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Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach
Author(s) -
Alfano Bruno,
Brunetti Arturo,
Covelli Eugenio M.,
Quarantelli Mario,
Panico Maria Rosaria,
Ciarmiello Andrea,
Salvatore Marco
Publication year - 1997
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910370113
Subject(s) - multispectral image , nuclear magnetic resonance , magnetic resonance imaging , segmentation , artificial intelligence , computer science , chemistry , pattern recognition (psychology) , physics , medicine , radiology
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.