Publications

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2003
Hassanien, A. E., and J. M. H. Ali, "Rough sets analysis for reduct generation of breast cancer data", INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, vol. 10: ISCA, pp. 263–270, 2003. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Feature extraction and rule classification algorithm of digital mammography based on rough set theory", Available at www.​ wseas.​ us/​ e-library/​ conferences/​ digest2003/​ papers, pp. 463–104, 2003. Abstract

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Ali, J. M., and A. E. Hassanien, "An Iris Recognition System to Enhance E-Security, Advanced Modeling and Optimization", vol, vol. 5, pp. 93–104, 2003. Abstract

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Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract

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Hassanien, A. E., and J. M. H. Ali, "An Efficient Classification and Image Retrieval Algorithm Based on Rough Set Theory", Proceedings of the 5th International Conference on Enterprise Information Systems, , Angers, France, April 22-26 , 2003.
2004
Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassanien, A. E., and J. M. Ali, "Digital mammogram segmentation algorithm using pulse coupled neural networks", Image and Graphics (ICIG'04), Third International Conference on: IEEE, pp. 92–95, 2004. Abstract
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Hassanien, A. E., and J. M. Ali, "Digital mammogram segmentation algorithm using pulse coupled neural networks", Image and Graphics (ICIG'04), Third International Conference on: IEEE, pp. 92–95, 2004. Abstract
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Helal, M. A., T. El-Arief, A. E. Hassanien, and N. El-Haggar, "An Efficient Texture Segmentation Algorithm for Isolating Iris Patterns Based on Wavelet Theory", PATTERN RECOGNITION AND IMAGE ANALYSIS C/C OF RASPOZNAVANIYE OBRAZOV I ANALIZ IZOBRAZHENII, vol. 14, no. 1: NAUKA/INTERPERIODICA PUBLISHING, pp. 97–103, 2004. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Hassanien, A. - E., "Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer", Journal of the American Society for information Science and Technology, vol. 55, no. 11: Wiley Subscription Services, Inc., A Wiley Company, pp. 954–962, 2004. Abstract
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Hassanien, A. - E., "Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer", Journal of the American Society for information Science and Technology, vol. 55, no. 11: Wiley Subscription Services, Inc., A Wiley Company, pp. 954–962, 2004. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough set approach for generation of classification rules of breast cancer data", Informatica, vol. 15, no. 1: Institute of Mathematics and Informatics, pp. 23–38, 2004. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough set approach for generation of classification rules of breast cancer data", Informatica, vol. 15, no. 1: Institute of Mathematics and Informatics, pp. 23–38, 2004. Abstract
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Hassanien, A. E., "Rough set approach for attribute reduction and rule generation: A case of patients with suspected breast cancer", Journal of the American Society for Information Science and Technology , vol. 55, issue 11, pp. 954-962 , 2004. Abstract
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Rough set theory is a relatively new intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is being used for the extraction of rules from databases. In this paper, we present a rough set approach to attribute reduction and generation of classification rules from a set of medical datasets. For this purpose, we first introduce a rough set reduction technique to find all reducts of the data that contain the minimal subset of attributes associated with a class label for classification. To evaluate the validity of the rules based on the approximation quality of the attributes, we introduce a statistical test to evaluate the significance of the rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated. In addition, the rough set classification accuracy is also compared to the well-known ID3 classifier algorithm. The study showed that the theory of rough sets is a useful tool for inductive learning and a valuable aid for building expert systems.

Ali, J., and A. E. Hassanien, "Rough Set Approach for Generation of Classification Rules of Breast Cancer Data.", Informatica, vol. 15, issue 1, pp. 23-38, 2004. Abstract

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases.

In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. 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. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.

Hassanien, A. E., J. M. H. Ali, and H. Nobuhara, "Detection of Spiculated Masses in Mammograms Based on Fuzzy Image Processing.", Artificial Intelligence and Soft Computing - ICAISC 2004, 7th International Conference, , Zakopane, Poland, Volume 3070/2004, 1002-1007, June 7-11, 2004. Abstract

This paper presents an efficient technique for the detection of spiculated massesin the digitized mammogram to assist the attending radiologist in making his decisions. The presented technique consists of two stages, enhancement of spiculation masses followed by the segmentation process. Fuzzy Histogram Hyperbolization (FHH) algorithm is first used to improve the quality of the digitized mammogram images. The Fuzzy C-Mean (FCM) algorithm is then applied to the preprocessed image to initialize the segmentation. Four measures of quantifying enhancement have been developed in this work. Each measure is based on the statistical information obtained from the labelled region of interest and a border area surrounding it. The methodology is based on the assumption that target and background areas are accurately specified. We have tested the algorithms on digitized mammograms obtained from the Digital Databases for Mammographic Image Analysis Society (MIAS).

2005
Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 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. 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.
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