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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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Emad, O., I. A. Yassine, and A. S. Fahmy, "Automatic Localization of the Left Ventricle in Cardiac MRI Images Using Deep Learning", IEEE- Engineering in Medicine and Biology Conference, Milan, Italy, pp. 683-686, 2015.
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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Soliman, O. S., J. Plato, A. E. Hassanien, and V. Snasel, "Automatic Localization and Boundary Detection of Retina using Basic Image Processing Filters", of the third international conference on intelligent human computer interaction , prague, czech republic, Advances in Intelligent Systems and Computing, 2013, Volume 179, Part 3,, pp. 169-182, 2013.
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", International Conference on Engineering and Technology (ICET), Cairo, Egypt, 19-20 April, 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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, "Automatic Knowledge Acquisition tool for irrigation and fertilization expert systems", Expert Systems with Applications, vol. Volume 24,, issue Issue 1, pp. 49-57, 2003.
Rafea, A., H. Hassen, and M. Hazman, "Automatic knowledge acquisition tool for irrigation and fertilization expert systems", Expert systems with Applications, vol. 24, issue 1: Elsevier, pp. 49-57, 2003. Abstract
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Rafea, A., H. Hassen, and M. Hazman, "Automatic knowledge acquisition tool for irrigation and fertilization expert systems", Expert systems with Applications, vol. 24, issue 1: Pergamon, pp. 49-57, 2003. Abstract
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Essa, K. S., and Z. E. Diab, "An automatic inversion approach for magnetic data applying the global bat optimization algorithm (GBOA): application to ore deposits and basement rock intrusion", Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 8, pp. 185, 2022.
Essa, K. S., and Z. E. Diab, "An automatic inversion approach for magnetic data applying the global bat optimization algorithm (GBOA): application to ore deposits and basement rock intrusion", Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 8, issue 185, 2022.
Li, Y., W. Li, S. Tang, W. Darwish, Y. Hu, and W. Chen, "Automatic indoor as-built building information models generation by using low-cost RGB-D sensors", Sensors, vol. 20, issue 1: Multidisciplinary Digital Publishing Institute, pp. 293, 2020. 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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