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-, Y., M. Ali, M. Rushdi, and J. Ho, "Affine-Constrained Group Sparse Coding and Its Application to Image-Based Classifications", {IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney, Australia, December 1-8, 2013, pp. 681–688, 2013. Abstract
Abdelsamad, Y., M. Rushdi, and B. Tawfik, "Functional and Spatial Design of Emergency Departments Using Quality Function Deployment", Journal of Healthcare Engineering, vol. 2018, pp. 1-8, 2018.
Ali, M., M. Rushdi, and J. Ho, "Deconstructing Kernel Machines", Machine Learning and Knowledge Discovery in Databases - European Conference, {ECML} {PKDD} 2014, Nancy, France, September 15-19, 2014. Proceedings, Part {I}, pp. 34–49, 2014. Abstract
Altalabi, W. M., M. A. Rushdi, and B. M. Tawfik, "Optimisation of medical equipment replacement using stochastic dynamic programming", Journal of Medical Engineering & Technology, vol. 44, issue 7, pp. 411-422, 2020.
Amin, M. N., M. A. Rushdi, R. N. Marzaban, A. Yosry, K. Kim, and A. M. Mahmoud, "Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images", Biomedical Signal Processing and Control, vol. 52, issue 2019, pp. 84-96, 2019.
Anam, A. M., M. A. Rushdi, and A. S. Fahmy, "Enhancement of low-resolution HEp-2 cell image classification using partial least-square regression", 2016 {IEEE} International Conference on Image Processing, {ICIP} 2016, Phoenix, AZ, USA, September 25-28, 2016, pp. 1245–1249, 2016. Abstract
Anam, A. M., and M. A. Rushdi, "Classification of scaled texture patterns with transfer learning", Expert Systems with Applications, vol. 120, issue 15 April 2019, pp. 448-460, 2019.
Annaby, M. H., M. H. Said, A. M. Eldeib, and M. A. Rushdi, "EEG-based motor imagery classification using digraph Fourier transforms and extreme learning machines", Biomedical Signal Processing and Control, vol. 69, pp. 1-14, 2021.
Annaby, M. H., H. A. Ayad, J. Prestin, and M. A. Rushdi, "Multiparameter discrete transforms based on discrete orthogonal polynomials and their application to image watermarking", Signal Processing: Image Communication, vol. 99, pp. 116434, 2021.
Annaby, M. H., A. M. Elwer, M. A. Rushdi, and M. E. M. Rasmy, "Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs.", Journal of digital imaging, vol. 34, issue 1, pp. 162-181, 2021. Abstract

Melanoma is the most fatal type of skin cancer. Detection of melanoma from dermoscopic images in an early stage is critical for improving survival rates. Numerous image processing methods have been devised to discriminate between melanoma and benign skin lesions. Previous studies show that the detection performance depends significantly on the skin lesion image representations and features. In this work, we propose a melanoma detection approach that combines graph-theoretic representations with conventional dermoscopic image features to enhance the detection performance. Instead of using individual pixels of skin lesion images as nodes for complex graph representations, superpixels are generated from the skin lesion images and are then used as graph nodes in a superpixel graph. An edge of such a graph connects two adjacent superpixels where the edge weight is a function of the distance between feature descriptors of these superpixels. A graph signal can be defined by assigning to each graph node the output of some single-valued function of the associated superpixel descriptor. Features are extracted from weighted and unweighted graph models in the vertex domain at both local and global scales and in the spectral domain using the graph Fourier transform (GFT). Other features based on color, geometry and texture are extracted from the skin lesion images. Several conventional and ensemble classifiers have been trained and tested on different combinations from those features using two datasets of dermoscopic images from the International Skin Imaging Collaboration (ISIC) archive. The proposed system achieved an AUC of [Formula: see text], an accuracy of [Formula: see text], a specificity of [Formula: see text] and a sensitivity of [Formula: see text].

