TubeTK/Documentation/EnhanceEdgesUsingDiffusion
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Description: Performs edge enhancing anisotropic diffusion: smooths strongly in the direction parallel to an edge, while smoothing normal to the edge is inversely proportional to the gradient. Implemented as described in Mendrik et al., Noise reduction in computed tomography scans using 3-D anisotropic diffusion with continuous switch, IEEE Transactions on Medical Imaging, 28(10), pp. 1585-1594, 2009.
USAGE:
./EnhanceEdgesUsingDiffusion [--returnparameterfile <std::string>] [--processinformationaddress <std::string>] [--xml] [--echo] [-n <int>] [-t <double>] [-e <double>] [-s <double>] [--] [--version] [-h] <std::string> <std::string>
Where:
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)
--xml Produce xml description of command line arguments (default: 0)
--echo Echo the command line arguments (default: 0)
-n <int>, --numIterations <int> Number of iterations used in optimization. (default: 1)
-t <double>, --timeStep <double> Time step used in optimization. (default: 0.11)
-e <double>, --EEDContrast <double> Contrast of edges, as opposed to noise. If set too low, noise will not be filtered. If set too high, plate-like structures will not be preserved. (default: 30)
-s <double>, --scale <double> Scale at which first derivatives are calculated when determining the structure tensor and gradient magnitude. If set too low, filter will be overly sensitive to noise. If set too high, small structures will not be well preserved. (default: 1)
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.
--version Displays version information and exits.
-h, --help Displays usage information and exits.
<std::string> (required) Input volume.
<std::string> (required) Output volume.
Author(s): Danielle F. Pace, Andinet Enquobahrie, Hua Yang, Stephen R. Aylward (Kitware)
Acknowledgements: This work is part of the TubeTK project at Kitware.