Improved fuzzy clustering algorithm with non-local information for image segmentation

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 l...

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Veröffentlicht in:Multimedia tools and applications Jg. 76; H. 6; S. 7869 - 7895
Hauptverfasser: Zhang, Xiaofeng, Sun, Yujuan, Wang, Gang, Guo, Qiang, Zhang, Caiming, Chen, Beijing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2017
Springer Nature B.V
Schlagworte:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Damping
Data Structures and Information Theory
Image segmentation
Multimedia Information Systems
Noise
Noise control
Noise sensitivity
Performance enhancement
Pixels
Spatial data
Special Purpose and Application-Based Systems
Non-local information
Image segmentation
Fuzzy clustering
FLICM
Pixel relevance
ISSN:1380-7501, 1573-7721
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-016-3399-x