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
- Description: Image to apply pixel classification to.
- Type: InputImageP
- Convert to RGB
- Description: Converts a composite image to RGB format. This should be set to match the image-type used for generation of the model.
- Type: BooleanP
- Default value: false
- Output image
- Description: Output probability map image.
- Type: OutputImageP
- Output bit depth
- Description: 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.
- Type: ChoiceP
- Default value: 32
- Choices:
- 8
- 16
- 32
- Output single class
- Description: Allows a single class (image channel) to be output. This is another feature for reducing memory usage.
- Type: BooleanP
- Default value: false
- Output class
- Description: Class (image channel) to be output. Channel numbering starts at 1.
- Type: IntegerP
- Default value: 1
- Classifier file path
- Description: Path to the classifier file (.model extension). This file needs to be created manually using the WEKA Trainable Segmentation plugin included with Fiji.
- Type: FilePathP
- Block size (simultaneous slices)
- Description: Number of image slices to process at any given time. This reduces the memory footprint of the module, but can slow down processing.
- Type: IntegerP
- Default value: 1