CamiTK in VTK format to ITK format to use ITK filters.
typedef itk::VTKImageToImageFilter<InputImageType> vtkToItkFilterType;
typename vtkToItkFilterType::Pointer vtkToItkFilter = vtkToItkFilterType::New();
// Declare and create your own private ITK filter here...
typedef itk::GradientMagnitudeRecursiveGaussianImageFilter <InputImageType, OutputImageType> GradientMagnitudeImageFilterType;
typename GradientMagnitudeImageFilterType::Pointer gradientFilter = GradientMagnitudeImageFilterType::New();
// In the same way, once the image is filtered, we need to convert it again to
// VTK format to give it to CamiTK.
typedef itk::ImageToVTKImageFilter<OutputImageType> itkToVtkFilterType;
typename itkToVtkFilterType::Pointer itkToVtkFilter = itkToVtkFilterType::New();
// ------------------------- WRITE YOUR CODE HERE ----------------------------------
// To update CamiTK progress bar while filtering, add an ITK observer to the filters.
ItkProgressObserver::Pointer observer = ItkProgressObserver::New();
// ITK observers generally give values between 0 and 1, and CamiTK progress bar
// wants values between 0 and 100...
observer->SetCoef(100.0);
// --------------------- Plug filters and parameters ---------------------------
// From VTK to ITK
vtkToItkFilter->SetInput(img);
vtkToItkFilter->AddObserver(itk::ProgressEvent(), observer);
vtkToItkFilter->Update();
observer->Reset();
gradientFilter->SetInput(vtkToItkFilter->GetOutput());
gradientFilter->SetSigma(sigma);
gradientFilter->AddObserver(itk::ProgressEvent(), observer);
gradientFilter->Update();
observer->Reset();
// From ITK to VTK
// Change the following line to put your filter instead of vtkToItkFilter
// For example: itkToVtkFilter->SetInput(filter->GetOutput());
itkToVtkFilter->SetInput(gradientFilter->GetOutput());
// --------------------- Actually execute all filters parts --------------------
itkToVtkFilter->Update();
// --------------------- Create and return a copy (the filters will be deleted)--
vtkSmartPointer<vtkImageData> resultImage = itkToVtkFilter->GetOutput();
int extent[6];
resultImage->GetExtent(extent);
result->SetExtent(extent);
result->DeepCopy(resultImage);
// Set CamiTK progress bar back to zero (the processing filter is over)
observer->Reset();
return result;
}
|
