Publications

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2001
Hassanien, A. E., I. El Henawy, and H. S. Own, "Multiresolution image denoising based on wavelet transform", International Symposium on Optical Science and Technology: International Society for Optics and Photonics, pp. 383–394, 2001. Abstract
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Hassanien, A. E., I. El Henawy, and H. S. Own, "Multiresolution image denoising based on wavelet transform", International Symposium on Optical Science and Technology: International Society for Optics and Photonics, pp. 383–394, 2001. Abstract
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Hassanien, A. E., I. El Henawy, and H. Own, "Multiresolution image denosing based on wavelet transform", Machine Graphics and Vision, vol. 10, no. 2, pp. 221–230, 2001. Abstract
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2002
Own, H. S., and A. E. Hassanien, "Multiresolution image registration algorithm in wavelet transform domain", Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on, vol. 2: IEEE, pp. 889–892, 2002. Abstract
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2005
Own, H., and A. Hassanien, "Q-shift Complex Wavelet-based Image Registration Algorithm", Computer Recognition Systems: Springer Berlin/Heidelberg, pp. 403–410, 2005. Abstract
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Own, H., and A. Hassanien, "Q-shift Complex Wavelet-based Image Registration Algorithm", Computer Recognition Systems: Springer Berlin/Heidelberg, pp. 403–410, 2005. Abstract
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Own, H., and A. E. Hassanien, "Automatic Image Registration Algorithm Based on Multiresolution Local Contrast Entropy and Mutual Information", International Journal of Computers and Their Applications, vol. 12, issue 1, pp. 9-15, 2005.
Own, H., and A. E. Hassanien, "Q-shift Complex Wavelet-based Image Registration Algorithm", Proceedings of the 4th International Conference on Computer Recognition Systems, CORES'05, pp. 403-410, Rydzyna Castle, Poland, May 22-25,, 2005. Abstract

This paper presents an efficient image registration technique using the Q-shift complex wavelet transform (Q-shift CWT). It is chosen for its key advantages compared to other wavelet transforms; such as shift invariance, directional selectivity, perfect reconstruction, limited redundancy and efficient computation. The experiments show that the proposed algorithm improves the computational efficiency and yields robust and consistent image registration compared with the classical wavelet transform.

2006
Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
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2007
Ella Hassanien, A., M. E. Abdelhafez, and H. S. Own, "Rough set analysis in knowledge discovery: a case of Kuwaiti diabetic children patients", Advances in Fuzzy Systems, pp. 1–13, 2007. Abstract
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Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
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Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
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2008
Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough sets data analysis in knowledge discovery: A case of kuwaiti diabetic children patients", Advances in fuzzy Systems, vol. 8: Hindawi Publishing Corp., pp. 2, 2008. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough sets data analysis in knowledge discovery: A case of kuwaiti diabetic children patients", Advances in fuzzy Systems, vol. 8: Hindawi Publishing Corp., pp. 2, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients", Advances in Fuzzy Systems,, vol. 2008, issue 1, pp. 13, 2008. AbstractWebsite

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

Own, H. S., and A. E. Hassanien, "Rough Wavelet Hybrid Image Classification Scheme", Journal of Convergence Information Technology, vol. 3, issue 4, pp. 65-75, 2008. AbstractWebsite

This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy

2009
Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Tourism