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Dr.Ir. L. (Lejla) Alic


  
    
Assistant Professor
Lejla Alic
Technohal room: 2180
Phone: +31 534898731
E-mail: l.alic@utwente.nl

General information

Lejla Alic studied Electrical Engineering at Delft University of Technology, the Netherlands. She graduated under Prof. Babuska (BSc 1999) on fuzzy clustering of medical signals, and under Dr. ir. van Wijk van Brievingh, Prof. Ince, and Prof. Lelieveldt (MSc 2001) on analysis of colonoscopy images. In 2013 she earned, under supervision of Prof. Niessen, a PhD from Erasmus Universiteit Rotterdam (Biomedical imaging Group Rotterdam) where she has worked on a broad range of methods for assessment and prediction of treatment outcome in pre-clinical and clinical oncology data. Her thesis entitled ‘Quantification of tumour heterogeneity in MRI’ was defended at advanced school for computing and imaging. In the time before and after her graduate school, she was a research fellow at Leiden University Medical Center (Division of Image Processing), at Academic Medical Center university of Amsterdam (Experimental Anesthesiology), and at Engineering Science (University of Oxford - Robotics Research Group, headed by Sir. Brady) . Furthermore, she was also a scientific researcher and project leader at TNO (Dutch contract research institute). In 2017 she started at Texas A&M University at Qatar (Department of Electrical and Computer Engineering) as an Assistant Research Scientist responsible for a variety of imaging applications including analysis of MRI and histology data, initiating new collaborations in the field of medical signal/image processing and analytics with a focus in machine learning.

In 2019 she was appointed as an Assistant Professor at the Department of Magnetic Detection and Imaging. She has (co-)authored over 30 publications (conference abstracts excluded) in the field of medical image analysis, computer vision, and laparoscopy.

Research interests

Magnetic particle detection, magnetic particle imaging; laparoscopy; dual ladling of particles; hyperspectral imaging; magnetic resonance imaging; image registration; image analysis; texture analysis; heterogeneity; machine leaning; data science.

Teaching responsibilities

M8-De bewegende mens en gezondheidsrecht

Publications

2019

Abbas, H., Zahed, K. , Alic, L., Zhu, Y., Sasangohar, F., Mehta, R., ... Qaraqe, K. A. (2019). A Wearable, Low-cost Hand Tremor Sensor for Detecting Hypoglycemic Events in Diabetic Patients. In I. Pasya, A. H. Awang, & F. C. Seman (Eds.), 2018 IEEE International RF and Microwave Conference (RFM) (pp. 182-184). [8846546] IEEE. https://doi.org/10.1109/RFM.2018.8846546

Aljihmani, L. , Alic, L., Boudjemline, Y., Hijazi, Z. M., Mansoor, B., Serpedin, E., & Qaraqe, K. (2019). Magnesium-Based Bioresorbable Stent Materials: Review of Reviews. https://doi.org/10.1007/s40735-019-0216-x

Eresen, A., Hafsa, N. E. , Alic, L., Birch, S. M., Griffin, J. F., Kornegay, J. N., & Ji, J. X. (2019). Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy. Muscle and Nerve, 60(5), 621-628. https://doi.org/10.1002/mus.26657

Eresen, A., Birch, S. M. , Alic, L., Griffin, J. F., Kornegay, J. N., & Ji, J. X. (2019). New similarity metric for registration of MRI to histology: Golden retriever muscular dystrophy imaging. https://doi.org/10.1109/TBME.2018.2870711

Abbas, H. , Alic, L., Rios, M., Abdul-Ghani, M., & Qaraqe, K. (2019). Predicting diabetes in healthy population through machine learning. In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019 (pp. 567-570). [8787404] IEEE. https://doi.org/10.1109/CBMS.2019.00117

Abbas, H. T. , Alic, L., Erraguntla, M., Ji, J. X., Abdul-Ghani, M., Abbasi, Q. H., & Qaraqe, M. K. (2019). Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test. PLoS ONE, 14(12), [e0219636]. https://doi.org/10.1371/journal.pone.0219636

Eresen, A. , Alic, L., Birch, S. M., Friedeck, W., Griffin, J. F., Kornegay, J. N., & Jim X., J. I. (2019). Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy. https://doi.org/10.1002/mus.26386

2018

Eresen, A. , Alic, L., Kornegay, J., & Ji, J. X. (2018). Assessment of disease severity in a Canine Model of Duchenne Muscular Dystrophy: Classification of Quantitative MRI. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 648-651). [8512303] IEEE. https://doi.org/10.1109/EMBC.2018.8512303

