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Proceedings Paper

Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods
Author(s): Oliver Lange; Anke Meyer-Baese; Axel Wismuller; Monica Hurdal
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Paper Abstract

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.

Paper Details

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
Show Author Affiliations
Oliver Lange, Florida State Univ. (United States)
Anke Meyer-Baese, Florida State Univ. (United States)
Axel Wismuller, Ludwig-Maximilians-Univ. Munchen (Germany)
Monica Hurdal, Florida State Univ. (United States)


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

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