Wenjuan Zhang
School of Computer Engineering, Huaihai Institute of Technology, Lianyungang, 222005, China
Jiayin Kang
School of Electronics Engineering, Huaihai Institute of Technology, Lianyungang, 222005, China
ABSTRACT
Segmentation of microscopic image of harmful algae is a crucial step in harmful algae classification and recognition system. FCM is a powerful tool for image segmentation and has been applied into various applications successfully. However, traditional FCM algorithm is sensitive to the noise due to the fact that it only accounts for the pixels value information, not takes the neighboring pixels spatial information into consideration. Furthermore, the performance of traditional FCM becomes poor when the input data are nonlinearly. In order to overcome the shortcomings of traditional FCM, this study presented a modified Fuzzy C-means (FCM) algorithm for image segmentation. The proposed method is realized by modifying the objective function in the Szilagyis algorithm via introducing histogram-based weight. Furthermore, the kernel method was introduced to replace the original Euclidean distance in the Szilagyis algorithm. Experimental results on microscopic images of harmful algae show that the proposed approach has better performance compared with other methods.
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How to cite this article
Wenjuan Zhang and Jiayin Kang, 2013. A Fast Kernel-induced Fuzzy C-means Algorithm and its Application to
Segmentation of Microscopic Image of Harmful Algae. Journal of Applied Sciences, 13: 2574-2578.
DOI: 10.3923/jas.2013.2574.2578
URL: https://scialert.net/abstract/?doi=jas.2013.2574.2578
DOI: 10.3923/jas.2013.2574.2578
URL: https://scialert.net/abstract/?doi=jas.2013.2574.2578
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