2017

Schols, R. M. , Alic, L., Wieringa, F. P., Bouvy, N. D., & Stassen, L. P. S. (2017). Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery. International journal of medical robotics and computer assisted surgery, 13(1), [e1748]. https://doi.org/10.1002/rcs.1748

2015

Schols, R. M. , Alic, L., Beets, G. L., Breukink, S. O., Wieringa, F. P., & Stassen, L. P. S. (2015). Automated spectroscopic tissue classification in colorectal surgery. Surgical Innovation, 22(6), 557-567. https://doi.org/10.1177/1553350615569076

2014

Schols, R. M., Ter Laan, M., Stassen, L. P. S., Bouvy, N. D., Amelink, A., Wieringa, F. P. , & Alic, L. (2014). Differentiation between nerve and adipose tissue using wide-band (350-1,830 nm) in vivo diffuse reflectance spectroscopy. Lasers in surgery and medicine, 46(7), 538-545. https://doi.org/10.1002/lsm.22264

Alic, L., Niessen, W. J., & Veenland, J. F. (2014). Quantification of heterogeneity as a biomarker in tumor imaging: A systematic review. PLoS ONE, 9(10), [e110300]. https://doi.org/10.1371/journal.pone.0110300

Van Rest, J., Grootjen, F. A., Grootjen, M., Wijn, R., Aarts, O., Roelofs, M. L., ... Kraaij, W. (2014). Requirements for multimedia metadata schemes in surveillance applications for security. Multimedia tools and applications, 70(1), 573-598. https://doi.org/10.1007/s11042-013-1575-9

2013

Alic, L. (2013). Quantification of tumour heterogeneity in MRI. Uitgeverij BOXPress.

Alic, L., van Vliet, M., Wielopolski, P. A., ten Hagen, T. L. M., van Dijke, C. F., Niessen, W. J., & Veenland, J. F. (2013). Regional heterogeneity changes in DCE-MRI as response to isolated limb perfusion in experimental soft-tissue sarcomas. Contrast Media and Molecular Imaging, 8(4), 340-349. https://doi.org/10.1002/cmmi.1528

2012

Bol, K., Haeck, J. C. , Alic, L., Niessen, W. J., De Jong, M., Bernsen, M., & Veenland, J. F. (2012). Quantification of DCE-MRI: A validation of three techniques with 3D-histology. In 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings (pp. 1044-1047). [6235737] https://doi.org/10.1109/ISBI.2012.6235737

2011

Bol, K., Haeck, J. C. , Alic, L., Bernsen, M., De Jong, M., Niessen, W. J., & Veenland, J. F. (2011). Developing a tool for the validation of quantitative DCE-MRI. In Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging [79651J] https://doi.org/10.1117/12.877834

Alic, L., Haeck, J. C., Bol, K., Klein, S., van Tiel, S. T., Wielepolski, P. A., ... Veenland, J. F. (2011). Facilitating tumor functional assessment by spatially relating 3D tumor histology and In Vivo MRI: Image registration approach. https://doi.org/10.1371/journal.pone.0022835

Alic, L., Van Vliet, M., Van Dijke, C. F., Eggermont, A. M. M., Veenland, J. F., & Niessen, W. J. (2011). Heterogeneity in DCE-MRI parametric maps: A biomarker for treatment response? https://doi.org/10.1088/0031-9155/56/6/006

2010

Alic, L., Haeck, J. C., Klein, S., Bol, K., Van Tiel, S. T., Wielopolski, P. A., ... De Jong, M. (2010). Multi-modal image registration: Matching MRI with histology. In Medical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging [762603] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7626). https://doi.org/10.1117/12.844123

2006

Alić, L., Veenland, J., van Vliet, M., van Dijke, C. F., Eggermont, A. M. M., & Niessen, W. J. (2006). Quantification of heterogeneity in dynamic contrast enhanced MRI data for tumor treatment assessment. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (pp. 944-947). [162575] (IEEE International Symposium on Biomedical Imaging; Vol. 2006). Piscataway, NJ: IEEE. https://doi.org/10.1109/ISBI.2006.1625075

2001

Babuška, R. , Alic, L., Lourens, M. S., Verbraak, A. F. M., & Bogaard, J. (2001). Estimation of respiratory parameters via fuzzy clustering. https://doi.org/10.1016/S0933-3657(00)00075-0