BrainSuite

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BrainSuite is a suite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools.

 

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BrainSuite
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Features

  • Cortical surface extraction, using a multi-stage user friendly approach. Tools include brain surface extraction, bias field correction, voxel classification, cerebrum labeling, and surface generation
  • Sophisticated visualization tools, including MRI visualization in 3 orthogonal views, and surface visualization of cortex, skull, and scalp models
  • Easy-to-use tools for voxel-based delineation of anatomical structures or regions of interest
  • Semi-automatic tools for curve-based delineation of cortical landmarks

 

BrainSuite Support

  

Download Details

Version: 9.01
Release Date:  2009-08-21
Developer(s): David W. Shattuck
License: LONI Software License
File Size: 25.89 MB
 
SYSTEM REQUIREMENTS OS: Linux 32-bit, Memory: 2 GB, Processor: X86
Reference(s)
Shattuck DW and Leahy RM (2002) BrainSuite: An Automated Cortical Surface Identification Tool, Medical Image Analysis, 8(2):129-142.
 
Shattuck DW and Leahy RM (2001) Graph Based Analysis and Correction of Cortical Volume Topology, IEEE Transactions on Medical Imaging, 20(11):1167-1177.
 
Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, and Leahy RM (2001) Magnetic Resonance Image Tissue Classification Using a Partial Volume Model,” NeuroImage 13(5):856-876.
 
Dogdas B, Shattuck DW, and Leahy RM (2005) Segmentation of Skull and Scalp in 3D Human MRI Using Mathematical Morphology Human Brain Mapping 26(4):273-85
 
Shattuck DW, Joshi AA, Pantazis D, Kan E, Dutton RA, Sowell ER, Thompson PM, Toga AW, Leahy RM (in press) Semi-automated Method for Delineation of Landmarks on Models of the Cerebral Cortex, Journal of Neuroscience Methods
 
Acknowledgement(s)
This work was supported by:
NIH-NCRR P41 RR013642
NIH-NIBIB R01 EB002010
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