Abstract
Fuzzy c-means (FCM) is a widely used unsupervised pattern recognition method for medical image segmentation. The conventional FCM algorithm and some existing variants are either sensitive to noise or prone to loss of details. This paper presents a modified FCM algorithm that incorporates bilateral filtering for medical image segmentation. The experimental results and quantitative analyses suggest that, compared to the conventional FCM, the proposed method improves clustering performance with higher standard of noise-resistance and detail-preservation.
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Liu, Y., Xiao, K., Liang, A., Guan, H. (2012). Fuzzy C-means Clustering with Bilateral Filtering for Medical Image Segmentation. 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_20
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DOI: https://doi.org/10.1007/978-3-642-28942-2_20
Publisher Name: Springer, Berlin, Heidelberg
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