Weka probability maps
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
- Input image (default = "") Image to apply pixel classification to.
- Convert to RGB (default = "false") Converts a composite image to RGB format. This should be set to match the image-type used for generation of the model.
- Output image (default = "") Output probability map image.
- Output bit depth (default = "32") 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 (default = "false") Allows a single class (image channel) to be output. This is another feature for reducing memory usage.
- Output class (default = "1") Class (image channel) to be output. Channel numbering starts at 1.
- Path type (default = "Specific file") Method 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 format (default = "") 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 (default = "") List of the currently-available metadata values for this workspace. These can be used when compiling a generic filename.
- Classifier file path (default = "") 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 (default = "1") 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 (default = "1") 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.