Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers

R. Craig Herndon, Jack L. Lancaster, Arthur W. Toga, Peter T. Fox

Research output: Contribution to journalArticle

39 Scopus citations

Abstract

White matter (WM) and gray matter (GM) were accurately measured using a technique based on a single standardized fuzzy classifier (FC) for each tissue. Fuzzy classifier development was based on experts' visual assessments of WM and GM boundaries from a set of T1 parametric MR images. The fuzzy classifier method's accuracy was validated and optimized by a set of T1 phantom images that were based on hand-detailed human brain cryosection images. Nine sets of axial T1 images of varying thickness equally distributed throughout the brain were simulated. All T1 data sets were mapped to the standardized FCs and rapidly segmented into WM and GM voxel fraction images. Resulting volumes revealed that, in most cases, the difference between measured and actual volumes was less than 5%. This was consistent throughout most of the brain, and as expected, the accuracy improved to generally less than 2% for the 1-mm simulated brain slices.

Original languageEnglish (US)
Pages (from-to)425-435
Number of pages11
JournalJournal of Magnetic Resonance Imaging
Volume6
Issue number3
DOIs
StatePublished - 1996

Keywords

  • Brain, MR
  • Image processing
  • Volume measurement

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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