Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods

Oliver Lange; Anke Meyer-Baese; Axel Wismuller M.D.; Monica Hurdal

doi:10.1117/12.601005

28 March 2005 Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods

Oliver Lange, Anke Meyer-Baese, Axel Wismuller M.D., Monica Hurdal

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Proceedings Volume 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III; (2005) https://doi.org/10.1117/12.601005
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Oliver Lange, Anke Meyer-Baese, Axel Wismuller M.D., and Monica Hurdal "Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods", Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); https://doi.org/10.1117/12.601005

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