Automated segmentation of multispectral brain MR images

Anders H. Andersen, Zhiming Zhang, Malcolm J. Avison, Don M. Gash

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


This work presents a robust and comprehensive approach for the in vivo automated segmentation and quantitative tissue volume measurement of normal brain composition from multispectral magnetic resonance imaging (MRI) data. Statistical pattern recognition methods based on a finite mixture model are used to partition the intracranial volume into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) spaces. A masking algorithm initially extracts the brain volume from surrounding extrameningeal tissue. Radio frequency (RF) field inhomogeneity effects in the images are then removed using a recursive method that adapts to the intrinsic local tissue contrast. Our technique supports heterogeneous data with multispectral MR images of different contrast and intensity weighting acquired at varying spatial resolution and orientation. The proposed image segmentation methods have been tested using multispectral T1-, proton density-, and T2-weighted MRI data from young and aged non-human primates as well as from human subjects.

Original languageEnglish
Pages (from-to)13-23
Number of pages11
JournalJournal of Neuroscience Methods
Issue number1
StatePublished - Dec 31 2002

Bibliographical note

Funding Information:
The authors wish to thank M. Zhang, R. Greene-Avison, and X. Wang for contributions to this paper. This work was supported by NIH Grants NS35080 (M.J.A.), NS25778 (D.M.G.), AG13494 (D.M.G.), and by the Vice Chancellor for Research and Graduate Studies, University of Kentucky Medical Center.


  • Automated
  • Computerized
  • Imaging
  • Magnetic
  • Multispectral
  • Resonance
  • Segmentation

ASJC Scopus subject areas

  • Neuroscience (all)


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