Paper Info
Title
Improved fuzzy clustering algorithm with non-local information for image segmentation.
Abstract
Fuzzy C-means(FCM) has been adopted to perform image segmentation due to its simplicity and efficiency. Nevertheless it is sensitive to noise and other image artifacts because of not considering spatial information. Up to now, a series of improved FCM algorithms have been proposed, including fuzzy local information C-means clustering algorithm(FLICM). In FLICM, one fuzzy factor is introduced as a fuzzy local similarity measure, which can control the trade-off between noise and details. However, the fuzzy factor in FLICM cannot estimate the damping extent of neighboring pixels accurately, which will result in poor performance in images of high-level noise. Aiming at solving this problem, this paper proposes an improved fuzzy clustering algorithm, which introduces pixel relevance into the fuzzy factor and could estimate the damping extent accurately. As a result, non-local context information can be utilized in the improved algorithm, which can improve the performance in restraining image artifacts. Experimental results on synthetic, medical and natural images show that the proposed algorithm performs better than current improved algorithms.
Year
DOI
Venue
2017
10.1007/s11042-016-3399-x
Multimedia Tools Appl.
Keywords
Field
DocType
Fuzzy clustering,Image segmentation,FLICM,Pixel relevance,Non-local information
Spatial analysis,Data mining,Fuzzy clustering,Pattern recognition,Similarity measure,Computer science,Fuzzy logic,Algorithm,Image segmentation,Artificial intelligence,Pixel,Cluster analysis
Journal
Volume
Issue
ISSN
76
6
1380-7501
Citations 
PageRank 
References 
14
0.50
20
Authors
6
Name
Order
Citations
PageRank
Xiaofeng Zhang1444.84
Yujuan Sun2262.37
Gang Wang334497.03
Qiang Guo462972.75
Caiming Zhang544688.19
beijing chen6634.63