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Proceedings Paper

Fuzzy clustering of fMRI data: toward a theoretical basis for choosing the fuzziness index
Author(s): Martin Buerki; Helmut Oswald; Gerhard Schroth
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Paper Abstract

The fuzzy clustering algorithm (FCA) is a promising approach for the unsupervised analysis of complex fMRI studies with unknown input functions. Among the few parameters required by the FCA, the fuzziness index m plays an important role and the outcome of the clustering depends strongly on it. Unfortunately, there is no theoretical basis currently known for choosing the value of m and so far, empirical approaches have been carried out to find a reasonable value. The theoretical approach presented here calculates the probability distribution of the membership values uij during one iteration of the FCA and judges the regularity of this distribution, therefore indicating the degree of fuzziness of the resulting partition. This allows us to estimate the compactness of the clusters. It turns our that this probability does not only depend on the fuzziness index m, but also on the length of the time courses, a fact that was until now not noticed. Consequently, a reasonable choice of the fuzziness index depends on the signal to noise ratio and the temporal dimension of the data.

Paper Details

Date Published: 9 May 2002
PDF: 9 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467054
Show Author Affiliations
Martin Buerki, Univ. Hospital of Bern (Switzerland)
Helmut Oswald, T-Systems Health Care Systems AG (Switzerland)
Gerhard Schroth, Univ. Hospital of Bern (Switzerland)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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