We employ unsupervised clustering techniques for the analysis of dynamic contrast-enhanced perfusion MRI time-series in patients with and without stroke. "Neural gas" network, fuzzy clustering based on deterministic annealing, self-organizing maps, and fuzzy c-means clustering enable self-organized data-driven segmentation w.r.t.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.

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); doi: 10.1117/12.601005

Published in SPIE Proceedings Vol. 5818:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III
Harold H. Szu, Editor(s)