Publications

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Ho, S. H., A. E. Hassanien, N. Van Du, Q. Salih, and H. Sooi, "FUZZY C-MEANS CLUSTERING WITH ADJUSTABLE FEATURE WEIGHTING DISTRIBUTION FOR BRAIN MRI VENTRICLES SEGMENTATION Kai Xiao1", Update, vol. 15, pp. 1, 2001. Abstract
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Jui, S. - L., C. Lin, Haibing Guan, A. Abraham, A. E. Hassanien, and K. Xiao, "Fuzzy c-means with wavelet filtration for MR image segmentation", Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on: IEEE, pp. 12–16, 2014. Abstract
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Ayeldeen, H., A. E. Hassanien, and A. Fahmy, "Fuzzy clustering and categorization of text documents", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, 4-6 Dec. pp. 263-267, 2013, Tunisia, , 4-6 Dec, 2013.
Ayeldeen, H., A. E. Hassanien, and A. Fahmy, "Fuzzy clustering and categorization of text documents", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, 4-6 Dec. pp. 263-267, 2013, Tunisia, , 4-6 Dec, 2013.
Ayeldeen, H., A. E. Hassanien, and A. A. Fahmy, "Fuzzy clustering and categorization of text documents", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 262–266, 2013. Abstract
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Xia, K., J. Li, H. G. Shuangjiu Xiao, F. Fang, and A. E. Hassanien, "Fuzzy Clustering with Multi-resolution Bilateral Filtering for Medical Image Segmentation", International Journal of Fuzzy System Applications (IJFSA), vol. 3, issue 4, 2013. fuzzy_clustering_with_multi-resolution_bilateral_filtering_for_medical_image_segmentation-revision.pdf
Xiao, K., J. Li, S. Xiao, Haibing Guan, F. Fang, and A. E. Hassanien, "Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation", International Journal of Fuzzy System Applications (IJFSA), vol. 3, no. 4: IGI Global, pp. 47–59, 2013. Abstract
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Hassanien, A. E., "Fuzzy rough sets hybrid scheme for breast cancer detection", Image and Vision Computing, vol. 25, issue 2, pp. 172–183, 2007. AbstractWebsite

This paper introduces a hybrid scheme that combines the advantages of fuzzy sets and rough sets in conjunction with statistical feature extraction techniques. An application of breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The introduced scheme starts with fuzzy image processing as pre-processing techniques to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest. A subsequently extract features from the segmented regions of the interested regions using the gray-level co-occurrence matrix is presented. Rough sets approach for generation of all reducts that contains minimal number of attributes and rules is introduced. Finally, these rules can then be passed to a classifier for discrimination for different regions of interest to test whether they are cancer or non-cancer. To measure the similarity, a new rough set distance function is presented. The experimental results show that the hybrid scheme applied in this study perform well reaching over 98% in overall accuracy with minimal number of generated rules. (This paper was not presented at any IFAC meeting).

Hassanien, A. E., "Fuzzy rough sets hybrid scheme for breast cancer detection", Image and vision computing, vol. 25, no. 2: Elsevier, pp. 172–183, 2007. Abstract
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Hassanien, A. E., "Fuzzy rough sets hybrid scheme for breast cancer detection", Image and vision computing, vol. 25, no. 2: Elsevier, pp. 172–183, 2007. Abstract
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Hassanien, A. E., and J. M. H. Ali, "A Fuzzy-Rule based Algorithm for Contrast Enhancement of Mammograms Breast Masses", Wseas Transaction, 2014. AbstractWebsite

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