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We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

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Mehran Ebrahimi
PhD

Assistant Professor

Computer science
Faculty of Science

Advancing medical imaging techniques to promote earlier diagnosis and minimally invasive surgical procedures.

Contact expert

mehran.ebrahimi@ontariotechu.ca
905.721.8668 ext. 6236

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Areas of expertise


  • PhD - Department of Applied Mathematics University of Waterloo, Waterloo, Ontario 2008

Mathematical Challenges in Medical Image Registration

Waterloo, Ontario June 7, 2015

Applied Mathematics, Modelling and Computational Science, Canadian Applied and Industrial Mathematics Society

Evaluating Thin Plate Spline Registration of the Breast in Two Supine Positions

Rochester, New York September 22, 2012

AMS Sectional Meeting, Special Session on Mathematical Image Processing

Mathematical Methods for Breast Image Registration

University of Toronto, Toronto, Ontario June 20, 2011

Fields-MITACS Conference on the Mathematics of Medical Imaging

A PDE Approach to Coupled Super-Resolution with Non-Parametric Motion

Chicago, Illinois April 12, 2010

Society for Applied and Industrial Mathematics International Conference on Imaging Science

2019

  • [KAE+19] S. Kulaseharan, A. Aminpour, M. Ebrahimi and E. Widjaja (2019). Identifying Lesions in Paediatric Epilepsy using Morphometric and Textural Analysis of Magnetic Resonance Images, NeuroImage: Clinical, Open access, January 2019 (Journal Article)

2018

  • [ME18] M. Mojica and M. Ebrahimi (2018). An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images, Proceedings of International Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO) in conjunction with MICCAI, Granada, Spain, September 2018 (Conference Proceedings)
  • [NNE19] K. Nazeri, E. Ng, and M. Ebrahimi (2018). Image Colorization using Generative Adversarial Networks, Lecture Notes in Computer Science, Proceedings of tenth international conference on Articulated Motion and Deformable Objects (AMDO), Palma, Mallorca, Spain, 12-13 July 2018 (Conference Proceedings)
  • [NAE18] K. Nazeri, A. Aminpour, and M. Ebrahimi (2018). Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification, Lecture Notes in Computer Science, Proceedings of International Conference on Image Analysis and Recognition (ICIAR). Póvoa de Varzim, Portugal(Conference Proceedings)
  • [HKE18] H. Thasarathan, K. Nazeri and M. Ebrahimi (2018). Perceptual Loss for Image to Image Translation, UOIT student research showcase (Conference Abstract)
  • [Moj+18] M. Mojica, M. Pop, M. Sermesant, and M. Ebrahimi (2018). Groupwise Registration and Diffusion Tensor Reorientation in Cardiac MRI: Application to Explanted Porcine Hearts, Imaging Network Ontario (ImNO) symposium, Toronto, Ontario, Canada (Conference Abstract)

