Modules:MaskImage-Documentation-3.6

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Gallery of New Features


Mask Image

Mask Image

Mask Image GUI
Mask Image Input: Image + Labelmap
Mask Image Output

General Information

Module Type & Category

Type: CLI

Category: Filtering, Arithmetic

Authors, Collaborators & Contact

  • Nicole Aucoin, Brigham and Women's Hospital
  • Contact: Nicole Aucoin, nicole@bwh.harvard.edu

Module Description

Apply a labelmap as a mask and set all pixels outside the mask (i.e. not matching the label value) to zero (or a given replacement value)

Usage

Use Cases, Examples

This module is especially appropriate for these use cases:

  • Use Case 1: Extracting the region or structures of interest using the mask.

Examples of the module in use:

  • Example Test: Shows an example of recovering the bone structures using a mask on the CT images

MaskTest.cxx

Tutorials

Links to tutorials explaining how to use this module:

Quick Tour of Features and Use

  • Input and Output:
    • Input Volume: the image to be masked
    • Mask Volume: labelmap containing the region to be preserved as a specific (single) label value
    • Masked Volume: result image created. Equals the Input Volume with all voxels not part of the mask replaced.
  • Settings:
    • Label Value: value of the Mask Volume to use as ROI. All voxels not matching this value will be replaced. All voxels matching this value will be preserved.
    • Replace Value: value to set for all voxels "outside" the mask. To remove unwanted structure, zero (0) is the common default. All voxels not matching the Label Value above will be set to this Replace Value.

Development

Notes from the Developer(s)

This is a wrapper around the itk::ThresholdImageFilter.

Dependencies

The Volumes module is required for this module's use.

Tests

On the Dashboard, these tests verify that the module is working on various platforms:

Known bugs

Follow this link to the Slicer3 bug tracker.


Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation


More Information

Acknowledgment

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.

References