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Wismuller, A.; Meyer-Baese, A.; Lange, O.; Reiser, M.F.; Leinsinger, G.
IEEE transactions on medical imaging, 2006-Jan., 2006-Jan, 2006-01-00, 20060101, Letnik: 25, Številka: 1Journal Article
We performed neural network clustering on dynamic contrast-enhanced perfusion magnetic resonance imaging time-series in patients with and without stroke. Minimal-free-energy vector quantization, self-organizing maps, and fuzzy c-means clustering enabled self-organized data-driven segmentation with respect to fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.
