Publications

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Miscellaneous
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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Chakraborty, M., A. - E. Hassanien, D. Slezak, and W. Zhu, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, : Springer-Verlag Berlin Heidelberg, 2009. Abstract
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Chakraborty, M., A. - E. Hassanien, D. Slezak, and W. Zhu, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, : Springer-Verlag Berlin Heidelberg, 2009. Abstract
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Journal Article
Azar, A. T., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, "Random Forest Classifier for Lymph Diseases", Computer Methods and Programs in Biomedicine , Elsevier 2013 , vol. 113, issue 2, pp. 465-473 , 2014. Website
Azar, A. T., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, "A random forest classifier for lymph diseases", Computer methods and programs in biomedicine, vol. 113, no. 2: Elsevier, pp. 465–473, 2014. Abstract
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Hussein, H. K., A. - E. Hassanien, and M. Nakajima, "Regular Section-PAPERS-Image Processing, Computer Graphics and Pattern Recognition-Escape-Time Modified Algorithm for Generating Fractal Images Based on Petri Net Reachability", IEICE Transactions on Information and Systems, vol. 82, no. 7: Tokyo, Japan: Institute of Electronics, Information and Communication Engineers, c1992-, pp. 1101–1108, 1999. Abstract
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Hassanien, A. - E., and M. Nakajima, "Regular Section-PAPERS-Image Processing, Computer Graphics and Pattern Recognition-Feature-Specification Algorithm Based on Snake Model for Facial Image Morphing", IEICE Transactions on Information and Systems, vol. 82, no. 2: Tokyo, Japan: Institute of Electronics, Information and Communication Engineers, c1992-, pp. 439–446, 1999. Abstract
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Hassanien, A. E., Eid Emary, and H. M. Zawbaa, "Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search", Journal of Visual Communication and Image Representation, vol. 31: Academic Press, pp. 186–196, 2015. Abstract
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Hassaniena, A. E., E. Emarya, and H. M. Zawbaa, "Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search.", J. Visual Communication and Image Representation , vol. 31, pp. 186-196 , 2015. AbstractWebsite

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis and surgery planning of retinopathy. Despite the high resolution of photographs in fundus photography, the contrast between the blood vessels and retinal background tends to be poor. Furthermore, pathological changes of the retinal vessel tree can be observed in a variety of diseases such as diabetes and glaucoma. Vessels with small diameters are much liable to effects of diseases and imaging problems. In this paper, an automated retinal blood vessels segmentation approach based on two levels optimization principles is proposed. The proposed approach makes use of the artificial bee colony optimization in conjunction with fuzzy cluster compactness fitness function with partial belongness in the first level to find coarse vessels. The dependency on the vessel reflectance is problematic as the confusion with background and vessel distortions especially for thin vessels, so we made use of a second level of optimization. In the second level of optimization, pattern search is further used to enhance the segmentation results using shape description as a complementary feature. Thinness ratio is used as a fitness function for the pattern search optimization. The pattern search is a powerful tool for local search while artificial bee colony is a global search with high convergence speed. The proposed retinal blood vessels segmentation approach is tested on two publicly available databases DRIVE and STARE of retinal images. The results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of sensitivity, specificity and accuracy.

Hassan, G., N. El-Bendary, A. E. Hassanien, A. Fahmy, V. Snasel, and others, "Retinal blood vessel segmentation approach based on mathematical morphology", Procedia Computer Science, vol. 65: Elsevier, pp. 612–622, 2015. 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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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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Hassan, G., and A. E. Hassanien, "Retinal fundus vasculature multilevel segmentation using whale optimization algorithm", Signal, Image and Video Processing, vol. 12, issue 2, pp. 263–270, 2018. AbstractWebsite

The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multilevel thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the n−1 optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.

Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. 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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Hassanien, A. E., M. Salama, J. Platos, and V. Snasel, "Rough local transfer function for cardiac disorders detection using heart sounds ", Logic Journal of the IGPL, Oxford, vol. (in press), 2014. Website
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