Anisotropic Diffusion

This Node implements the so-called anisotropic diffusion scheme of Perona and Malik, 1990. For details on the anisotropic diffusion principles, see: http://en.wikipedia.org/wiki/Anisotropic_diffusion, and the original paper: Perona and Malik. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence (1990) vol. 12 pp. 629-639

Options

Diffusion Function

This Option allows to use different Functions to be used for filtering. At the moment there are two Functions available: 1. STRONG_EDGE_ENHANCER can be used to enhance Edges and smooth everything in between 2. WIDE_REGION_ENHANCER doesn't preserve Edges but gives a very smooth image in total.

Kappa

This parameter depends on the Function one uses. Usually a greater value means a stronger filtering, but this may result in more artifacts. It is recommended to use more Iterations to get a stronger filtering result instead of increasing Kappa.

Delta t

This is the integration constant, typically less than 1. Usually a greater value means a stronger filtering, but this may result in more artifacts. It is recommended to use more Iterations to get a stronger filtering result instead of increasing Delta t.

Iterations

This number says how often the Method is applied to the image, with the settings above. Be careful not to choose a value too large, this may slow down your calculations.

Dimension Selection

This component allows the selection of dimensions of interest. If an algorithm cannot, as it only supports fewer dimensions than the number of dimensions of the image, or shouldnot, as one wants to run the algorithm only on subsets of the image, be applied on the complete image, the dimension selection can be used to define the plane/cube/hypercube on which the algorithm is applied. Example 1 with three dimensional Image (X,Y,Time): An algorithm cannot be applied to the complete image, as it only supports two dimensional images. If you select e.g. X,Y then the algorithm will be applied to all X,Y planes individually. Example 2 with three dimensional Image (X,Y,Time): The algorithm can be applied in two, three or even more dimensions. Select the dimensions to define your plane/cube/hypercube on which the algorithm will be applied.

Column Selection

Column Creation Mode

Mode how to handle the selected column. The processed column can be added to a new table, appended to the end of the table, or the old column can be replaced by the new result

Column Suffix

A suffix appended to the column name. If "Append" is not selected, it can be left empty.

Column Selection

Selection of the columns to be processed.

Views

Image Viewer

Another, possibly interactive, view on table cells. Displays the selected cells with their associated viewer if it exists. Available views are:
- Labeling View
-- View on a labeling/segmentation
- Combined View
-- A viewer shown when the user selects an interval of rows and columns in the viewer. This viewer combines all images and labelings in the selected interval to one image by rendering them next to each other. Alternatively, the images and labelings can be layed over each other.
- XML
-- XML tree
- Histogram Viewer
-- This viewer shows the histogram of the currently selected image.
- Image Viewer
-- This viewer renders the selected image-cell.
- Missing Value Viewer
-- An empty viewer that is shown when the input cell has no value to display.
- BigDataViewer
-- A viewer shown when the user selects an interval of rows and columns in the viewer. This viewer combines all images and labelings in the selected interval to one image by rendering them next to each other. Alternatively, the images and labelings can be layed over each other.

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