Publications

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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 764–769, 2012. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. Hefny, "Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes", 12th International Conference on Intelligent Systems Design and Applications (ISDA), , Kochi, India, pp. 764 - 769, 2012. Abstract

The main purpose of this paper is to show the use of formal concept analysis (FCA) as data mining approach for mining the common hypermethylated genes between breast cancer subtypes, by extracting formal concepts which representing sets of significant hypermethylated genes for each breast cancer subtypes, then the formal context is built which leading to construct a concept lattice which is composed of formal concepts. This lattice can be used as knowledge discovery and knowledge representation therefore, becoming more interesting for the biologists.

Amin, K. M., M. A. Fattah, A. E. Hassanien, and G. Schaefer, "A binarization algorithm for historical arabic manuscript images using a neutrosophic approach", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 266–270, 2014. Abstract
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Amin, I. I., A. E. Hassanien, H. A. Hefny, and S. K. Kassim, "Visualizing and identifying the DNA methylation markers in breast cancer tumor subtypes", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 161–171, 2014. Abstract
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Amin, I. I., A. E. Hassanien, S. K. Kassim, and H. A. Hefny, "Big DNA Methylation data analysis and visualizing in a common form of breast cancer", Big Data in Complex Systems: Springer International Publishing, pp. 375–392, 2015. Abstract
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Amira El sayed, A. E. Hassanien, S. E. - O. Hanafy, and M. Tolba, "Multi-layer hybrid machine learning techniques for anomalies detection and classification approach. ", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 216-221, 2013, Tunisia, , 4-6 Dec, 2013.
Amira Sayed A. Aziza, S. E. - O. Hanafi, and A. E. Hassanien, " , Comparison of classification techniques applied for network intrusion detection and classification, ", Journal of Applied Logic Available online 14 November 2017, 2017. AbstractWebsite
Anter, A. M., A. E. Hassanien, M. A. A. ELsoud, and M. F. Tolba, "Neutrosophic sets and fuzzy c-means clustering for improving ct liver image segmentation", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 193–203, 2014. Abstract
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Anter, A. M., A. T. Azar, A. E. Hassanien, N. El-Bendary, and M. A. Elsoud, "Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 193–198, 2013. Abstract
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Anter, A. M., A. E. Hassanien, A. T. Azar, and M. A. Elsoud, "Automatic Liver Parenchyma Segmentation System from Abdominal CT Scans using Hybrid Techniques", International Journal of Biomedical Engineering and Technology, vol. 17, issue 2, 2015. AbstractWebsite

In this paper, a multi–layer heuristic approach is introduced to segment liver region from other tissues in multi–slice CT images. Image noise is a principal factor which hampers the visual quality of medical images and can therefore lead to misdiagnosis. To address this issue, we first utilise an algorithm based on median filter to remove noise and enhance the contrast of the CT image. This is followed by performing an adaptive threshold algorithm and morphological operators to preserve the liver structure and remove the fragments of other organs. Then, connected component labelling algorithm was applied to remove false positive regions and focused on liver region. To evaluate the performance of the proposed system, we present tests on different liver CT scans images. The experimental results show that the overall accuracy offered by the employed system is high compared with other related works as well as very fast which segment liver from abdominal CT in less than 0.6 s/slice.

Anter, A. M., M. A. El Souod, A. T. Azar, and A. E. Hassanien, "A hybrid approach to diagnosis of hepatic tumors in computed tomography images", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 1, no. 2: IGI Global, pp. 31–48, 2014. Abstract
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Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), 2013 , Okinawa, Japan. , 5 Nov. , 2013.
Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on: IEEE, pp. 937–941, 2013. Abstract
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Anter, A. M., A. E. Hassanien, M. A. Elsoud, and A. T. Azar, "Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques", International Journal of Biomedical Engineering and Technology, vol. 17, no. 2: Inderscience Publishers, pp. 148–167, 2015. Abstract
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Anter, A. M., M. A. Elsoud, and A. E. Hassanien, "Automatic mammographic parenchyma classification according to BIRADS dictionary", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies. IGI Global, pp. 22–37, 2014. Abstract
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Anter, A. M., A. E. Hassanien, M. A. Elsoud, and T. - H. Kim, "Feature Selection Approach Based on Social Spider Algorithm: Case Study on Abdominal CT Liver Tumor", Advanced Communication and Networking (ACN), 2015 Seventh International Conference on: IEEE, pp. 89–94, 2015. Abstract
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Anter, A. M., A. T. Azar, A. E. Hassanien, N. El-Bendary, and M. A. Elsoud, "Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 193–198, 2013. Abstract
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Anter, A. M., M. A. Elsoud, and A. E. Hassanien, "Automatic liver Parenchyma segmentation from abdominal CT images", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 32–36, 2013. Abstract
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Asad, A. H., Eid Elamry, and A. E. Hassanien, "Retinal vessels segmentation based on water flooding model", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 43–48, 2013. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "Integrated features based on gray-level and hu moment-invariants with ant colony system for retinal blood vessels segmentation", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 60–73, 2012. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "Integrated features based on gray-level and hu moment-invariants with ant colony system for retinal blood vessels segmentation", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 60–73, 2012. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation", International Journal of Rough Sets and Data Analysis, vol. 1, issue 2, pp. 14-31, 2014.
Asad, A. H., Eid Elamry, A. E. Hassanien, and M. F. Tolba, "New global update mechanism of ant colony system for retinal vessel segmentation", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 221–227, 2013. Abstract
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Asad, A. H., Eid Elamry, A. E. Hassanien, and M. Tolba, "New Global Update Mechanism of Ant Colony System for Retinal Vessel Segmentation,", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 222-228, 2013, Tunisia, , 4-6 Dec, 2013.
Asad, A. H., A. T. Azar, and A. E. Hassanien, "A new heuristic function of ant colony system for retinal vessel segmentation", Medical Imaging: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 2063–2081, 2017. Abstract
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