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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 830252, 7 pages
Research Article

A Hybrid Technique for Medical Image Segmentation

1School of Electrical Engineering, University of Ulsan, Building 7, Room No. 308, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea
2School of Electronics and Computer Engineering, Chonnam National University, Building 7, Room No. 506, 77 Yongbong-ro, Buk-gu, Gwangju 500-757, Republic of Korea

Received 22 May 2012; Revised 11 July 2012; Accepted 12 July 2012

Academic Editor: Tai Hoon Kim

Copyright © 2012 Alamgir Nyma 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.


Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.