Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 290607, 11 pages
Research Article

A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

1Department of Computer Science and Information Engineering, National Chin-Yi, University of Technology, Taiping 411, Taiwan
2Networks and Communications Group, Advantech Co., Ltd., Neihu 114, Taiwan

Received 25 February 2014; Accepted 4 April 2014; Published 5 May 2014

Academic Editor: Her-Terng Yau

Copyright © 2014 Chuin-Mu Wang and Geng-Cheng Lin. 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.


After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST and -means.