Publications

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2012
Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Li, T., H. S. Nguyen, G. Wang, J. W. Grzymala-Busse, R. Janicki, A. - E. Hassanien, and H. Yu, Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings, : Springer, 2012. Abstract
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Li, T., H. S. Nguyen, G. Wang, J. W. Grzymala-Busse, R. Janicki, A. - E. Hassanien, and H. Yu, Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings, : Springer, 2012. Abstract
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zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough sets-based rules generation approach: A hepatitis c virus data sets", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 52–59, 2012. Abstract
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zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough sets-based rules generation approach: A hepatitis c virus data sets", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 52–59, 2012. Abstract
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2011
Heba, T., E. - B. Nashwa, H. AboulElla, B. Yehia, and S. Vaclav, "Retinal Feature-Based Registration Schema", Informatics Engineering and Information Science Communications in Computer and Information Science Volume 252, 2011, pp 26-36 , Ostrava, Czech Republic, 7-9 July, 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.

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.

Taha, H. M., N. El-Bendary, A. E. Hassanien, Y. Badr, and V. Snasel, "Retinal feature-based registration schema", Informatics engineering and information science: Springer Berlin Heidelberg, pp. 26–36, 2011. Abstract
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Taha, H. M., N. El-Bendary, A. E. Hassanien, Y. Badr, and V. Snasel, "Retinal feature-based registration schema", Informatics engineering and information science: Springer Berlin Heidelberg, pp. 26–36, 2011. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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2010
Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Reducing the influence of normalization on data classification", Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on: IEEE, pp. 609–613, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Reducing the influence of normalization on data classification", Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on: IEEE, pp. 609–613, 2010. 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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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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2009
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.

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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