Fully automated biomedical image segmentation by self-organized model adaptation

Neural Networks

Volumn 17, Issue 8-9, 2004, Pages 1327-1344

  Wismüller, Axel ^a,b   Vietze, Frank ^a   Behrends, Johannes ^a   Meyer Baese, Anke ^b   Reiser, Maximilian ^a   Ritter, Helge ^c  

^a LUDWIG MAXIMILIANS UNIVERSITY   (Germany)

^b FLORIDA STATE UNIVERSITY   (United States)

^c BIELEFELD UNIVERSITY   (Germany)

Author keywords

Feature maps; Image segmentation; Knowledge Discovery; Magnetic resonance imaging; Neural networks; Pattern analysis; Self Organization

Indexed keywords

DATA SETS; SELF-ORGANIZED DEFORMATION; VISUAL CLASSIFICATION; VOXEL CLUSTERS;

ALGORITHMS; APPROXIMATION THEORY; BIOMEDICAL ENGINEERING; BRAIN; FEATURE EXTRACTION; IMAGE SEGMENTATION; MAGNETIC RESONANCE; PROBLEM SOLVING; SET THEORY;

SELF ORGANIZING MAPS;

ALGORITHM; BRAIN; CEREBROSPINAL FLUID; CONFERENCE PAPER; EXPERIMENTAL MODEL; GRAY MATTER; HUMAN; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE PROCESSING; IMAGE QUALITY; LABORATORY AUTOMATION; LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PLASTICITY; PRIORITY JOURNAL; PROBABILITY; PROBLEM SOLVING; THEORETICAL STUDY; WHITE MATTER;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS (COMPUTER);

EID: 9144265904     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2004.06.015     Document Type: Conference Paper

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