TMI Viewer Instructions Examples
The tmi_viewer package contains two programs:
Displays multimodal neuroimaging data (surfaces and volumes) in the same space from a TMI file (although TMI files are not required).
Features:
- Multiple surfaces with vertex painting can be easily viewed with voxel images.
- Voxel images can be viewed as: (1) surfaces using a marching cube algorithm, (2) voxel contour, (3) and voxel scalar field.
- The default settings are optimized for viewing neuroimages.
- tmi_viewer is highly optimized for speed.
- Many autothresholding algorithms available including: Otsu et al., Li et al., Yen et al., and Z threholding. (See Citations)
- Extremely fast algorithm for applying Lapacian or Taubin (low-pass) smooth. e.g., ~1000 passes takes around one minute.
- Easy export of background transparent images for creating figures.
- Many new look-up tables (LUTs) that are specifically for visualising neuroimaging statistics, as well as all LUTs included with matplotlib.
Outputs a web-page with whole brain slices from voxel-based neuroimages in native coordinates with optional overlaps.
Features:
- Great for making figures: Creates a web-page displaying overlapping any number of voxel-based images, and they can be at any resolution.
- Many autothresholding algorithms available including: Otsu et al., Li et al., Yen et al., and Z threholding. (See Citations)
- Import images, binarize them at any threshold, and paint the image outline.
- Specify number of slices, size of slices, transparency, etc.
These programs relies on Mayavi, and setting can changed using the Mayavi interactive session. If you use them please cite:
Ramachandran, P. and Varoquaux, G., Mayavi. 3D Visualization of Scientific Data. IEEE Computing in Science & Engineering, 13 (2), pp. 40-51 (2011).