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Fuzzy c-means clustering with non local spatial information for noisy image segmentation

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Abstract

As an effective image segmentation method, the standard fuzzy c-means (FCM) clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy cmeans clustering algorithm with non local spatial information (FCM_NLS). FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance (MR) images, show that FCM_NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, China

    Feng Zhao, Licheng Jiao & Hanqiang Liu

Authors
  1. Feng Zhao
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  2. Licheng Jiao
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  3. Hanqiang Liu
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Correspondence to Feng Zhao.

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Zhao, F., Jiao, L. & Liu, H. Fuzzy c-means clustering with non local spatial information for noisy image segmentation. Front. Comput. Sci. China 5, 45–56 (2011). https://doi.org/10.1007/s11704-010-0393-8

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  • DOI: https://doi.org/10.1007/s11704-010-0393-8

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