Weka pixel classification

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 mode	Controls whether the output image is a probability map or single channel classified image. For probabiliy maps, each class is assigned its own channel with floating point values in the range 0-1 depending on the likelihood of that pixel belonging to that class. With classified images the pixel value corresponds to the most probable class at that position (class numbering starts at 0).
Output image	Output image, which can be either a probability map or pre-assigned class 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: "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	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.