Quantification of white matter and gray matter volumes from three- dimensional magnetic resonance volume studies using fuzzy classifiers

R. Craig Herndon, Jack L. Lancaster, Jay N. Giedd, Peter T. Fox

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We accurately measured white matter (WM) and gray matter (GM) from three-dimensional (3D) volume studies, using a fuzzy classification technique. The new segmentation method is a modification of a recently published method developed for T1 parametric images. 3D MR images were transformed into pseudo forms of T1 parametric images and segmented into WM and GM voxel fraction images with a set of standardized fuzzy classifiers. This segmentation method was validated with synthesized 3D MR images as phantoms. These phantoms were developed from cryosectioned human brain images located in the superior, middle, and inferior regions of the cerebrum. Phantom volume measurements revealed that, generally, the difference between measured and actual volumes was less than 3% for 1.5-mm simulated brain slices. The average cerebral GM/WM ratio calculated from 3D MR studies in four subjects was 1.77, which compared favorably with the estimate of 1.67 derived from anatomical data. Results indicate that this is an accurate and rapid method for quantifying WM and GM from T1-weighted 3D volume studies.

Original languageEnglish (US)
Pages (from-to)1097-1105
Number of pages9
JournalJournal of Magnetic Resonance Imaging
Volume8
Issue number5
DOIs
StatePublished - Sep 1 1998

Keywords

  • Brain MR
  • Image processing
  • Volume measurement

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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