Modules > Images > Process

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 imageImage to apply pixel classification to.
Convert to RGBConverts a composite image to RGB format. This should be set to match the image-type used for generation of the model.
Output imageOutput probability map image.
Output bit depthBy 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 classAllows a single class (image channel) to be output. This is another feature for reducing memory usage.
Output classClass (image channel) to be output. Channel numbering starts at 1.
Path typeMethod to use for generation of the classifier filename:
  • "Matching format" Will generate a name from metadata values stored in the current workspace. This is useful if the classifier varies from input file to input file.
  • "Specific file" Will load the classifier file at a specific location. This is useful if the same file is to be used for all input files.
Generic formatFormat 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 fieldsList of the currently-available metadata values for this workspace. These can be used when compiling a generic filename.
Classifier file pathPath to the classifier file (.model extension). This file needs to be created manually using the WEKA Trainable Segmentation plugin included with Fiji.
Simultaneous slicesNumber of image slices to process at any given time. This reduces the memory footprint of the module, but can slow down processing.
Tile factorNumber 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.