Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers.
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.
Herndon
R C
RC
Department of Radiology and Research Imaging Center, University of Texas Health Science Center at San Antonio, USA.
Lancaster
J L
JL
Toga
A W
AW
Fox
P T
PT
D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation*; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D019047:Phantoms, Imaging; D012016:Reference Values; D015203:Reproducibility of Results