A Novel Fuzzy C-Means for Image Segmentation

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 113)


In this paper, we present a novel algorithm for fuzzy segmentation of infrared image data using fuzzy clustering. A conventional FCM assigns the data into group, where the data is nearest to the center of group. Although FCM is populated in image segmentation, it still has the following disadvantages: (1) a conventional FCM algorithm does not consider spatial information for clustering. (2) The algorithm is sensitive to noise. In this paper we present a fuzzy-means algorithm that incorporates spatial information and the prior probability of a pixel neighborhood into the membership function for clustering. The modified FCM has a great improvement for noisy image and infrared image segmentation.


Fuzzy k-means Spatial information Clustering Segmentation 



The authors would like to thank the anonymous reviewers for their detailed review and constructive comments. This work is supported by the National Natural Science Foundation of China (Grant no. 60736046).


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.College of Computer ScienceSichuan UniversityChengduChina
  2. 2.State Key Laboratory of Fundamental Science on Synthetic VisionChengduChina
  3. 3.College of Computer ScienceSichuan Panzhihua UniversityPanzhihuaChina
  4. 4.College of Computer ScienceLeshan Normal UniversityLeshanChina
  5. 5.School of Information Science and TechnologyChengdu UniversityChengduChina

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