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Max Homogeneity

StreamableKNIME Image Types and Nodes version 1.7.0.201812031714 by University of Konstanz

The Maximum Homogeneity Neighborhood Node lays 8 different areas around one Pixel and applies a measurement of homogeneity which is then processed in some sort of weight. Then, it calculates each areas mean-value and combines both mean-value and weight to the new pixel value, so that the most homogeneous areas take the most part in the new value of the pixel and areas that lay over edges are not taken into account. Via a parameter you can regulate the influence of the weights.

Options

Lambda
The Parameter to determine the influence of the weights via weights^lambda. For lambda = 0 the filter becomes a simple mean filter which filters the most noise but preserves no edges. For lambda -> infinity only the most homogeneous area is taken to get the new Pixel-Value thus Edges are most preserved but the denoising is not as strong.
Window Span
The Window Span parameter determines the span of the window in one direction. The resulting window size is given by: span*2+1 in each dimension. So the bigger it gets, the more noise is filtered out but the less details are preserved.
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.

Out of Bounds Selection

The 'OutOfBounds Strategy' is used when an algorithm needs access to pixels which lie outside of an image (for example convolutions). The strategy determines how an image is extended, for examples see here

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.

Input Ports

Images to Filter

Output Ports

Filtered Images

Views

Image Viewer
Another, possibly interactive, view on table cells. Displays the selected cells with their associated viewer if it exists. Available views are:
- 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.
- Missing Value Viewer
-- An empty viewer that is shown when the input cell has no value to display.
- 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.
- Image Viewer
-- This viewer renders the selected image-cell.
- Histogram Viewer
-- This viewer shows the histogram of the currently selected image.
- XML
-- XML tree
- Labeling View
-- View on a labeling/segmentation

Update Site

To use this node in KNIME, install KNIME Image Types and Nodes from the following update site:

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