Automatic magnetic resonance tissue characterization for three-dimensional magnetic resonance imaging of the brain.
Computer-assisted diagnostic systems enhance the information available from magnetic resonance imaging. Segmentations are the basis on which three-dimensional volume renderings are made. The application of a raw data-based, operator-independent (automatic), magnetic resonance segmentation technique for tissue differentiation is demonstrated. Segmentation images of vasogenic edema with gross and histopathological correlation are presented for demonstration of the technique. A pixel was classified into a tissue class based on a feature vector using unsupervised fuzzy clustering techniques as the pattern recognition method. Correlation of fuzzy segmentations and gross and histopathology were successfully performed. Based on the results of neuropathological correlation, the application of fuzzy magnetic resonance image segmentation to a patient with a brain tumor and extensive edema represents a viable technique for automatically displaying clinically important tissue differentiation. With this pattern recognition technique, it is possible to generate automatic segmentation images that display diagnostically relevant neuroanatomical and neuropathological tissue contrast information from raw magnetic resonance data for use in three-dimensional volume reconstructions.
Phillips
W E
WE
Department of Radiology, Medical College of Georgia, Augusta 30912-3900, USA.
Phuphanich
S
S
Velthuizen
R P
RP
Silbiger
M L
ML
D000328:Adult; D000465:Algorithms; D001929:Brain Edema / Q000175:diagnosis* / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003626:Data Display; D017143:Fuzzy Logic; D005909:Glioblastoma / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis / Q000473:pathology; D016609:Neoplasms, Second Primary / Q000175:diagnosis / Q000473:pathology; D010363:Pattern Recognition, Automated