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Proceedings Paper

Performance evaluation based on cluster validity indices in medical imaging
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Paper Abstract

Exploratory data-driven methods such as unsupervised clustering are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). The major problem with clustering of real bioimaging data is that of deciding how many clusters are present. This motivates the application of cluster validity techniques in order to quantitatively evaluate the results of the clustering algorithm. In this paper, we apply three different cluster validity techniques, namely, Kim's index, Calinski Harabasz index, and the intraclass index to the evaluation of the clustering results of fMRI data. The benefits and major limitations of these cluster validity techniques are discussed based on the achieved results of several datasets.

Paper Details

Date Published: 17 April 2006
PDF: 9 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624714 (17 April 2006); doi: 10.1117/12.660670
Show Author Affiliations
Oliver Lange, Florida State Univ. (United States)
Anke Meyer-Bäse, Florida State Univ. (United States)
Axel Wismüller, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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