Annaby, M. H., M. A. Rushdi, and E. A. Nehary, "Image encryption via discrete fractional Fourier-type transforms generated by random matrices", Sig. Proc.: Image Comm., vol. 49, pp. 25–46, 2016. AbstractWebsite
Annaby, M. H., Y. M. Fouda, and M. A. Rushdi, "Improved Normalized Cross-Correlation for Defect Detection in Printed-Circuit Boards", IEEE Transactions on Semiconductor Manufacturing, vol. 32, issue 2, pp. 199-211, 2019.
Atwine, D., Y. W. Karanja, A. Ahluwalia, C. D. Maria, D. Assefa, V. Konde, E. Khundi, P. N. Makobore, M. Moshi, M. Nzomo, et al., "Nurturing next-generation biomedical engineers in Africa: The impact of Innovators’ Summer Schools", Global Health Innovation, vol. 3, issue 2, 2020.
Elbeshbeshy, A. M., M. A. Rushdi, and S. M. El-Metwally, "Electromyography Signal Analysis and Classification using Time-Frequency Representations and Deep Learning.", Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2021, pp. 661-664, 2021. Abstract

Analysis and classification of electromyography (EMG) signals are crucial for rehabilitation and motor control. This study investigates electromyogram (EMG) time-frequency representations and then creates conventional and deep learning models for EMG signal classification. Firstly, a dataset of single-channel surface EMG signals has been recorded for four subjects to differentiate between forearm flexion and extension. Then, different time-frequency EMG representations have been used to build conventional and deep learning models for EMG classification. We compared the performance of pre-trained convolutional neural network models, namely GoogLeNet, SqueezeNet and AlexNet, and achieved accuracies of 92.71%, 90.63% and 87.5%, respectively. Also, data augmentation techniques on the levels of raw EMG signals and their time- frequency representations helped improve the accuracy of GoogLeNet to 96.88%. Furthermore, our approach demonstrated superior performance on another publicly available 10-class EMG dataset, and also using traditional classifiers trained on hand-crafted features.

Elkhouly, H. I., M. A. Rushdi, and R. K. Abdel-Magied, "Eco-friendly date-seed nanofillers for polyethylene terephthalate composite reinforcement", Materials Research Express, vol. 7, issue 2, pp. 025101, 2020.
Makram, A. W., M. A. Rushdi, A. M. Khalifa, and M. E. T. -, "Tag removal in cardiac tagged {MRI} images using coupled dictionary learning", 37th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society, {EMBC} 2015, Milan, Italy, August 25-29, 2015, pp. 7921–7924, 2015. Abstract
Nejhum, S. S. M., M. Rushdi, and J. Ho, "Visual Tracking using Structured Superpixels", 9th International Conference on Vision Systems (ICVS), Saint Petersburg, Russia, 12-15 July, 2013.
Rushdi, M., M. Ali, and J. Ho, "Color De-Rendering using Coupled Dictionary Learning", 20th International Conference on Image Processing (ICIP), Melbourne, Autralia, 15-18 September, 2013.
Rushdi, M., and J. Ho, "Augmented Coupled Dictionary Learning for Image Super-Resolution", 11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, December 12-15, 2012. Volume 1, pp. 262–267, 2012. Abstract
Rushdi, M. A. M., A. M. A. Rushdi, M. Zarouan, and W. Ahmad, "Satisfiability in intuitionistic fuzzy logic with realistic tautology", Kuwait Journal of Science, vol. 45, issue 2, pp. 15-21, 2018.
Rushdi, M., and J. Ho, "Texture Classification using Sparse {K-SVD} Texton Dictionaries", {VISAPP} 2011 - Proceedings of the Sixth International Conference on Computer Vision Theory and Applications, Vilamoura, Algarve, Portugal, 5-7 March, 2011, pp. 187–193, 2011. Abstract
Rushdi, M., M. Ali, and J. Ho, "Color de-rendering using coupled dictionary learning", {IEEE} International Conference on Image Processing, {ICIP} 2013, Melbourne, Australia, September 15-18, 2013, pp. 315–319, 2013. Abstract
Rushdi, A. M., and M. A. Rushdi, "Mathematics and Examples of the Modern Syllogistic Method of Propositional Logic", Frontiers in Information Systems: Bentham Books, 2018.
Rushdi, M., and J. Ho, "Large-scale-invariant Texture Recognition", {VISAPP} 2011 - Proceedings of the Sixth International Conference on Computer Vision Theory and Applications, Vilamoura, Algarve, Portugal, 5-7 March, 2011, pp. 442–445, 2011. Abstract