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DoG Spot Detection

StreamableKNIME Image Types and Nodes version 1.7.0.201812031714 by University of Konstanz

In the computer vision literature, this approach is referred to as the Difference of Gaussians (DoG) approach. Besides minor technicalities, however, this operator is in essence similar to the Laplacian and can be seen as an approximation of the Laplacian operator. In a similar fashion as for the Laplacian blob detector, blobs can be detected from scale-space extrema of differences of Gaussians (see http://en.wikipedia.org/wiki/Blob_detection#The_difference_of_Gaussians_approach).

Options

Maxima/Minima?
Select whether you want to detect minima or maxima.
Threshold
Threshold value for detected extrema. Maxima below or minima above the value will be disregarded.
Normalize Threshold?
Whether the peak value should be normalized. If this option is checked, the DoG will be scaled by Sigma1 / (Sigma2 - Sigma1).
Sigma 1
Sigma for the smaller scale
Sigma 2
Sigma for the larger scale
Out of Bounds Strategy
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 Fiji Wiki
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.

Input Ports

Images

Output Ports

BitType Images of Spots

Views

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