Publications

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Conference Paper
Ahmed, Z., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, 8-10 Dec. , 2012. Abstract3220052.pdf

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Rule Generation Approach for Granular Computing Using Rough Mereology", International Conference on Computer Research and Development, 5th (ICCRD 2013): ASME Press, 2013. Abstract
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Book Chapter
Hore, S., T. Bhattacharya, N. Dey, A. E. Hassanien, A. Banerjee, and S. R. B. Chaudhuri, "A Real Time Dactylology Based Feature Extractrion for Selective Image Encryption and Artificial Neural Network", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 203–226, 2016. Abstract
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Reham Gharbia, Ali Hassan El Baz, A. E. Hassanien, and V. Snasel, "Region-based Image Fusion Approach of Panchromatic and Multi-spectral Images", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 535–545, 2015. Abstract
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Mohamed Tahoun, A. E. Hassanien, and R. Reulke, "Registration of Optical and Radar Satellite Images Using Local Features and Non-rigid Geometric Transformations", Surface Models for Geosciences: Springer International Publishing, pp. 249–261, 2015. Abstract
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Sahba, F., H. R. Tizhoosh, and M. M. A. Salama, "Reinforced Medical Image Segmentation", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 327–345, 2009. Abstract
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Reham Gharbia, S. A. Ahmed, and A. E. Hassanien, "Remote Sensing Image Registration Based on Particle Swarm Optimization and Mutual Information", Information Systems Design and Intelligent Applications: Springer India, pp. 399–408, 2015. Abstract
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Heba M. Taha, N. El-Bendary, A. E. Hassanien, Y. Badr, and V. Snasel, "Retinal Feature-Based Registration Schema", Informatics Engineering and Information Science, Berlin Heidelberg, pp. 26-36, Communications in Computer and Information Science - Springer , 2011. Abstract

This paper presents a feature-based retinal image registration schema. A structural feature, namely, bifurcation structure, has been used for the proposed feature-based registration schema. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. The proposed schema is composed of five fundamental phases, namely, input retinal images pre-processing, vascular network detection, noise removal, bifurcation points detection in vascular networks, and bifurcation points matching in pairs of retinal images. The effectiveness of the proposed schema is demonstrated by the experiments with 12 pairs retinal images collected from clinical patients. The registration is carried out through optimizing a certain similarity function, namely, normalized correlation of images. It has been observed that the proposed schema has achieved good performance accuracy.

Hassan, G., and A. E. Hassanien, "A Review of Vessel Segmentation Methodologies and Algorithms: Comprehensive Review, ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI USA, 2017. Abstract

“Prevention is better than cure”, true statement which all of us neglect. One of the most reasons which cause speedy recovery from any diseases is to discover it in advanced stages. From here come the importance of computer systems which preserve time and achieve accurate results in knowing the diseases and its first symptoms .One of these systems is retinal image analysis system which considered as a key role and the first step of Computer Aided Diagnosis Systems (CAD). In addition to monitor the patient health status under different treatment methods to ensure How it effects on the disease.. In this chapter the authors examine most of approaches that are used for vessel segmentation for retinal images, and a review of techniques is presented comparing between their quality and accessibility, analyzing and catgrizing them. This chapter gives a description and highlights the key points and the performance measures of each one.

Adham Mohamed, M. M. M. Fouad, Esraa Elhariri, N. El-Bendary, H. M. Zawbaa, Mohamed Tahoun, and A. E. Hassanien, "RoadMonitor: an intelligent road surface condition monitoring system", Intelligent Systems' 2014: Springer International Publishing, pp. 377–387, 2015. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms", Intelligent Systems' 2014: Springer International Publishing, pp. 731–741, 2015. Abstract
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Hassanien, A. E., H. Al-Qaheri, and A. Abraham, "Rough Hybrid Scheme", Rough Fuzzy Image Analysis: Foundations and Methodologies: CRC Press, pp. 5–1, 2010. Abstract
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Waleed Yamany, N. El-Bendary, H. M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 259–270, 2014. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Kacprzyk, J., J. F. Peters, A. Abraham, and A. E. Hassanien, "Rough Sets in Medical Imaging", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 47–87, 2009. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and J. Kacprzyk, "Rough Sets in Medical Imaging: Foundations and Trends", Computational Intelligence in Medical Imaging: Techniques and Applications, USA, CRC, 2009. Abstract

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AboulElla, H., A. Abraham, J. F. Peters, and G. Schaefer, "Rough Sets in Medical Informatics Applications", Applications of Soft Computing - Advances in Intelligent and Soft Computing, pp 23-30, Berlin , Springer Berlin Heidelberg (ISSN: 978-3-540-89618-0), 2009. Abstract

Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative ‘youth’ compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.

Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "Rough sets in medical informatics applications", Applications of soft computing: Springer Berlin Heidelberg, pp. 23–30, 2009. Abstract
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Book
Hassanien, A. E., Rough Computing: Theories, Technologies, and Applications, , USA, IGI Global USA, 2008. AbstractWebsite

Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.

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