Abstract
This paper presents a Fuzzy C-Means based image segmentation approach with an optimum threshold using measure of fuzziness. The optimized version, herein denoted as FCM-t, benefits from an optimum threshold, calculated using measure of fuzziness. This allows the revealing of ambiguous pixels, which are eventually assigned to the appropriate clusters by calculating the rounded average cluster values in the ambiguous pixels neighbourhood. The proposed approach showed significantly better results compared to the traditional Fuzzy C-Means, at the cost of some processing power. By benefiting from the optimum threshold approach, one is able to increase the segmentation performance by approximately three times more than with the traditional FCM.
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Ali, AR., Couceiro, M., Anter, A.M., Hassanien, A.E., Tolba, M.F., Snášel, V. (2014). Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_9
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DOI: https://doi.org/10.1007/978-3-319-08156-4_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08155-7
Online ISBN: 978-3-319-08156-4
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