Subscribe Now Subscribe Today
Research Article

Neighboring Weighted Fuzzy C-Means with Kernel Method for Image Segmentation and Its Application

Wenjuan Zhang and Jiayin Kang
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Image segmentation plays a crucial role in many image applications. Fuzzy C-Means (FCM) is a powerful method to segment image and has been applied to many image segmentations successfully. However, on the one hand, traditional FCM algorithm is sensitive to the noise due to the fact that it only accounts for the pixel’s value information, not takes the neighboring pixels’ spatial information into consideration; on the other hand, its performance becomes poor when the input data are nonlinearly. To overcome the above mentioned problems, this study proposed a neighboring weighted FCM associated with kernel method algorithm (KNW-FCM) for image segmentation. The values of the coefficients within neighboring window are adaptively determined by the characteristics of the image itself. Furthermore, the original Euclidean distance in the traditional FCM is replaced by the kernel-induced distance. Additionally, as a real application, we apply the proposed algorithm to segment the microscopic image of harmful algae. Experimental results show that the proposed algorithm achieves better performance compared with other algorithms.

Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

Wenjuan Zhang and Jiayin Kang, 2013. Neighboring Weighted Fuzzy C-Means with Kernel Method for Image Segmentation and Its Application. Journal of Applied Sciences, 13: 2306-2310.

DOI: 10.3923/jas.2013.2306.2310


1:  Gonzalez, R.C. and R.E. Woods, 2002. Digital Image Processing. Pearson Education Inc., Upper Saddle River, NJ., USA.

2:  Gorriz, J.M., J. Ramirez, E.W. Lang and C.G. Puntonet, 2006. Hard C-means clustering for voice activity detection. Speech Communi., 48: 1638-1649.
Direct Link  |  

3:  Sikka, K., N. Sinha, P.K. Singh and A.K. Mishra, 2009. A fully automated algorithm under modified FCM framework for improved brain MR image segmentation. Magnetic Resonance Imaging, 27: 994-1004.
CrossRef  |  PubMed  |  

4:  Liu, J. and M. Xu, 2008. Kernelized fuzzy attribute C-means clustering algorithm. Fuzzy Sets Syst., 159: 2428-2445.
CrossRef  |  Direct Link  |  

5:  Ma, B., H.Y. Qu and H.S. Wong, 2007. Kernel clustering-based discriminant analysis. Pattern Recogn., 40: 324-327.
CrossRef  |  Direct Link  |  

6:  Kaur, P., A.K. Soni and A. Gosain, 2013. A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images. Pattern Recognition Lett., 34: 163-175.
CrossRef  |  Direct Link  |  

7:  Xing, H.J. and B.G. Hu, 2008. An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification. Neurocomputing, 71: 1008-1021.
Direct Link  |  

©  2021 Science Alert. All Rights Reserved