Computer Science

Dana Cobzas

Assistant Professor, Computer Science

PhD (Alberta)

5-174A, City Centre Campus
10700 – 104 Avenue
Edmonton, AB


Dr. Cobzas's areas of expertise are centered around imaging and computer vision, with particular interest in mathematical models for medical image processing. She has experience and interest in methods for medical image segmentation, registration, noise reduction.  Besides medical imaging, she has good knowledge of most computer vision aspects like dynamic vision (tracking), 3D modeling from uncalibrated images and video (reconstruction of geometry and appearance from images). 

Available to supervise honours or individual study students.

Image analysis, computer vision, medical imaging, image segmentation, machine learning

Teaching and Research Interests

Dr. Cobzas is interested in teaching imaging courses, starting with basic image processing, computer vision or computer graphics to more advanced courses on medical image analysis. 

Selected Publications / Presentations / Conference Papers

Tang, M., Valipour, S., Zhang, V., Cobzas, D., & Jagersabd, M. (September, 2017). A deep level set method for image segmentation. In Deep learning in medical image analysis workshop. International Conference on Medical Image Computing and Computer Assisted Intervention. Quebec City, Canada.

Zhang, L., Cobzas, D., Wilman, A., & Kong, L. (September, 2017).  An unbiased penalty for sparse classification with application to neuroimaging data. International Conference on Medical Image Computing and Computer Assisted Intervention. Quebec City, Canada.

Elkady A., Cobzas D., Sun H., Blevins G., & Wilman, A.H., (2017). Progressive iron accumulation in MS revealed by sparse classification of deep gray matter. J Magn Reson Imaging.

Popuri K., Cobzas D., Esfandiari N., Baracos B., & Jägersand, M. (2015). Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. Transaction of Medical Imaging. 35(2), 512-20.

Mosayebi, P., Cobzas, D., Mutrtha A., &Jagersand, M. (2011). Tumor invasion margin on the riemannian space of brain fibers. Medical Image Analysis. 16(2), 361-73.

Awards / Grants / Fellowships

NSERC Discovery, 2015
NSERC Discovery, 2010

Boards / Committees

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), reviewer
IEEE International Symposium on Biomedical Imaging (ISBI), reviewer