An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm

Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness. However, FCM has the disadvantages of sensitivity to initial values, falling easily into local optimal solution...

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Veröffentlicht in:Multimedia tools and applications Jg. 79; H. 25-26; S. 18839 - 18858
Hauptverfasser: Dhanachandra, Nameirakpam, Chanu, Yambem Jina
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2020
Springer Nature B.V
Schlagworte:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Global optimization
Image enhancement
Image processing
Image segmentation
Inertia
Learning
Literature reviews
Magnetic resonance imaging
Multimedia Information Systems
Noise
Noise reduction
Noise sensitivity
Optimization
Parameters
Particle swarm optimization
Special Purpose and Application-Based Systems
Weight
Image segmentation
Dynamic particle swarm optimization
Clustering
MRI image
Fuzzy c-means
ISSN:1380-7501, 1573-7721
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Zusammenfassung:Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness. However, FCM has the disadvantages of sensitivity to initial values, falling easily into local optimal solution and sensitivity to noise. To tackle these disadvantages, many optimization-based fuzzy clustering methods have been proposed in the literature survey. Particle swarm optimization (PSO) has good global optimization capability and a hybrid of FCM and PSO have improved accuracy over tradition FCM clustering. In this paper, a new image segmentation method based on Dynamic Particle swarm optimization (DPSO) and FCM algorithm along with the noise reduction mechanism is proposed. DPSO has the advantages to change the inertia weight and learning parameters dynamically. It adopts the inertia weight according to the fitness value and learning parameters along with time. The proposed method combines DPSO with FCM, using the advantages of global optimization searching and parallel computing of DPSO to find a superior result of the FCM algorithm. Moreover, a noise reduction mechanism based on the surrounding pixels is used for enhancing the anti-noise ability. The synthetic image and Magnetic Resonance Imaging (MRI) have been used for testing the proposed method by introducing different types of noises and the results show that the proposed algorithm has better performance and less sensitive to noise.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-08699-8