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Introduction and Acknowledgements
- Title: Mask Scalar Volume
- Author(s)/Contributor(s): Nicole Aucoin (SPL, BWH), Ron Kikinis (SPL, BWH)
- Acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
- Contact: Nicole Aucoin,
Information on NA-MIC can be obtained from the NA-MIC website.
Surgical Planning Laboratory
Masks two images. The output image is set to 0 everywhere except where the chosen label from the mask volume is present, at which point it will retain it's original values. Although all image types are supported on input, only signed types are produced. The two images do not have to have the same dimensions.
Most frequently used for these scenarios:
- Use Case 1: Extracting the region or structures of interest using the mask.
Examples of the module in use:
- Recovering the bone structures using a mask on CT images
Brain masked with the label map at value 72, as well as showing the generated model from the segmented image.
Panels and their use
- Input and Output: Input/output parameters
- Input Volume (InputVolume): Input volume to be masked
- Mask Volume (MaskVolume): Label volume containing the mask
- Masked Volume (OutputVolume): Output volume: Input Volume masked by label value from Mask Volume
- Settings: Filter settings
- Label value (Label): Label value in the Mask Volume to use as the mask
- Replace value (Replace): Value to use for the output volume outside of the mask
List of parameters generated transforming this XML file using this XSL file. To update the URL of the XML file, edit this page.
- Brains module Foreground Masking
Information for Developers