Curvature Flow

Class: NodeImageCurvatureFlowFilter

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Curvature driven image denoising algorithm. Iso-brightness contours in the grayscale input image are viewed as a level set. The level set is then evolved using a curvature-based speed function.

The advantage of this approach is that sharp boundaries are preserved with smoothing occurring only within a region. However, it should be noted that continuous application of this scheme will result in the eventual removal of all information as each contour shrinks to zero and disappear.

This filter has two parameters: the number of update iterations to be performed and the timestep between each update. The timestep should be “small enough” to ensure numerical stability. Stability is guaranteed when the timestep meets the CFL (Courant-Friedrichs-Levy) condition. Broadly speaking, this condition ensures that each contour does not move more than one grid position at each timestep. In the literature, the timestep is typically user specified and have to manually tuned to the application.

Example workflows

Denoising filters example workflow

Inputs

Image

Input image.

Type: Image4DFloat, Required, Single

Outputs

Output

Resulting image.

Type: Image4DFloat

Settings

Time Step Number

Set the timestep parameter.

Iterations Integer

Set the number of iterations.

References

CurvatureFlowImageFilter in SimpleITK