Segmentation of magnetic resonance images using a neuro-fuzzy algorithm

Ramiro Castellanos, Sunanda Mitra

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


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.

Original languageEnglish
Pages (from-to)207-212
Number of pages6
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
StatePublished - 2000
EventCBMS 2000: 13th IEEE Sympoisum on Computer-Based Medical Systems - Houston, TX, USA
Duration: Jun 22 2000Jun 24 2000


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