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Dr.Ir. L. (Lejla) Alic

Assistant Professor
Lejla Alic
Technohal room: 2180
Phone: +31 534898731

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


Eresen, A., Hafsa, N.E., Alic, L., Birch, S.M., Griffin, J.F., IV, Kornegay, J.N., Ji, J.X. Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy (2019).

Abbas, H., Alic, L., Rios, M., Abdul-Ghani, M., Qaraqe, K. Predicting diabetes in healthy population through machine learning (2019).

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

Aljihmani, L., Alic, L., Boudjemline, Y., Hijazi, Z.M., Mansoor, B., Serpedin, E., Qaraqe, K. Magnesium-Based Bioresorbable Stent Materials: Review of Reviews (2019).

Eresen, A., Alic, L., Birch, S.M., Friedeck, W., Griffin, J.F., IV, Kornegay, J.N., Jim X., J.I. Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy (2019).

Abbas, H.T., Alic, L., Erraguntla, M., Ji, J.X., Abdul-Ghani, M., Abbasi, Q.H., Qaraqe, M.K. Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test (2019).

Abbas, H., Zahed, K., Alic, L., Zhu, Y., Sasangohar, F., Mehta, R., Lawley, M., Abbasi, Q.H., Qaraqe, K.A. A Wearable, Low-cost Hand Tremor Sensor for Detecting Hypoglycemic Events in Diabetic Patients (2018).

Eresen, A., Alic, L., Kornegay, J., Ji, J.X. Assessment of disease severity in a Canine Model of Duchenne Muscular Dystrophy: Classification of Quantitative MRI (2018).

Schols, R.M., Alic, L., Wieringa, F.P., Bouvy, N.D., Stassen, L.P.S. Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery (2017).

Schols, R.M., Alic, L., Beets, G.L., Breukink, S.O., Wieringa, F.P., Stassen, L.P.S. Automated spectroscopic tissue classification in colorectal surgery (2015).

Alic, L., Niessen, W.J., Veenland, J.F. Quantification of heterogeneity as a biomarker in tumor imaging: A systematic review (2014).

Schols, R.M., Ter Laan, M., Stassen, L.P.S., Bouvy, N.D., Amelink, A., Wieringa, F.P., Alic, L. Differentiation between nerve and adipose tissue using wide-band (350-1,830 nm) in vivo diffuse reflectance spectroscopy (2014).

Van Rest, J., Grootjen, F.A., Grootjen, M., Wijn, R., Aarts, O., Roelofs, M.L., Burghouts, G.J., Bouma, H., Alic, L., Kraaij, W. Requirements for multimedia metadata schemes in surveillance applications for security (2014).

Alic, L., van Vliet, M., Wielopolski, P.A., ten Hagen, T.L.M., van Dijke, C.F., Niessen, W.J., Veenland, J.F. Regional heterogeneity changes in DCE-MRI as response to isolated limb perfusion in experimental soft-tissue sarcomas (2013).

Bol, K., Haeck, J.C., Alic, L., Niessen, W.J., De Jong, M., Bernsen, M., Veenland, J.F. Quantification of DCE-MRI: A validation of three techniques with 3D-histology (2012).

Alic, L., Haeck, J.C., Bol, K., Klein, S., van Tiel, S.T., Wielepolski, P.A., de Jong, M., Niessen, W.J., Bernsen, M., Veenland, J.F. Facilitating tumor functional assessment by spatially relating 3D tumor histology and In Vivo MRI: Image registration approach (2011).

Bol, K., Haeck, J.C., Alic, L., Bernsen, M., De Jong, M., Niessen, W.J., Veenland, J.F. Developing a tool for the validation of quantitative DCE-MRI (2011).

Alic, L., Van Vliet, M., Van Dijke, C.F., Eggermont, A.M.M., Veenland, J.F., Niessen, W.J. Heterogeneity in DCE-MRI parametric maps: A biomarker for treatment response? (2011).

Alic, L., Haeck, J.C., Klein, S., Bol, K., Van Tiel, S.T., Wielopolski, P.A., Bijster, M., Niessen, W.J., Bernsen, M., Veenland, J.F., De Jong, M. Multi-modal image registration: Matching MRI with histology (2010).

Alić, L., Veenland, J., Van Vliet, M., Van Dijke, C.F., Eggermont, A.M.M., Niessen, W.J. Quantification of heterogeneity in dynamic contrast enhanced MRI data for tumor treatment assessment (2006).

Babuška, R., Alic, L., Lourens, M.S., Verbraak, A.F.M., Bogaard, J. Estimation of respiratory parameters via fuzzy clustering (2001).