T. E. Taha

Work place: Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, 32952, Menouf, Egypt.

E-mail: taha_117@hotmail.com

Website:

Research Interests: Computer systems and computational processes, Medical Informatics, Bioinformatics

Biography

Taha E. Taha was born in Tanta, Egypt, on October 11, 1946. He received the B.Sc. degree(with distinction) in communication engineering from Menoufia University, Egypt, in June 1969, the M.Sc. degree in communication engineering from Helwan University, Egypt, in April 1978, and the Ph.D. degree (very honorable) in electronic engineering from the National Polytechnic Institute, Toulouse, France, in June 1985. From September 1969 to July 1978, he was a Demonstrator, in July 1978, he was an Assistant Lecturer, in November1985, he was a Lecturer, in February1990, he was an Assistant Professor, in September 1995,he was named Professor, all in the Faculty of Electronic Engineering, Menoufia University, Communication Department,. He was appointed Vice Dean from February 2002 to October 2005, and Head of the Communication Department, from November 2005 to July 2007. At present, he is an Emeritus Professor at the same department. His main research interests are surface acoustic wave devices, optical devices, superconductor devices, medical applications of ultrasound, and bioinformatics.

Author Articles
Comparative Study of Different Denoising Filters for Speckle Noise Reduction in Ultrasonic B-Mode Images

By Amira A. Mahmoud S. EL Rabaie T. E. Taha O. Zahran F. E. Abd El-Samie W. Al-Nauimy

DOI: https://doi.org/10.5815/ijigsp.2013.02.01, Pub. Date: 8 Feb. 2013

Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categories, spatial filtering algorithms and transform domain based algorithms. In this paper a comparative study of different denoising filters for speckle noise reduction in ultrasonic b-mode images based on calculating the Peak Signal to Noise Ratio (PSNR) value as a metric is presented. The quantitative results of comparison are tabulated by calculating the PSNR of the output image.

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