Colon Cell Image Segmentation Based on Level Set and Kernel-Based Fuzzy Clustering

  • Amin Gharipour
  • Alan Wee-Chung Liew
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


This paper presents an integration framework for image segmentation. The proposed method is based on Fuzzy c-means clustering (FCM) and level set method. In this framework, firstly Chan and Vese’s level set method (CV) and Bayes classifier based on mixture of density models are utilized to find a prior membership value for each pixel. Then, a supervised kernel based fuzzy c-means clustering (SKFCM) algorithm assisted by prior membership values is developed for final segmentation.

The performance of our approach has been evaluated using high-throughput fluorescence microscopy colon cancer cell images, which are commonly used for the study of many normal and neoplastic procedures. The experimental results show the superiority of the proposed clustering algorithm in comparison with several existing techniques.


Image segmentation fuzzy c-means kernel based fuzzy c-means supervised kernel based fuzzy c-means Chan-Vese method Bayes classifier 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amin Gharipour
    • 1
  • Alan Wee-Chung Liew
    • 1
  1. 1.School of Information and Communication TechnologyGriffith UniversityAustralia

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