A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Fundamentals of digital image processing
Fundamentals of digital image processing
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Digital Image Processing
Survey on the use of smart and adaptive engineering systems in medicine
Artificial Intelligence in Medicine
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This paper evaluates a segmentation technique for Magnetic Resonance (MR) images of the brain based on the adaptive fuzzy leader clustering (AFLC) algorithm. This approach performs vector quantization by updating the winning prototype of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the value of a vigilance parameter restricts the number of prototypes representing the feature vectors. The choice of the misclassification rate (MCR) as a quantitative measure shows that AFLC outperforms other existing segmentation methods.