Image Normalizer

Enhances the images contrast by using the selected method. The image can be either normalized and use an optional stauration threshold or it can be equalized by linearizing the cumulative distribution function of the image's histogramm.

Options

Enhancement Type

Choose either "Normalize", "Equalize" or "Manual".

• Normalize: This enhancement type stretches the histogram of the image. I.e. your image has an intensity range from 50-170 and the data type range is 0 to 255 then 50 gets substracted from each pixel intensity (so the new range is 0 to 120) and is then multiplied with 255/120 so the new intensity range is 0 to 255.
• Equalize: This enhancement type linearizes the cumulative distribution function of the image's histogram. This enhancement differs from normalization as it does not only stretches the histogram but also compresses it (please note that only 256 bins are used for the required histogram!).
• Manual: This enhancement type allows you to set the minimum and maximum value of the source or of the target image manually. For example, if you want to normalize the image values between 0 and 1, your should check the checkbox 'isTarget' and set min = 0 and max = 1.

Saturation (%)

Enter a percentage how many pixels are already saturated. This parameter only affects the normalizer.

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.

ROI Options

Labeling Column

If you choose a column with a labeling here, the node will only operate on the Regions of Interests (ROIs) which are defined by the incoming labeling!

Filling Mode

The FillingMode determines how all values, which lie outside your defined region of interest, will be set. This option is only needed if you choose a labeling column, such that the node operates on ROIs instead of the entire image. There are currently four FillingModes:

• Value of Source: In this mode, pixels outside of the ROIs remain unchanged.
• Minimum of Result Type: Here, values outside of the ROI are set to the smallest legal value of the output image type.
• Maximum of Result Type: All values outside of the ROI are set to the largest posible value of the output image type.
• No Filling: No action is taken after initializing the target image, thus all pixels outside the ROIs remain zero.

Views

Image Viewer

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

Workflows

• 01_Gradient_MagnitudeKNIME Hub
• 01_Spot_DetectionKNIME Hub
• 01_Spot_Detection modif KNIME Forum
• 01_Tess4JKNIME Hub
• 01_Tess4JNodePit Space
• Show all 38 workflows

Links

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Developers