Open AccessImage segmentation algorithm based on neutrosophic fuzzy clustering with non‐local information
Author(s) -
Wen Jinyu,
Xuan Shibin,
Li Yuqi,
Peng Qihui,
Gao Qing
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5949
Subject(s) - cluster analysis , pattern recognition (psychology) , fuzzy clustering , artificial intelligence , image segmentation , fuzzy logic , flame clustering , computer science , segmentation , segmentation based object categorization , canopy clustering algorithm , cure data clustering algorithm , correlation clustering , scale space segmentation , mathematics , data mining , algorithm
To improve the boundary processing ability and anti‐noise performance of image segmentation algorithma neutrosophic fuzzy clustering algorithm based on non‐local information is proposed here. Initially, the proposed approach uses the data distribution of deterministic subset to determine the clustering centre of the fuzzy subset. Besides, the fuzzy non‐local pixel correlation is introduced into the neutrosophic fuzzy mean clustering algorithm. The experimental results on synthetic images, medical images and natural images show that the proposed method is more robust and more accurate than the existing clustering segmentation methods.