An improved K-means clustering algorithm for fish image segmentation

Fish contour extraction from images is the foundation of many fish image applications such as disease early warning and diagnostics, animal behavior, aquatic product processing, etc. In order to improve the accuracy and stability of fish image segmentation, we propose a new fish images segmentation...

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Veröffentlicht in:Mathematical and computer modelling Jg. 58; H. 3-4; S. 784 - 792
Hauptverfasser: Yao, Hong, Duan, Qingling, Li, Daoliang, Wang, Jianping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2013
Schlagworte:
[formula omitted]-means clustering
algorithms
animal behavior
computer techniques
Contour extraction
decision making
diagnostic techniques
fish
Image segmentation
mathematical models
Mathematical morphology
Image segmentation
Contour extraction
Mathematical morphology
K-means clustering
ISSN:0895-7177, 1872-9479
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Zusammenfassung:Fish contour extraction from images is the foundation of many fish image applications such as disease early warning and diagnostics, animal behavior, aquatic product processing, etc. In order to improve the accuracy and stability of fish image segmentation, we propose a new fish images segmentation method which is the combination of the K-means clustering segmentation algorithm and mathematical morphology. Firstly, the traditional K-means clustering segmentation algorithm has been improved for fish images. The best number of clusters is determined by the number of gray histogram peaks, and the cluster centers data is filtered by comparing the mean with the threshold decided by Otsu. Secondly, the opening and closing operations of mathematical morphology are used to get the contour of the fish body. The experimental results show that the algorithm realized the separation between the fish image and the background in the condition of complex backgrounds. Compared with Otsu and other segmentation algorithms, our algorithm is more accurate and stable.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0895-7177
1872-9479
DOI:10.1016/j.mcm.2012.12.025