Abstract
Standard Fuzzy C-Means (FCM) clustering has been widely used as an effective method for image segmentation. However, FCM is sensitive to initialization and is easily trapped in local optima. In this paper, several enhanced models for FCM clustering were proposed, namely W_SS_FCM, LAWS_SS_FCM and H_FCM, to promote the performance of standard FCM. The proposed algorithms merge partial supervision with spatial locality to increase conventional FCM’s robustness. A comparison study was conducted to validate the proposed methods’ performance applying well established measures on three datasets. Experimental results show considerable improvement over standard FCM and other variants of the algorithm. It also manifests high robustness against noise attacks.
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El-Sonbaty, Y., Youssef, S.M., Fathalla, K.M. (2011). Enhanced Fuzzy-Based Models for ROI Extraction in Medical Images. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_4
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DOI: https://doi.org/10.1007/978-3-642-27183-0_4
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