Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation
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 propos...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
World Scientific Co. Pte. Ltd.
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/11307/1/Improved%20Fast%20Fuzzy%20C.pdf http://psasir.upm.edu.my/id/eprint/11307/ |
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| Institution: | Universiti Putra Malaysia |
| Language: | English English |
| Summary: | 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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