Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

Bruno Alfano, Arturo Brunetti, Eugenio M. Covelli, Mario Quarantelli, Maria Rosaria Panico, Andrea Ciarmiello, Marco Salvatore

Research output: Contribution to journalArticle

Abstract

The purpose of this study was the development and testing of a method for unsupervised, automated brain segmentation. Two spin-echo sequences were used to obtain relaxation rates and proton-density maps from 1.5 T MR studies, with two axial data sets including the entire brain. Fifty normal subjects (age range, 16 to 76 years) were studied. A Three-dimensional (3D) spectrum of the tissue voxels was used for automatic segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) and for calculation of their volumes. Accuracy and reproducibility were tested with a three-compartment phantom simulating GM, WM, and CSF. In the normal subjects, a significant decrease of GM fractional volume and increased CSF volume with age were observed (P <0.0001), with no significant changes in WM. This multi-spectral segmentation method permits reproducible, operator-independent volumetric measurements.

Original languageEnglish
Pages (from-to)84-93
Number of pages10
JournalMagnetic Resonance in Medicine
Volume37
Issue number1
DOIs
Publication statusPublished - Jan 1997

Keywords

  • brain
  • image processing
  • MRI
  • volume measurement

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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