Citation
Li, Min and Huang, Tinglei and Zhu, Gangqiang
(2008)
Improved Fast Fuzzy C-Means Algorithm for Medical MR Images
Segmentation.
Journal of Circuits Systems and Computers.
pp. 285-288.
ISSN 0218-1266
Abstract
Fuzzy c-means (FCM) clustering algorithm has been
widely used in automated image segmentation. However,
the standard FCM algorithm takes a long time to
partition a large dataset. In addition, in current fuzzy
cluster algorithms it is difficult to determine the cluster
centers. This paper proposes a modified FCM algorithm
for MR (Magnetic Resonance) brain images
segmentation. This method fetches in statistic histogram
information for minimizing the iteration times, and in the
iteration process, the optimal number of clusters is
automatically determined. Using this method, an optimal
classification rate is obtained in the test dataset, which
includes large stochastic noises. The experiment results
have shown that the segmentation method proposed in
this paper is more accurate and faster than the standard
FCM or the fast fuzzy c-means (FFCM) algorithm.
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