Publications

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Book Chapter
Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Bio-inspiring Techniques in Watermarking Medical Images: A Review", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 93–114, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Robust 3D-Watermarking Scheme Based on Progressive Mesh and Self Organization Maps", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 131–142, 2013. Abstract
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Oliva, D., and A. E. Hassanien, "Digital Images Segmentation Using a Physical-Inspired Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Segmentation is one of the most important tasks in image processing. It classifies the pixels into two or more groups depending on their intensity levels and a threshold value. The classical methods exhaustively search the best thresholds for a spec image. This process requires a high computational effort, to avoid this situation has been incremented the use of evolutionary algorithms. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. This chapter introduces a multilevel thresholding (MT) algorithm based on the EMO and the Otsu's method as objective function. The combination of those techniques generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image reducing the number of iterations.

Oliva, D., and A. E. Hassanien, "Digital Images Segmentation Using a Physical-Inspired Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 975–996, 2017. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Osman, M. A., A. Darwish, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

Osman, M. A., A. Darwish, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

Osman, M. A., A. Darwish, A. E. Khedr, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 925–941, 2017. Abstract
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Azar, A. T., A. E. Hassanien, T. - H. Kim, and others, "Expert system based on neural-fuzzy rules for thyroid diseases diagnosis", Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012. Abstract
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Azar, A. T., A. E. Hassanien, T. - H. Kim, and others, "Expert system based on neural-fuzzy rules for thyroid diseases diagnosis", Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012. Abstract
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Azar, A. T., A. E. Hassanien, T. - H. Kim, and others, "Expert system based on neural-fuzzy rules for thyroid diseases diagnosis", Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An Optimized Approach for Medical Image Watermarking", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 71–91, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms", Intelligent Systems' 2014: Springer International Publishing, pp. 731–741, 2015. Abstract
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Conference Paper
Ali, M. A., A. Assefa, D. Assefa, L. Bal{\'ık, A. Basu, O. Berger, E. Berhan, B. Beshah, E. Birhan, T. Buriánek, et al., "Abraham, Ajith 183, 293,303, 315, 371 Ahmed, Nada 315 Aldosari, Hamoud M. 303 Alhamedi, Adel H. 303", Afro-European Conference for Industrial Advancement: Proceedings of the First International Afro-European Conference for Industrial Advancement AECIA 2014, vol. 334: Springer, pp. 383, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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Davendra, D., A. El-Atta, H. Ahmed, M. A. Abu ElSoud, M. Adamek, M. Adhikari, A. Adl, H. Aldosari, Abder-Rahman Ali, A. F. Ali, et al., "Cordeschi, Nicola 43 Couceiro, Micael 83, 131 Czopik, Jan 365 Dasgupta, Kousik 271", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, vol. 303: Springer, pp. 439, 2014. 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. 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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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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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.

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