Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization.
This paper evaluates a segmentation technique for magnetic resonance (MR) images of the brain based on fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the local values of different relaxation parameters form the feature vectors which are represented by a relatively small set of prototypes. The experiments evaluate a variety of FALVQ algorithms in terms of their ability to identify different tissues and discriminate between normal tissues and abnormalities.
Karayiannis
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NB
Pai
P I
PI
D000465:Algorithms; D001921:Brain / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008577:Meningeal Neoplasms / Q000175:diagnosis; D008579:Meningioma / Q000175:diagnosis