Interval Type-2 enhanced possibilistic fuzzy C-means noisy image segmentation algorithm amalgamating weighted local information

The fuzzy clustering algorithms based on interval type-2 are effective methods for data clustering and image segmentation with some potential advantages in dealing with higher-order uncertainty. However, the fuzzy clustering algorithms based on interval type-2 have limited accuracy in noisy image se...

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Vydáno v:	Engineering applications of artificial intelligence Ročník 137; s. 109135
Hlavní autoři:	Huang, Chengquan, Lei, Huan, Chen, Yang, Cai, Jianghai, Qin, Xiaosu, Peng, Jialei, Zhou, Lihua, Zheng, Lan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.11.2024
Témata:	Interval type-2 possibilistic fuzzy C-Means Local features Noisy image Weighted local information Weighted sum function Weighted local information Interval type-2 possibilistic fuzzy C-Means Local features Weighted sum function Noisy image
ISSN:	0952-1976
On-line přístup:	Získat plný text
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Shrnutí:	The fuzzy clustering algorithms based on interval type-2 are effective methods for data clustering and image segmentation with some potential advantages in dealing with higher-order uncertainty. However, the fuzzy clustering algorithms based on interval type-2 have limited accuracy in noisy image segmentation. Therefore, we propose a new noisy image segmentation algorithm based on weighted local information for interval type-2 enhanced possibilistic fuzzy C-means clustering. Firstly, a new possibilistic fuzzy weighted local information factor, which amalgamates the mutually guided image filtering, is devised to control the relationship between mutually guided image filtering and the original image utilizing the absolute difference image between the original image and mutually guided image filtering. Additionally, two weighted sum functions are also introduced into the new possibilistic fuzzy weighted local information factor to calculate the grayscale differences between the current pixel and its neighborhood pixels. Secondly, the distance of the objective function is corrected using mutually guided image filtering to obtain the local features and global information of the image. Finally, this paper introduces the new possibilistic fuzzy weighted local information factor into the interval type-2 possibilistic fuzzy C-means and proposes a new interval type-2 enhanced possibilistic fuzzy C-means clustering algorithm with local information and mutually guided image filtering constraints to improve noisy image segmentation results. Through extensive experiments on noisy synthetic images and noisy real images, the proposed algorithm can achieve higher performance and more accurate segmentation results compared with several existing algorithms.
ISSN:	0952-1976
DOI:	10.1016/j.engappai.2024.109135