Publications

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Journal Article
Xiao, K., Alei Liang, Haibing Guan, and A. E. Hassanien, " Extraction and application of deformation-based feature in medical images.", Neurocomputing :, vol. 120, pp. 177-184, 2013. Website
El-Said, S. A., H. M. A. Atta, and A. E. Hassanien, " Interactive soft tissue modelling for virtual reality surgery simulation and planning,", Int. J. Computer Aided Engineering and Technology, Inderscience, , vol. 9, issue 1, pp. pp. 38-61, 2017. AbstractWebsite

While most existing virtual reality-based surgical simulators in the literature use linear deformation models, soft-tissues exhibit geometric and material nonlinearities that should be taken into account for realistic modelling of the deformations. In this paper, an interactive soft tissue model (ISTM) which enables flexible, accurate and robust simulation of surgical interventions on virtual patients is proposed. In ISTM, simulating the tool-tissue interactions using nonlinear dynamic analysis is formulated within a total Lagrangian framework, and the energy function is modified by adding a term in order to achieve material incompressibility. The simulation results show that ISTM increases the stability and eliminates integration errors in the dynamic solution, decreases calculation costs by a factor of 5-7, and leads to very stable and sufficiently accurate results. From the simulation results it can be concluded that the proposed model can successfully create acceptable soft tissue models and generate realistically visual effects of surgical simulation.

Ragab A. El-Sehiemy, Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, " Multiobjective Real-Coded Genetic Algorithm for Economic/Environmental Dispatch Problem, ", Studies in Informatics and Control, , vol. 22, issue 2, pp. 113-122, 2013. Website
Tharwatd, A., T. Gaber, and A. E. Hassanien, " One-dimensional vs. two-dimensional based features: Plant identification approach, ", Journal of Applied Logic , vol. Available online 15 November 2017 , 2017. AbstractWebsite

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

Karam, H., A. E. Hassanien, and M. Nakajima, "15-1 Polar Decomposition Interpolations for Linear Fractal Metamorphosis", 映像情報メディア学会年次大会講演予稿集, no. 1998: 一般社団法人映像情報メディア学会, pp. 200–201, 1998. Abstract
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Hassanien, A. E., H. Karam, and M. Nakajima, "15-2 Image Metamorphosis for Inter Slice Interpolation of Medical Images", 映像情報メディア学会年次大会講演予稿集, no. 1998: 一般社団法人映像情報メディア学会, pp. 202–203, 1998. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th International Conference on Intelligent Systems Design and Applications, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th International Conference on Intelligent Systems Design and Applications, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th InternaƟonal Conference on, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th InternaƟonal Conference on, , 2010. Abstract
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Alshabrawy, O. S., A. E. Hassanien, W. A. Awad, and A. A. Salama, 2013 13th International Conference on Hybrid Intelligent Systems (HIS), , 2013. Abstract

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Hassanien, A. E., and M. Nakajima, "7) Image Morphing by using Thin Plate Spline Transformation and Snake Model", 映像情報メディア学会誌: 映像情報メディア, vol. 52, no. 6: 一般社団法人映像情報メディア学会, pp. 833, 1998. Abstract
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Xiao, K., S. H. Ho, and A. E. Hassanien, "Aboul Ella Hassanien: Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means", Fundamenta Informaticae, vol. 87, issue 3-4, pp. 465-481, 2008. Website
Azar, A. T., and A. E. Hassanien, "Aboul Ella Hassanien: Dimensionality reduction of medical big data using neural-fuzzy classifier.", soft computing , vol. 19, issue 4, pp. 1115-1127, 2015. Website
M.Moftah, H., A. T. Azar, E. T. Al-Shammari, N. I.Ghali, A. E. Hassanien, and M. Shoman, "Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation", Neural Computing and Applications (Springer), 2013. Abstract

Image segmentation is vital for meaningful analysis and interpretation
of medical images. The most popular method for clustering is k-means
clustering. This article presents a new approach intended to provide more reliable
Magnetic Resonance (MR) breast image segmentation that is based on
adaptation to identify target objects through an optimization methodology
that maintains the optimum result during iterations. The proposed approach
improves and enhances the effectiveness and efficiency of the traditional kmeans
clustering algorithm. The performance of the presented approach was
evaluated using various tests and different MR breast images. The experimental
results demonstrate that the overall accuracy provided by the proposed
adaptive k-means approach is superior to the standard k-means clustering
technique.

Moftah, H. M., A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, "Adaptive k-means clustering algorithm for MR breast image segmentation", Neural Computing and Applications, vol. 24, no. 7-8: Springer London, pp. 1917–1928, 2014. Abstract
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Moftah, H. M., A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, "Adaptive k-means clustering algorithm for MR breast image segmentation", Neural Computing and Applications, vol. 24, no. 7-8: Springer London, pp. 1917–1928, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive watermarking approach based on weighted quantum particle swarm optimization", Neural Computing and Applications, vol. 27, issue 2, pp. 469–481, 2016. AbstractWebsite

In this paper, we propose a novel optimal singular value decomposition (SVD)-based image watermarking approach that uses a new combination of weighted quantum particle swarm optimization (WQPSO) algorithm and a human visual system (HVS) model for both the hybrid discrete wavelet transform and discrete cosine transform (DCT). The proposed SVD-based watermarking approach initially decomposes the host image into sub-bands; afterwards, singular values of the DCT of the lower sub-band of the host image are quantized using a set of optimal quantization steps deduced from a combination of the WQPSO algorithm and the HVS model. To evaluate the performance of the proposed approach, we present tests on different images. The experimental results show that the proposed approach yields a watermarked image with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks, including JPEG compression, Gaussian noise, salt and pepper noises, Gaussian filters, median filters, image cropping, and image scaling. Moreover, the results of various experimental analyses demonstrated the superiority of the WQPSO approach over other optimization techniques, including classical PSO and QPSO in terms of local convergence speed, resulting in a better balance between global and local searches of the watermarking algorithm.

Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive watermarking approach based on weighted quantum particle swarm optimization", Neural Computing and Applications, vol. 27, no. 2: Springer London, pp. 469–481, 2016. Abstract
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Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent", International Journal of Smart Home, (ISSN: 1975-4094), vol. 6, issue 1, pp. 37-45, 2012. AbstractIJSH_ 2012.pdfWebsite

In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.

Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive watermarking approach for medical imaging using swarm intelligent", International Journal of Smart Home, vol. 6, no. 1, pp. 37–50, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive watermarking approach for medical imaging using swarm intelligent", International Journal of Smart Home, vol. 6, no. 1, pp. 37–50, 2012. Abstract
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Awad, A. I., A. E. Hassanien, and K. Baba, "Advances in Security of Information and Communication Networks", Communications in Computer and Information Science, vol. 381, 2013. Abstract
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Atteya, W. A., L. E. M. Bakrawy, R. Batista, S. Bazzi, M. Beheshti, A. P. Bennett, A. Boulmakoul, S. Bureerat, L. Caggiani, N. S. Choubey, et al., "Akbarzadeh-T, Mohammad-R. 175 Antunes, Carlos Henggeler 13", Advances in Intelligent and Soft Computing 96, vol. 6, pp. 437, 2011. Abstract
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Hassanien, A. E., T. - H. Kim, P. S. Rajan, and K. K. K. Hari, "Analysis of Energy Utilization through Mobile Ad Hoc Network with AODV", Proc. of the Intl. Conf. on Computer Applications, vol. 1, 2012. Abstract

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