Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2752

Full Length Research Paper

Colour image segmentation using the second order statistics and a modified fuzzy C-means technique

Rafika Harrabi* and Ezzeddine Ben Braiek
University of Tunis, CEREP Unit, ESSTT, 5 Avenue, Taha Hussein, 1008, Tunis, Tunisia.
Email: [email protected]

  •  Accepted: 23 April 2012
  •  Published: 09 May 2012

Abstract

 

This paper presents a new colour image segmentation method based on Fuzzy C-means technique and the second order statistics. The importance of combining statistical features extracted from the co-occurrence matrix and the standard Fuzzy C-Means clustering algorithm in the segmentation context is studied in this paper, to obtain a more reliable and accurate segmentation results. In the first phase of segmentation, a characterization degree is employed to identify the most significant statistical features extracted from the co-occurrence matrix. In the second phase, the Fuzzy C-means (FCM) algorithm is used to cluster the statistical feature vectors into homogeneous regions. Segmentation results from the proposed method are validated and a comparative study versus existing techniques is presented. The experimental results on medical and synthetic colour images demonstrate the superiority of introducing the second order statistics in the Fuzzy C-Means algorithm for colour image segmentation.

 

Key words: Segmentation, medical colour images, fuzzy logic, fuzzy C-Means, second order statistics, co-occurrence matrix.