Dr. Muhammad Usman Akram

Assistant Professor
National University of Sciences and Technology, Pakistan

Highest Degree
Ph.D. in Computer Engineering from National University of Sciences and Technology, Islamabad, Pakistan

My URL
https://livedna.org/92.11857

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Biography

Dr. Muhammad Usman Akram is assistant professor at college of Electrical & Mechanical Engineering, National University of Sciences & Technology, Pakistan. He Holds a PhD degree in Computer Engineering with specialization in medical image analysis and is among the youngest PhDs in Pakistan. His research areas are image/signal processing, biometrics, medical image analysis and pattern recognition. He is a recipient of different national and international awards such as P@SHA 2015, P@SHA 2014, APICTA 2014, P@SHA 2013, APICTA 2013, P@SHA 2012, APICTA 2012, Travel fellowships, cash awards on journal publications, commandant's plaque of excellence and president's gold medal from NUST. He is also HEC Approved PhD Supervisor. He has also worked on various ICT R&D funded projects and 03 of his projects are under evaluation for funding. He is also among the youngest PhD supervisor who has produced a PhD student less than 30 years of age. He has supervised 30 MS students and currently supervising 05 PhD and 08 MS students. Dr. Usman has 115+ international publications and has presented research papers in various National and International conferences. He has 21 ISI indexed impact factor papers with an accumulated impact factor of 31+. His articles have been cited from renowned researchers from all over the world. Dr. Usman is also serving as reviewer for multiple impact factor journals of IEEE, springer and Elsevier i.e. IEEE Transaction on Medical Imaging, Journal of Medical Systems, Computers in Biology and Medicines, IET image processing, IEEE Transaction on Biomedical Engineering etc. He is head of a research group named Biometrics and Medical Image/Signal Analysis (BIOMISA) at College of EME, NUST (www.biomisa.org).

Area of Interest:

Computer Sciences
Pattern Recognition
Medical Image Analysis
Biometrics
Digital Image Processing

Selected Publications

  1. Hassan, T., M.U. Akram, B. Hassan, A.M. Syed and S.A. Bazaz, 2016. Automated segmentation of subretinal layers for the detection of macular edema. Appl. Optics, 55: 454-461.
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  2. Akram, S., M.Y. Javed, M.U. Akram, U. Qamar and A. Hassan, 2016. Pulmonary nodules detection and classification using hybrid features from computerized tomographic images. J. Med. Imaging Health Inf., 6: 252-259.
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  3. Waheed, A., Z. Waheed, M.U. Akram and A. Shaukat, 2015. Removal of false blood vessels using shape based features and image inpainting. J. Sensors, Vol. 2015. 10.1155/2015/839894.
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  4. Waheed, A., M.U. Akram, S. Khalid, Z. Waheed, M.A. Khan and A. Shaukat, 2015. Hybrid features and mediods classification based robust segmentation of blood vessels. J. med. Syst., 39: 1-14.
    Direct Link  |  

  5. Usmani, D., M.U. Akram, S. Abbas, T. Ahmed and I. Basit, 2015. Generation of retinal image mosaic using weber local descriptor. J. Inf. Assur. Secur., 10: 111-119.
    Direct Link  |  

  6. Tahir, F., M.U. Akram, M. Abbas and A.A. Khan, 2015. Detection of laser marks from retinal images for improved diagnosis of diabetic retinopathy. Int. J. Comput. Inf. Syst. Ind. Manage. Applic., 7: 131-138.
    Direct Link  |  

