Journal of South China University of Technology (Natural Science Edition) ›› 2014, Vol. 42 ›› Issue (3): 1-7.doi: 10.3969/j.issn.1000-565X.2014.03.001

• Electronics, Communication & Automation Technology •     Next Articles

An Automatic Image Segmentation Algorithm Based on Improved FCM

Zhou Xiao- ming1,2 Li Zhao1 Liu Xiong- ying1   

  1. 1. School of Sciences,South China University of Technology,Guangzhou 510640,Guangdong,China;2. State Key Laboratory of Subtropical Building Science,South China University of Techndogy,Guangzhou 510640,Guangdong,China
  • Received:2013-11-14 Revised:2013-12-29 Online:2014-03-25 Published:2014-02-19
  • Contact: 周晓明(1963-)，男，教授，主要从事电磁场与信号处理研究． E-mail:zhouxm@scut.edu.cn
  • About author:周晓明(1963-)，男，教授，主要从事电磁场与信号处理研究．
  • Supported by:

    国家自然科学基金资助项目(61372008);亚热带建筑科学国家重点实验室开放课题(2013KA02)

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Abstract:

The clustering number needs to be determined artificially when image segmentation using FCM is per-formed.In order to solve this problem,an improved algorithm based on FCM is proposed.In this algorithm,first,a sub- image decomposition of the ori- ginal image is conducted on the basis of quad- tree structure,by which theoriginal image is divided into 2 ×2 sub- images equally and each sub- image is divided into 2 ×2 sub- images equallyagain until the sub- image satiates certain conditions.Then,the sub- image is segmented by using FCM with theclustering number 2.Moreover,region merging is carried out according to the region area and the Bhattacharyyadistance of two adjacent regions’histogram,with the segmentation results being finally obtained without determi-ning the clustering number directly.Experimental results indicate that the proposed algorithm possesses good seg-mentation effectiveness,and that,to some extent,the computation complexity is reduced,because the segmenta-tion using FCM helps reduce the number of samples for each clustering.

Key words: FCM, image segmentation, information entropy, clustering number

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