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

Rong LAN; Yang LIN

doi:10.11999/JEIT180651

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1472-1479

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

Citation:

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

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

doi: 10.11999/JEIT180651

Rong LAN,
Yang LIN^,

1.
School of Telecommunications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an 710121, China
3.
International Joint Research Center for Wireless Communication and Information Processing, Shannxi Province Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61571361, 61671377), Shaanxi Provincial Department of Education Scientific Research Plan (16JK1709), New Star Team of Xi’an University of Posts and Telecommunications (xyt2016-01)

Received Date: 2018-07-03
Rev Recd Date: 2018-12-29

Available Online: 2019-01-07

Publish Date: 2019-06-01

Abstract

Abstract

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.
- Image segmentation,
- Fuzzy C-Means (FCM),
- Intuitionistic fuzzy set,
- Non-local spatial information,
- Hesitation degree

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References(22)

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