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Citation: Rong LAN, Yang LIN. Suppressed Non-local Spatial Intuitionistic Fuzzy C-means Image Segmentation Algorithm[J]. Journal of Electronics and Information Technology, ;2019, 41(6): 1472-1479. doi: 10.11999/JEIT180651 shu

Suppressed Non-local Spatial Intuitionistic Fuzzy C-means Image Segmentation Algorithm

  • Corresponding author: Yang LIN, 784046805@qq.com
  • Received Date: 2018-07-03
    Accepted Date: 2018-12-29
    Available Online: 2019-06-01

Figures(6) / Tables(4)

  • In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
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