| Literature DB >> 16398415 |
A Wismüller1, A Meyer-Baese, O Lange, M F Reiser, G Leinsinger.
Abstract
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.Entities:
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Year: 2006 PMID: 16398415 DOI: 10.1109/TMI.2005.861002
Source DB: PubMed Journal: IEEE Trans Med Imaging ISSN: 0278-0062 Impact factor: 10.048