Abstract
The image segmentation issues have been exploited by researchers over the years for diverse application. A hybrid algorithm for image segmentation is proposed in this paper which is the integration of fuzzy c means (FCM) clustering and swarm intelligence. The algorithm is applied to segmentation problems of two medical image modalities, i.e., magnetic resonance imaging (MRI) image, and computed tomography (CT) image. A detailed comparison of the different swarm intelligence based algorithms is presented. The optimization technique is used to generate optimized cluster centers in the image segmentation process. The effectiveness of the algorithms is validated by cluster validity indices.
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Ibungomacha Singh, T., Laishram, R., Roy, S. (2019). Comparative Study of Combination of Swarm Intelligence and Fuzzy C Means Clustering for Medical Image Segmentation. In: Luhach, A.K., Hawari, K.B.G., Mihai, I.C., Hsiung, PA., Mishra, R.B. (eds) Smart Computational Strategies: Theoretical and Practical Aspects. Springer, Singapore. https://doi.org/10.1007/978-981-13-6295-8_7
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DOI: https://doi.org/10.1007/978-981-13-6295-8_7
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