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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 430516, 13 pages
Research Article

A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation

1Informatics Research Institute, City for Scientific Research and Technological Applications, Borg El Arab, Alexandria, Egypt
2Computers and Control Engineering Department, Faculty of Engineering, University of Tanta, Tanta, Egypt

Received 3 May 2013; Revised 18 August 2013; Accepted 20 August 2013

Academic Editor: Liang Li

Copyright © 2013 Shaheera Rashwan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.