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#-wd1419 #Needed for Intel compilers with remark #1419: external declaration in primary source file
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#-wd1572 #Needed for Intel compilers with remark #1572: floating-point equality and inequality comparisons are unreliable
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#-wd2259 #Needed for Intel compilers with remark #2259: non-pointer conversion from "itk::SizeValueType={unsigned long}" to "double" may lose significant bits
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#-wd1268 #Needed for Intel compliers with warning #1268: support for exported templates is disabled
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#-wd1268 #Needed for Intel compilers with warning #1268: support for exported templates is disabled
Copy file name to clipboardExpand all lines: SoftwareGuide/Latex/DesignAndFunctionality/ConfidenceConnectedOnBrainWeb.tex
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The previous code was used in this example replacing the image dimension by 3.
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Gradient Anistropic diffusion was applied to smooth the image. The filter used 2 iterations, a time step of 0.05 and a conductance value of 3. The smoothed volume was then segmented using the Confidence Connected approach. Five seed points were used at coordinate locations (118,85,92), (63,87,94), (63,157,90), (111,188,90), (111,50,88). The ConfidenceConnnected filter used the parameters, a neighborhood radius of 2, 5 iterations and an $f$ of 2.5 (the same as in the previous example). The results were then rendered using VolView.
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Figure \ref{fig:3DregionGrowingScreenshot1} shows the rendered volume. Figure \ref{fig:SlicesBrainWeb} shows an axial, saggital and a coronal slice of the volume.
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Figure \ref{fig:3DregionGrowingScreenshot1} shows the rendered volume. Figure \ref{fig:SlicesBrainWeb} shows an axial, sagittal and a coronal slice of the volume.
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