Publications

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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
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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.
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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
El-dahshan, E., A. Redi, A. E. Hassanien, and K. Xiao, "Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network", Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on: IEEE, pp. 308–311, 2007. Abstract
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El-dahshan, E., A. Redi, A. E. Hassanien, and K. Xiao, "Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network", Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on: IEEE, pp. 308–311, 2007. Abstract
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El-dahshan, E., A. Redi, A. E. Hassanien, and K. Xiao, "Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network", Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on: IEEE, pp. 308–311, 2007. 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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Xiao, K., S. H. Ho, and A. E. Hassanien, "Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction", Malaysian Journal of Computer Science, vol. 20, no. 2: Faculty of Computer Science and Information Technology, pp. 115, 2007. Abstract
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Xiao, K., S. H. Ho, and A. E. Hassanien, "Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction", Malaysian Journal of Computer Science, vol. 20, no. 2: Faculty of Computer Science and Information Technology, pp. 115, 2007. Abstract
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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, and A. E. H. K. and Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent systems , issue Accepted , 2015. Abstract

Abstract—Extraction of relevant features is of significant importance for brain tumor segmentation systems. In this paper, with the objective of improving brain tumor segmentation accuracy, we present an improved feature extraction component to take advantage of the correlation between intracranial structure deformation and the compression from brain tumor growth. Using 3-dimensional non-rigid registration and deformation modeling techniques, the component is capable of measuring lateral ventricular (LaV) deformation in the volumetric magnetic resonance (MR) images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the proposed component is evaluated qualitatively and quantitatively with promising results on 11 datasets comprising real patient and simulated images.

Shang-Ling, S. Z. Jui, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, pp. 66-76, 2016. AbstractWebsite

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features", IEEE Intell. Syst. submitted, under review, 2015. Abstract
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Xiao, K., A. E. Hassanien, Y. Sun, and E. K. K. Ng, "Brain mr image tumor segmentation with ventricular deformation", Image and Graphics (ICIG), 2011 Sixth International Conference on: IEEE, pp. 297–302, 2011. Abstract
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Xiao, K., A. E. Hassanien, Y. Sun, and E. K. K. Ng, "Brain mr image tumor segmentation with ventricular deformation", Image and Graphics (ICIG), 2011 Sixth International Conference on: IEEE, pp. 297–302, 2011. Abstract
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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, no. 2: IEEE, pp. 66–76, 2016. Abstract
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Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density Based Fuzzy Thresholding for Image Segmentation", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, pp. 118--127, 2012. Abstract3220118.pdf

In this paper, we introduce an image segmentation framework which
applies automatic threshoding selection using fuzzy set theory and fuzzy
density model. With the use of different types of fuzzy membership function,
the proposed segmentation method in the framework is applicable for images of
unimodal, bimodal and multimodal histograms. The advantages of the method
are as follows: (1) the threshoding value is automatically retrieved thus requires
no prior knowledge of the image; (2) it is not based on the minimization of a
criterion function therefore is suitable for image intensity values distributed
gradually, for example, medical images; (3) it overcomes the problem of local
minima in the conventional methods. The experimental results have
demonstrated desired performance and effectiveness of the proposed approach.

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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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Xiao, K., L. A. Liang, Haibing Guan, and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120: Elsevier, pp. 177–184, 2013. Abstract
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Xiao, K., L. A. Liang, Haibing Guan, and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120: Elsevier, pp. 177–184, 2013. Abstract
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Jui, S. - L., C. Lin, Haibing Guan, A. Abraham, A. E. Hassanien, and K. Xiao, "Fuzzy c-means with wavelet filtration for MR image segmentation", Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on: IEEE, pp. 12–16, 2014. Abstract
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Xia, K., J. Li, H. G. Shuangjiu Xiao, F. Fang, and A. E. Hassanien, "Fuzzy Clustering with Multi-resolution Bilateral Filtering for Medical Image Segmentation", International Journal of Fuzzy System Applications (IJFSA), vol. 3, issue 4, 2013. fuzzy_clustering_with_multi-resolution_bilateral_filtering_for_medical_image_segmentation-revision.pdf
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