Publications

Export 1250 results:
Sort by: Author Title Type [ Year  (Desc)]
2007
Xiao, K., S. H. Ho, and A. E. Hassanien, "Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction", Malaysian Journal of Computer Science, vol. 20, no. 2: Faculty of Computer Science and Information Technology, pp. 115, 2007. Abstract
n/a
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
n/a
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
n/a
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
n/a
Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
n/a
Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
n/a
2006
Hassanien, A. E., "A Copyright Protection using Watermarking Algorithm", Informatica, vol. 17 , issue 2, pp. 187-198, 2006. AbstractWebsite

In this paper, a digital watermarking algorithm for copyright protection based on the concept of embed digital watermark and modifying frequency coefficients in discrete wavelet transform (DWT) domain is presented. We embed the watermark into the detail wavelet coefficients of the original image with the use of a key. This key is randomly generated and is used to select the exact locations in the wavelet domain in which to embed the watermark. The corresponding watermark detection algorithm is presented. A new metric that measure the objective quality of the image based on the detected watermark bit is introduced, which the original unmarked image is not required for watermark extraction. The performance of the proposed watermarking algorithm is robust to variety of signal distortions, such a JPEG, image cropping, geometric transformations and noises.

Hassanien, A. E., and D. Slezak, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, issue 4, pp. 205-218 , 2006. AbstractWebsite

The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two outcomes: malignant cancer or benign cancer. Algorithms based on fuzzy image processing are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. Then, the rough set approach to attribute reduction and rule generation is presented. Finally, rough neural network is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. Rough neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bounds. To evaluate performance of the presented rough neural approach, we run tests over different mammogram images. The experimental results show that the overall classification accuracy offered by rough neural approach is high compared with other intelligent techniques

Hassanien, A. E., "A copyright protection using watermarking algorithm", Informatica, vol. 17, no. 2: Institute of Mathematics and Informatics, pp. 187–198, 2006. Abstract
n/a
Hassanien, E. A., "Hiding iris data for authentication of digital images using wavelet theory", Pattern Recognition and Image Analysis, vol. 16, no. 4: MAIK Nauka/Interperiodica, pp. 637–643, 2006. Abstract
n/a
Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
n/a
Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
n/a
Hassanien, A. E., "Pulse coupled neural network for detection of masses in digital mammogram", Neural network world journal, vol. 2, no. 6, pp. 129–141, 2006. Abstract
n/a
Hassanien, A. E., "Pulse coupled neural network for detection of masses in digital mammogram", Neural network world journal, vol. 2, no. 6, pp. 129–141, 2006. Abstract
n/a
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
n/a
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
n/a
Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
n/a
Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
n/a
Hassanien, A., M. Nachtegael, D. VAN DER WEKEN, H. NOBUHARA, and E. Kerre, Soft Computing in Image Processing, : Springer, 2006. Abstract
n/a
Hassanien, A., M. Nachtegael, D. VAN DER WEKEN, H. NOBUHARA, and E. Kerre, Soft Computing in Image Processing, : Springer, 2006. Abstract
n/a
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.

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.