Weka probability maps
Performs pixel classification using the WEKA Trainable Segmentation plugin.
Description
Performs pixel classification using the WEKA Trainable Segmentation plugin.
This module loads a previously-saved WEKA classifier model and applies it to the input image. It then returns the multi-channel probability map.
Image stacks are processed in 2D, one slice at a time.
Parameters
Parameter | Description |
Input image | Image to apply pixel classification to. |
Convert to RGB | Converts a composite image to RGB format. This should be set to match the image-type used for generation of the model. |
Output image | Output probability map image. |
Output bit depth | By default images will be saved as floating point 32-bit (probabilities in the range 0-1); however, they can be converted to 8-bit (probabilities in the range 0-255) or 16-bit (probabilities in the range 0-65535). This is useful for saving memory or if the output probability map will be passed to image threshold module. |
Output single class | Allows a single class (image channel) to be output. This is another feature for reducing memory usage. |
Output class | Class (image channel) to be output. Channel numbering starts at 1. |
Path type | Method to use for generation of the classifier filename:
|
Generic format | Format for a generic filename. Plain text can be mixed with global variables or metadata values currently stored in the workspace. Global variables are specified using the "V{name}" notation, where "name" is the name of the variable to insert. Similarly, metadata values are specified with the "M{name}" notation. |
Available metadata fields | List of the currently-available metadata values for this workspace. These can be used when compiling a generic filename. |
Classifier file path | Path to the classifier file (.model extension). This file needs to be created manually using the WEKA Trainable Segmentation plugin included with Fiji. |
Simultaneous slices | Number of image slices to process at any given time. This reduces the memory footprint of the module, but can slow down processing. |
Tile factor | Number of tiles per dimension each image will be subdivided into for processing. For example, a tile factor of 2 will divide the image into a 2x2 grid of tiles. This reduces the memory footprint of the module. |