S. F. El-Zoghdy

Work place: College of Computers and Information Technology/Information Technology Department, Taif, 888, KSA

E-mail: s.zoghdy@tu.edu.sa


Research Interests: Computer systems and computational processes, Network Security, Distributed Computing, Parallel Computing


Dr. Said El-Zoghdy received his BSc degree in pure Mathematics and Computer Sciences in 1993, and MSc degree for his work in computer science in 1997, all from the Faculty of Science, Menoufia, Shebin El-Koom, Egypt. In 2004, he received his Ph. D. in Computer Science from the Institute of Information Sciences and Electronics, University of Tsukuba, Japan. From 1994 to 1997, he was a demonstrator of computer science at the Faculty of Science, Menoufia University, Egypt. From December 1997 to March 2000, he was an assistant lecturer of computer science at the same place. From April 2000 to March 2004, he was a Ph. D. candidate at the Institute of Information Sciences and Electronics, University of Tsukuba, Japan, where he was conducting research on aspects of load balancing in distributed and parallel computer systems. From April 2004 to 2007, he worked as a lecturer of computer science, Faculty of Science, Menoufia University, Egypt. Currently, he is working as an assistant professor of computer science at College of Computers and Information Technology, Taif University, Kingdom of Saudi Arabia. His research interests are performance evaluation, load balancing in distributed/parallel computing systems, Grid computing, network security and cryptography.

Author Articles
New Region Growing based on Thresholding Technique Applied to MRI Data

By A. Afifi S. Ghoniemy E.A. Zanaty S. F. El-Zoghdy

DOI: https://doi.org/10.5815/ijcnis.2015.07.08, Pub. Date: 8 Jun. 2015

This paper proposes an optimal region growing threshold for the segmentation of magnetic resonance images (MRIs). The proposed algorithm combines local search procedure with thresholding region growing to achieve better generic seeds and optimal thresholds for region growing method. A procedure is used to detect the best possible seeds from a set of data distributed all over the image as a high accumulator of the histogram. The output seeds are fed to the local search algorithm to extract the best seeds around initial seeds. Optimal thresholds are used to overcome the limitations of region growing algorithm and to select the pixels sequentially in a random walk starting at the seed point. The proposed algorithm works automatically without any predefined parameters. The proposed algorithm is applied to the challenging application “gray matter/white matter” segmentation datasets. The experimental results compared with other segmentation techniques show that the proposed algorithm produces more accurate and stable results.

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