Publications

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Book
Dey, N., V. Bhateja, and A. E. Hassanien, Medical Imaging in Clinical Applications: Algorithmic and Computer-Based Approaches, , Germany , Springer, 2016. images_1.jpgWebsite
Book Chapter
Mouhamed, M. R., A. Darwish, and A. E. Hassanien, "2D and 3D Intelligent Watermarking", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 652–669, 2017. Abstract
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Chakraborty, S., S. Chatterjee, N. Dey, A. S. Ashour, and A. E. Hassanien, "Comparative Approach Between Singular Value Decomposition and Randomized Singular Value Decomposition-based Watermarking", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 133–149, 2017. Abstract
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Hassanien, A. E., G. Schaefer, and A. Darwish, "Computational intelligence in speech and audio processing: recent advances", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 303–311, 2010. Abstract
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Hassanien, A. E., G. Schaefer, and A. Darwish, "Computational intelligence in speech and audio processing: recent advances", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 303–311, 2010. 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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Dey, N., A. S. Ashour, and A. E. Hassanien, "Feature Detectors and Descriptors Generations with Numerous Images and Video Applications: A Recap", Feature Detectors and Motion Detection in Video Processing: IGI Global, pp. 36–65, 2017. Abstract
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Salama, M. A., H. F. Eid, R. A. Ramadan, A. Darwish, and A. E. Hassanien, "Hybrid intelligent intrusion detection scheme", Soft computing in industrial applications: Springer Berlin Heidelberg, pp. 293–303, 2011. Abstract
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Darwish, A., M. M. El-Gendy, and A. E. Hassanien, "A New Hybrid Cryptosystem for Internet of Things Applications", Multimedia Forensics and Security: Springer International Publishing, pp. 365–380, 2017. Abstract
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Hore, S., T. Bhattacharya, N. Dey, A. E. Hassanien, A. Banerjee, and S. R. B. Chaudhuri, "A Real Time Dactylology Based Feature Extractrion for Selective Image Encryption and Artificial Neural Network", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 203–226, 2016. Abstract
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Dey, N., A. S. Ashour, S. Chakraborty, S. Banerjee, E. Gospodinova, M. Gospodinov, and A. E. Hassanien, "Watermarking in Biomedical Signal Processing", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 345–369, 2017. Abstract
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Conference Paper
Xiao, K., S. H. Ho, A. E. Hassanien, V. N. Du, and Q. Salih, " Fuzzy C-means clustering with adjustable feature weighting distribution for brain MRI ventricles segmentation. ", SIP 2007: 466-471, Honolulu, Hawaii, USA, August 20-22, 2007.
El-Bendary, N., H. M. Zawbaa, M. S. Daoud, A. E. Hassanien, and K. Nakamatsu, "ArSLAT: Arabic sign language alphabets translator", Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on: IEEE, pp. 590–595, 2010. Abstract
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El-Bendary, N., H. M. Zawbaa, M. S. Daoud, A. E. Hassanien, and K. Nakamatsu, "ArSLAT: Arabic sign language alphabets translator", Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on: IEEE, pp. 590–595, 2010. Abstract
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Sayed, G. I., A. Darwish, A. E. Hassanien, and J. - S. Pan, "Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 306–313, 2016. Abstract
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Schaefer, G., N. P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images Using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 299–308, 2014. 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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Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density based fuzzy thresholding for image segmentation", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 2012. Abstract
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Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density based fuzzy thresholding for image segmentation", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 2012. Abstract
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Jagatheesan, K., B. Anand, N. Dey, T. Gaber, A. E. Hassanien, and T. - H. Kim, "A Design of PI Controller using Stochastic Particle Swarm Optimization in Load Frequency Control of Thermal Power Systems", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 25–32, 2015. Abstract
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Gaber, T., T. Kotyk, N. Dey, A. D. C. V. Amira Ashour, A. E. Hassanienan, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) , Springer. , Beni Suef University, Beni Suef, Egypt, Nov. 28-30, 2015. Abstract

Apoptosis is an imperative constituent of various processes including proper
progression and functioning of the immune system, embryonic development as well
as chemical-induced cell death. Improper apoptosis is a reason in numerous human/
animal’s conditions involving ischemic damage, neurodegenerative diseases,
autoimmune disorders and various types of cancer. An outstanding feature of
neurodegenerative diseases is the loss of specific neuronal populations. Thus, the
detection of the dead cells is a necessity. This paper proposes a novel algorithm to
achieve the dead cells detection based on color intensity and contrast changes and
aims for fully automatic apoptosis detection based on image analysis method. A
stained cultures images using Caspase stain of albino rats hippocampus specimens
using light microscope (total 21 images) were used to evaluate the system
performance. The results proved that the proposed system is efficient as it achieved
high accuracy (98.89 ± 0.76 %) and specificity (99.36 ± 0.63 %) and good mean
sensitivity level of (72.34 ± 19.85 %).

Kotyk, T., N. Dey, A. S. Ashour, C. V. A. Drugarin, T. Gaber, A. E. Hassanien, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 57–68, 2016. Abstract
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Acharjee, S., S. Chakraborty, S. Samanta, A. T. Azar, A. E. Hassanien, and N. Dey, "Highly secured multilayered motion vector watermarking", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, 28-30 Nov. 2014.
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