  7. Shaheen, S., M.Y. Javed, M. Mufti, S. Khalid, A. Khanum, S.A. Khan and M.U. Akram, 2015. A novel compression technique for multi-camera nodes through directional correlation. Int. J. Distrib. Sensor Networks, Vol. 2015. 10.1155/2015/539838.
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  8. Shaheen, S., A. Khannum, M.U. Akram, S.A. Khan, S. Seo and M.Y. Javed, 2015. Evaluating the significance of error checksums for wireless video streaming. Multimedia Tools Applic., 10.1007/s11042-015-2636-z.
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  9. Rashid, S., U. Akram and S.A. Khan, 2015. WML: Wireless sensor network based machine learning for leakage detection and size estimation. Procedia Comput. Sci., 63: 171-176.
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  10. Khawaja, S.G., M.H. Mushtaq, S.A. Khan, M.U. Akram and H.U. Jamal, 2015. Designing area optimized application-specific network-on-chip architectures while providing hard QoS guarantees. Plos One, Vol. 10. 10.1371/journal.pone.0125230.
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  11. Khalid, S., U. Akram and S. Razzaq, 2015. Behaviour recognition using multivariate m-mediod based modelling of motion trajectories. Multimedia Syst., 21: 485-505.
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  12. Irshad, S., M.U. Akram, S. Ayub and A. Ayaz, 2015. Retinal Blood Vessels Differentiation for Calculation of Arterio-Venous Ratio. In: Image Analysis and Recognition. Kamel, M. and A. Campilho (Ed.). Springer International Publishing, Switzerland., ISBN: 978-3-319-20800-8, pp: 411-418..
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  13. Haq, I.U., M.U. Akram and Y. Saijo, 2015. Detection of abnormal blood vessels on optic disc for diagnosis of proliferative diabetic retinopathy. J. Inst. Ind. Applic. Eng., 3: 1-5.
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  14. Gill, A.F., S.A. Fatima, M.U. Akram, S.G. Khawaja and S.E. Awan, 2015. Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set. In: Theory and Applications of Applied Electromagnetics. Sulaiman, H.A., M.A. Othman, M.Z.A. Abd Aziz and M.F. Abd Malek (Ed.). Springer International Publishing, Switzerland., ISBN: 978-3-319-17268-2, pp: 49-57..
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  15. Akram, S., M.Y. Javed, A. Hussain, F. Riaz and M.U. Akram, 2015. Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques. J. Exp. Theor. Artif. Intell., 27: 737-751.
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  16. Akram, M.U., A. Tariq, S. Khalid, M.Y. Javed, S. Abbas and U.U. Yasin, 2015. Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques. Aust. Phys. Eng. Sci. Med., 38: 643-655.
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  17. Usman, A., S.A. Khitran, M.U. Akram and Y. Nadeem, 2014. A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images. In: Image Analysis and Recognition. Campilho, A. and M. Kamel (Ed.). Springer International Publishing, Switzerland., ISBN: 978-3-319-11754-6, pp: 303-310..
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  18. Israr, U.H., U. Akram and Y. Saijo, 2014. Automated detection of optic disc using vessels tracking. Trans. Jpn Soc. Med. Biol. Eng., 52: 283-284.
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  19. Butt, W.H., M.U. Akram, S.A. Khan and M.Y. Javed, 2014. Covert network analysis for key player detection and event prediction using a hybrid classifier. Sci. World J., Vol. 2014. 10.1155/2014/615431.
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  20. Akram, M.U., S.A. Khitran, A. Usman and U.U. Yasin, 2014. Detection of Hemorrhages in Colored Fundus Images Using Non Uniform Illumination Estimation. In: Image Analysis and Recognition. Campilho, A. and M. Kamel (Ed.). Springer International Publishing, Switzerland., ISBN: 978-3-319-11754-6, pp: 329-336..
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  21. Akram, M.U., S. Khalid, A. Tariq, S.A. Khan and F. Azam, 2014. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput. Biol. Med., 45: 161-171.
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  22. Akram, M.U., A. Tariq, S.A. Khan and M.Y. Javed, 2014. Automated detection of exudates and macula for grading of diabetic macular edema. Comput. Methods Programs Biomed., 114: 141-152.
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  23. Yaseen, K., A. Tariq and M.U. Akram, 2013. A comparison and evaluation of computerized methods for OD localization and detection in retinal images. Int. J. Future Comput. Commun., 2: 613-616.
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  24. Tariq, A., M.U. Akram, A. Shaukat and S.A. Khan, 2013. Automated detection and grading of diabetic maculopathy in digital retinal images. J. Digital Imaging, 26: 803-812.
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  25. Shabbir, S., A. Tariq and M.U. Akram, 2013. A comparison and evaluation of computerized methods for blood vessel enhancement and segmentation in retinal images. Int. J. Future Comput. Commun., 2: 600-603.
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  26. Akram, M.U., S. Khalid, A. Tariq and M.Y. Javed, 2013. Detection of neovascularization in retinal images using multivariate m-Mediods based classifier. Computerized Med. Imaging Graphics, 37: 346-357.
    CrossRef  |  PubMed  |  Direct Link  |  

