DoG Spot Detection

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.

Views

Image Viewer

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

Links

• No links available

Developers