MRI: stability of three supervised segmentation techniques.
Supervised segmentation methods from three families of pattern recognition techniques were used to segment multispectral MRI data. Studied were the maximum likelihood method (MLM), k-nearest neighbors (k-NN), and a back-propagation artificial neural net (ANN). Performance was measured in terms of execution speed, and stability for the selection of training data, namely, region of interest (ROI) selection, and interslice and interpatient classifications. MLM proved to have the smallest execution times, but demonstrated the least stability. k-NN showed the best stability for training data selection. To evaluate the segmentation techniques, multispectral images were used of normal volunteers and patients with gliomas, the latter with and without MR contrast material. All measures applied indicated that k-NN provides the best results.
Clarke
L P
LP
Center for Engineering and Medical Image Analysis (CEMIA), College of Engineering, University of South Florida, Tampa 33612.
Velthuizen
R P
RP
Phuphanich
S
S
Schellenberg
J D
JD
Arrington
J A
JA
Silbiger
M
M
D000328:Adult; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis / Q000453:epidemiology; D003287:Contrast Media; D005069:Evaluation Studies as Topic; D005682:Gadolinium; D019786:Gadolinium DTPA; D005910:Glioma / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D016571:Neural Networks, Computer; D015588:Observer Variation; D009942:Organometallic Compounds; D010363:Pattern Recognition, Automated; D004369:Pentetic Acid
D003287:Contrast Media; D009942:Organometallic Compounds; D004369:Pentetic Acid; D005682:Gadolinium; D019786:Gadolinium DTPA