Local Thresholding

The local thresholding is performed in an efficient way. If possible (depends on the chosen method) an integral image approach is used which is very fast.

Options

Thresholding method

The used thresholding Method: Mean, Median, Sauvola, MidGrey, Bernsen, Niblack. Mean, Niblack and Sauvola are implemented using the fast integral image approach.

Parameters

Thresholding method dependent parameters. Window size specifies the size of the local region. Other parameters: Contrast threshold (needed for Bernsen), c (constant offset value, needed for Mean, Median, Niblack and MidGrey), k (needed for Niblack and Sauvola with different values) and r (needed for Sauvola).

Speed Up

Activates the usage of integral images. If the sum of all pixels is smaller than the internal type of the integral image overflows will occur.

Neighborhood Type

Select a rectangle or circle neighborhood as sliding window.

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

Window Span

The size of the span which will be used by the algorithm.

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:
- XML
-- XML tree
- 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.
- Image Viewer
-- This viewer renders the selected image-cell.
- Histogram Viewer
-- This viewer shows the histogram of the currently selected image.
- 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.
- Labeling View
-- View on a labeling/segmentation

Workflows

• 01_Spot_DetectionKNIME Hub
• 01_Spot_Detection modif KNIME Forum
• Akhtar_et_al_Bacterial_FISH_Example_WorkflowKNIME Hub

Links

• No links available

Developers