Abstract
Image segmentation is a fundamental component in image processing, object tracking, and clinical research. Many scholars have proposed various techniques; however, fuzzy c-means (FCM) algorithms have been proven effectively. In order to retain more image details as well as canceling image noise, a novel clustering algorithm based on the local coefficient of variation is presented in this paper, termed as LCVFCM. The major characteristics of LCVFCM are: firstly, the local gray similarity matrix is modified based on the differences between median pixel and neighborhood pixels to reduce the impact of noise on image pixels, and secondly, a new fuzzy factor is established by introducing local coefficient of variation to improve the performance of LCVFCM. The corresponding experiments with a large number of synthetic and real images prove our model achieves excellent segmentation capability and has stronger anti-noise ability, as compared with other segmentation methods.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 51275272, 51605253), Zhejiang Provincial Natural Science Foundation of China (No. LQ17C160001, LQ18F010007), Provincial Public-benefit Technology Application Research of Zhejiang (No. 2017C37082, 2016C37058, 2016C31127), Key Laboratory of Air-driven Equipment Technology of Zhejiang Province (No.2018E10011).
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Jiang, X., Zhang, D., Lin, H., Li, X., Xiao, J., Li, B. (2019). A Robust Image Segmentation Approach Using Fuzzy C-Means Clustering with Local Coefficient of Variation. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_12
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DOI: https://doi.org/10.1007/978-981-10-8944-2_12
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