Paper
3 July 2001 Additional speed-up technique to fuzzy clustering using a multiresolution approach
Martin Buerki, Helmut Oswald, Karl Loevblad, Gerhard Schroth
Author Affiliations +
Abstract
The fuzzy clustering algorithm (FCA) is a powerful tool for unsupervised investigation of complex data in functional MRI. The original, computationally very expensive algorithm has been adapted in various ways to increase its performance while keeping it stable and sensitive. A simple and highly efficient way to speed up the FCA is preselection (screening) of potentially interesting time-courses, in a way that those time-courses, where only noise is expected are discarded. Although quite successful, preselecting data by some criterion is a step back to model driven analysis and should therefore be used with deliberation. Furthermore, some screening methods run the risk of missing non-periodic signals. We propose an additional adaptation using a multi-resolution approach that first scales down the data volumes. Starting with the lowest resolution, the FCA is applied to that level and then, the computed centroids are used as initial values to the FCA for the next higher level of resolution and so on until the original resolution is reached. The processing of all lower resolution levels serves as a good and fast initialization of the FCA, resulting in a stable convergence and an improved performance without loss of information.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Martin Buerki, Helmut Oswald, Karl Loevblad, and Gerhard Schroth "Additional speed-up technique to fuzzy clustering using a multiresolution approach", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.430989
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Computer simulations

Data modeling

Functional magnetic resonance imaging

Fuzzy logic

Brain activation

Image processing

Magnetic resonance imaging

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