Abstract
Fuzzy c-means algorithm (FCM) is one of the most widely used clustering methods for modern medical image segmentation applications. However the conventional FCM algorithm has certain possibilities of converging to a local minimum of the objective function, thus lead to undesired segmentation results. To address this issue, an improved FCM which is based on clustering centroids updates with the use of particle swarm optimization (PSO) is proposed in this paper. This algorithm is designed to support multidimensional feature data and be accessible through parallel computation. The experimental results suggest that, compared to the conventional FCM algorithm, the proposed algorithm leads to higher chances of global optimum clustering and is less computationally intensive when large clustering number is needed.
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Pang, L., Xiao, K., Liang, A., Guan, H. (2012). A Improved Clustering Analysis Method Based on Fuzzy C-Means Algorithm by Adding PSO Algorithm. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_21
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DOI: https://doi.org/10.1007/978-3-642-28942-2_21
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