  27. Akram, M.U., S. Khalid and S.A. Khan, 2013. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit., 46: 107-116.
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  28. Akram, M.U. and S.A. Khan, 2013. Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy. Eng. Comput., 29: 165-173.
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  29. Akram, M.U. and S. Khalid, 2013. A Software System for Grading Diabetic Retinopathy by Analyzing Retinal Images. In: Knowledge-Based Processes in Software Development. Saeed, S. and I. Alsmadi (Ed.). IGI Global, Pennsylvania, USA., ISBN: 9781466642294, pp: 176-193..
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  30. Tariq, A. and M.U. Akram, 2012. Personal identification using ear recognition. Telkomnika Telecommun. Comput. Electron. Control, 10: 321-326.
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  31. Jamal, I., M.U. Akram and A. Tariq, 2012. Retinal image preprocessing: Background and noise segmentation. Telkomnika Telecommun. Comput. Electron. Control, 10: 537-544.
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  32. Akram, U.M. and S.A. Khan, 2012. Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy. J. Med. Syst., 36: 3151-3162.
    CrossRef  |  PubMed  |  Direct Link  |  

  33. Akram, M.U., I. Jamal and A. Tariq, 2012. Blood vessel enhancement and segmentation for screening of diabetic retinopathy. Telkomnika Telecommun. Comput. Electron. Control, 10: 327-334.
    CrossRef  |  Direct Link  |  

  34. Akram, M.U., A. Tariq, M.A. Anjum and M.Y. Javed, 2012. Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy. Appl. Optics, 51: 4858-4866.
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  35. Akram, M.U., A. Tariq and S.A. Khan, 2012. Detection of Neovascularization for Screening of Proliferative Diabetic Retinopathy. In: Image Analysis and Recognition. Campilho, A. and M. Kamel (Ed.). Springer Berlin Heidelberg, Germany., ISBN: 978-3-642-31297-7, pp: 372-379..
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  36. Akram, M.U., A. Khanum and K. Iqbal, 2010. An automated system for liver CT enhancement and segmentation. ICGST Int. J. Graphics Vision Image Process., 10: 17-22.

  37. Akram, M.U., A. Khan, K. Iqbal and W.H. Butt, 2010. Retinal Images: Optic Disk Localization and Detection. In: Image Analysis and Recognition. Campilho, A. and M. Kamel (Ed.). Springer Berlin Heidelberg, Germany., ISBN: 978-3-642-13774-7, pp: 40-49..
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  38. Tariq, A., M.U. Akram, S. Nasir and R. Arshad, 2008. Fingerprint Image Postprocessing Using Windowing Technique. In: Image Analysis and Recognition. Campilho, A. and M. Kamel (Ed.). Springer Berlin Heidelberg, Germany., ISBN: 978-3-540-69811-1, pp: 915-924..
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  39. Anwar, R., M.U. Akram, R. Arshad and M.U. Munir, 2008. A Modified Singular Point Detection Algorithm. In: Image Analysis and Recognition. Campilho, A. and M. Kamel (Ed.). Springer Berlin Heidelberg, Germany., ISBN: 978-3-540-69811-1, pp: 905-914..
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  40. Akram, M.U., A. Tariq, S.A. Khan and S. Nasir, 2008. Fingerprint image: Pre-and post-processing. Int. J. Biometrics, 1: 63-80.
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