Interval Type-2 enhanced possibilistic fuzzy C-means noisy image segmentation algorithm amalgamating weighted local information-Reference-Cited by-同舟云学术

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

Published:2024-11 Issue: Volume:137 Page:109135
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Huang Chengquan^ORCID,Lei Huan,Chen Yang,Cai Jianghai,Qin Xiaosu,Peng Jialei,Zhou Lihua,Zheng Lan

Publisher

Elsevier BV

Reference56 articles.

1. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data;Ahmed;IEEE Trans. Med. Imag.,2002

2. Contour detection and hierarchical image segmentation;Arbeláez;IEEE Trans. Pattern Anal. Mach. Intell.,2011

3. Generalized possibilistic fuzzy C-means with novel cluster validity indices for clustering noisy data;Askari;Appl. Soft Comput.,2017

4. FCM: the fuzzy C-means clustering algorithm;Bezdek;Comput. Geosci.,1984

5. Validation of fuzzy and possibilistic clustering results;Cebeci,2017

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