MRI: Stability of three supervised segmentation techniques

Magnetic Resonance Imaging

Volumn 11, Issue 1, 1993, Pages 95-106

  Clarke, L P ^a   Velthuizen, R P ^a   Phuphanich, S ^b   Schellenberg, J D ^a   Arrington, J A ^a   Silbiger, M ^a  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

^b H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

Author keywords

Image segmentation; MRI; Neural networks; Pattern recognition methods

Indexed keywords

ADULT; ANALYTIC METHOD; ARTICLE; CASE REPORT; CLASSIFICATION; COMPUTER ANALYSIS; COMPUTER MODEL; CONTRAST ENHANCEMENT; DATA ANALYSIS; GLIOMA; HUMAN; INTERMETHOD COMPARISON; MALE; MAXIMUM LIKELIHOOD METHOD; NUCLEAR MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION; PERFORMANCE; TRAINING;

ADULT; BRAIN; BRAIN NEOPLASMS; COMPARATIVE STUDY; CONTRAST MEDIA; EVALUATION STUDIES; GADOLINIUM; GADOLINIUM DTPA; GLIOMA; HUMAN; IMAGE ENHANCEMENT; LIKELIHOOD FUNCTIONS; MAGNETIC RESONANCE IMAGING; MALE; NEURAL NETWORKS (COMPUTER); OBSERVER VARIATION; ORGANOMETALLIC COMPOUNDS; PATTERN RECOGNITION; PENTETIC ACID; SUPPORT, NON-U.S. GOV'T; SUPPORT, U.S. GOV'T, NON-P.H.S.;

EID: 0027481396     PISSN: 0730725X     EISSN: None     Source Type: Journal    
DOI: 10.1016/0730-725X(93)90417-C     Document Type: Article

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