Publications

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Alaa Tharwat, B. E. Elnaghi, A. M. Ghanem, and A. E. Hassanien, "Automatically Human Age Estimation Approach via Two-Dimensional Facial Image Analysis", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 491–501, 2016. Abstract
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Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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Zawbaa, H. M., and A. E. Hassanien, Automatic Soccer Video Summarization, , Cairo, Cairo Unversity, 2012. Abstract

This thesis presents an automatic soccer video summarization system using machine learning (ML) techniques. The proposed system is composed of ve phases. Namely; in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing
phase, it applies two types of classi cation (shot type classi cation and play / break classification) to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the proposed system applies two machine learning algorithms, namely; support vector machine (SVM) and arti cial neural network (ANN), for emphasizing important segments with championship logo appearance. Also, in the excitement event detection phase, the proposed system uses both machine learning algorithms for detecting the scoreboard which contain an information about the score of the game. The proposed system also uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor lter for detecting goal net. Finally, in the event detection and summarization phase, the proposed system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The event detection and summarization has attained recall 94% and precision 97.3% for soccer match videos from ve international soccer championships.

Zawbaa, H. M., and A. E. Hassanien, Automatic Soccer Video Summarization, : Cairo Unversity, 2012. Abstract
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Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), 2013 , Okinawa, Japan. , 5 Nov. , 2013.
Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on: IEEE, pp. 937–941, 2013. Abstract
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Fouad, M. M. M., H. zawbaa, N. Elbendary, and H. Aboul Ella, "Automatic Nile Tilapia Fish Classification Approach using Machine Learning Techniques", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 173-179, , Tunisia, , 4-6 Dec, 2013.
Fouad, M. M. M., H. M. Zawbaa, N. El-Bendary, and A. E. Hassanien, "Automatic nile tilapia fish classification approach using machine learning techniques", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 173–178, 2013. Abstract
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Ahmed M. Anter, M. A. Elsoud, and A. E. Hassanien, "Automatic Mammographic Parenchyma Classification According to BIRADS Dictionary", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, pp. 22-37,, 2014. Abstract

Internal.density.of.the.breast.is.a.parameter.that.clearly.affects.the.performance.of.segmentation.and.
classification.algorithms.to.define.abnormality.regions..Recent.studies.have.shown.that.their.sensitivity.
is.significantly.decreased.as.the.density.of.the.breast.is.increased..In.this.chapter,.enhancement.and. segmentation.processis applied to increase the computation and focus onmammographic parenchyma.
This.parenchyma is analyzed to discriminate tissue density according to BIRADS using Local Binary
Pattern.(LBP),.Gray.Level.Co-Occurrence.Matrix.(GLCM),.Fractal.Dimension.(FD),.and.feature.fusion.
technique.is.applied.to.maximize.and.enhance.the.performance.of.the.classifier.rate..The.different.methods.
for.computing.tissue.density.parameter.are.reviewed,.and.the.authors.also.present.and.exhaustively.
evaluate.algorithms.using.computer.vision.techniques..The.experimental.results.based.on.confusion.
matrix.and.kappa.coefficient.show.a.higher.accuracy.is.obtained.by.automatic.agreement.classification.

Anter, A. M., M. A. Elsoud, and A. E. Hassanien, "Automatic mammographic parenchyma classification according to BIRADS dictionary", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies. IGI Global, pp. 22–37, 2014. Abstract
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Soliman, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. Abstract
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Soliman, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. Abstract
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Soliman, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. Abstract
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Anter, A. M., A. E. Hassanien, A. T. Azar, and M. A. Elsoud, "Automatic Liver Parenchyma Segmentation System from Abdominal CT Scans using Hybrid Techniques", International Journal of Biomedical Engineering and Technology, vol. 17, issue 2, 2015. AbstractWebsite

In this paper, a multi–layer heuristic approach is introduced to segment liver region from other tissues in multi–slice CT images. Image noise is a principal factor which hampers the visual quality of medical images and can therefore lead to misdiagnosis. To address this issue, we first utilise an algorithm based on median filter to remove noise and enhance the contrast of the CT image. This is followed by performing an adaptive threshold algorithm and morphological operators to preserve the liver structure and remove the fragments of other organs. Then, connected component labelling algorithm was applied to remove false positive regions and focused on liver region. To evaluate the performance of the proposed system, we present tests on different liver CT scans images. The experimental results show that the overall accuracy offered by the employed system is high compared with other related works as well as very fast which segment liver from abdominal CT in less than 0.6 s/slice.

Anter, A. M., A. E. Hassanien, M. A. Elsoud, and A. T. Azar, "Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques", International Journal of Biomedical Engineering and Technology, vol. 17, no. 2: Inderscience Publishers, pp. 148–167, 2015. Abstract
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Ahmed M. Anter, M. A. Elsoud, and A. E. Hassanien, "Automatic Liver Parenchyma Segmentation from Abdominal CT Images", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 32 – 36, Cairo, EGYPT -, December 29-30, , 2013.
Anter, A. M., M. A. Elsoud, and A. E. Hassanien, "Automatic liver Parenchyma segmentation from abdominal CT images", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 32–36, 2013. Abstract
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El-Masry1, W. H., Eid Emary, and A. E. Hassanien, "Automatic Liver CT Image Clustering based on Invasive Weed Optimization Algorithm ", The second International Conference on Engineering and Technology (ICET 2014), German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
El-Masry, W. H., Eid Emary, and A. E. Hassanien, "Automatic liver CT image clustering based on invasive weed optimization algorithm", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–5, 2014. 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.
Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization ", World Congress on Information and Communication Technologies (WICT), pp. 790 - 794, India, Oct. 30 2012-Nov. Abstract

This paper presents an automatic image annotation approach that integrates the Naive Bayes classifier with particle swarm optimization algorithm for classes' probabilities weighting. The proposed hybrid approach refines the output of multi-class classification that is based on the usage of Naive Bayes classifier for automatically labeling images with a number of words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. One Naive Bayes classifier is trained for all the classes. Particle swarm optimization algorithm is employed as a search strategy in order to identify an optimal weighting for classes probabilities from Naive Bayes classifier. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance of the other approaches, considering annotation accuracy, for the experimented dataset.

Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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