Ahmed Hag-ElSafi

Work place: Smart Empower Innovation Labs Inc., Edmonton, Alberta, Canada

E-mail: ahmed.hagelsafi@smartempower.ca


Research Interests: Image Processing, Solid Modeling, Embedded System, Computational Science and Engineering


Rami Zewail received a BSc. and MSc. In Electronics & Communications Engineering, Arab Academy for Science and Technology, Egypt, in 2002 and 2004 respectively. And a PhD degree in Electrical and Computer Engineering, University of Alberta, Canada, in 2010. He has over 15 years of academic and industrial R&D experience in areas of machine learning, digital signal processing, and embedded computing. He has contributed to the scientific community with over 15 publications in areas of image processing, statistical modeling, and embedded computing. Currently, he is the Principal Researcher for Machine Learning at Smart Empower Innovations Labs Inc., Edmonton, Alberta, Canada. Dr. Zewail is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association of Professional Engineers & Geoscientists (APEGA). He served as a reviewer for the Journal of Electronics Imaging, and Journal of Optical Engineering for the SPIE society, USA.

Author Articles
Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation

By Rami Zewail Ahmed Hag-ElSafi

DOI: https://doi.org/10.5815/ijigsp.2017.09.01, Pub. Date: 8 Sep. 2017

Medical experts often examine hundreds of x-ray images searching for salient features that are used to detect pathological abnormalities. Inspired by our understanding of the human visual system, automated salient features detection methods have drawn much attention in the medical imaging research community. However, despite the efforts, detecting robust and stable salient features in medical images continues to constitute a challenging task. This is mainly attributed to the complexity of the anatomical structures of interest which usually undergo a wide range of rigid and non-rigid variations.
In this paper, we present a novel appearance-based salient feature extraction and matching method based on sparse Contourlet-based representation. The multi-scale and directional capabilities of the Contourlets is utilized to extract salient points that are robust to noise, rigid and non-rigid deformations. Moreover, we also include prior knowledge about local appearance of the salient points of the structure of interest. This allows for extraction of robust stable salient points that are most relevant to the anatomical structure of interest. 

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