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The Scientific World Journal
Volume 2014, Article ID 964870, 13 pages
Research Article

A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection

Department of Computer Engineering, Faculty of Engineering, Firat University, 23119 Elazig, Turkey

Received 3 December 2013; Accepted 20 February 2014; Published 23 March 2014

Academic Editors: S. Balochian and V. Bhatnagar

Copyright © 2014 Burhan Ergen. 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.


This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.