2017

  • [Ku17] S. Kulaseharan (2017). Identifying Lesions in Paediatric Epilepsy using Morphometric and Textural analysis of Magnetic Resonance Images (MSc Thesis)
  • [Eb17] M. Ebrahimi (2017). Breast Imaging: Mammography, Digital Tomosynthesis, Dynamic Contrast Enhancement, Book Chapter, Reference Module in Biomedical Sciences, Elsevier, 2017 (Journal Article)
  • [Moj+17] M. Mojica, M. Pop, M. Sermesant, and M. Ebrahimi (2017). Multilevel Non-Parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts, STACOM (Statistical Atlases and Computational Modelling of the Heart) 2017, Quebec City, September 2017 (Conference Proceedings)
  • [ABE17a] G. Ahmadian, S. Bohun, and M. Ebrahimi (2017). Coherent Point Drift Algorithms for Breast Image Registration. International Journal of CARS (2017) 12(Suppl 1), S193-195 (Conference Proceedings)
  • [ME17a] L. Ma and M. Ebrahimi (2017). Slice-to-Volume Image Registration Models for MRI-Guided Cardiac Procedures. Proceedings of Functional Imaging and Modelling of the Heart (FIMH). Toronto, Canada, 139--151 (Conference Proceedings)
  • [ABE17b] G. Ahmadian, S. Bohun, and M. Ebrahimi (2017). Coherent point drift algorithms for breast image registration. In Imaging Network Ontario (ImNO) symposium, London, Ontario, Canada (Conference Abstract)
  • [ME17b] L. Ma and M. Ebrahimi (2017). Slice-to-volume Parametric Image Registration Models with Applications to Cardiac MRI. SPIE Digital Library as part of the proceedings of the Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling conference. Orlando, Florida, USA, 16 pages (Conference Proceedings)
  • [FBE17] C. Falconer, S. Bohun, and M. Ebrahimi (2017). A Note on Boosting Algorithms for Image Denoising. Lecture Notes in Computer Science, Proceedings of International Conference on Image Analysis and Recognition (ICIAR). Montreal, Canada, 134- 142 (Conference Proceedings)
  • [KEW17] S. Kulaseharan, M. Ebrahimi, and E. Widjaja (2017). Identifying Epileptogenic Lesions using Cortical Thickness. Imaging Network Ontario (ImNO) symposium. London, Ontario, Canada (Conference Abstract)
  • [Ah17] G. Ahmadian (2017). A Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers, Master of Science (MSc), Modelling and Computational Science, UOIT (MSc Thesis)
  • [Ng17] E. Ng (2017). Thin Plate Spline Image Registration in the Presence of Localized Landmark Errors, Undergraduate Thesis in Applied and Industrial Mathematics, UOIT (BSc Thesis)

2016

  • [ABE16] G. Ahmadian, S. Bohun, and M. Ebrahimi (2016). Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers, Journal of Computational Vision and Imaging Systems, Open Access, DOI: http://dx.doi.org/10.15353/vsnl.v2i1 (Journal Article)
  • [Ma16] L. Ma (2016). Mathematical methods for 2D-3D cardiac image registration, Master of Science (MSc), Modelling and Computational Science, UOIT(MSc Thesis)
  • [ME16a] L. Ma and M. Ebrahimi (2016). A Flexible 2D-3D Parametric Image Registration Algorithm for Cardiac MRI. Lecture Notes in Computer Science, Proceedings of The International Conference on Image Analysis and Recognition ICIAR. Povoa de Varzim, Portugal, 661-671 (Conference Proceedings)
  • [MEP16a] L. Ma, M. Ebrahimi, and M. Pop (2016). Subject-specific patch-based denoising for contrast-enhanced cardiac MR images. SPIE Digital Library, proceedings of the Medical Imaging, Volume 9784, 7 pages (Conference Proceedings)
  • [ME16b] L. Ma and M. Ebrahimi (2016). A Flexible Approach to 2D-3D Image Registration. SIAM International Conference on Imaging Science. Albuquerque, New Mexico, USA (Conference Abstract)
  • [MEP16b] L. Ma, M. Ebrahimi, and M. Pop (2016). Subject-specific patch-based denoising for contrast-enhanced cardiac MR images. Imaging Network Ontario (ImNO) symposium. Toronto, Ontario, Canada (Conference Proceedings)
  • [NE16] E. Ng and M. Ebrahimi (2016). A Discretize-then-Optimize Approach to Super-Resolution Reconstruction and Motion Estimation. In SIAM International Conference on Imaging Science, Albuquerque, New Mexico, USA (Conference Abstract)
  • [Ebr16] M. Ebrahimi (2016). Inverse Problems in Medical Image Processing. Invited speaker for the Medical Imaging Symposium, 13th Conference on Computer and Robot Vision (CRV). Victoria, BC, Canada (Oral Presentation)
  • [Le16] Q. Leung (2016). Graph Cuts Segmentation, Undergraduate Thesis in Applied and Industrial Mathematics, UOIT (BSc Thesis)

2015

  • [Ebr15] M. Ebrahimi (2015). Inverse Problems in Medical Image Processing. Invited presentation, Sunnybrook Medical Image Analysis Laboratories (SMIAL) seminar series (Oral Presentation)

  • [EM15] M. Ebrahimi and L. Ma (2015). Mathematical Challenges in Medical Image Registration. Applied Mathematics, Modeling and Computational Science- Canadian Applied and Industrial Mathematics Society (AMMCS-CAIMS) Congress, [Invited Speaker to the Modeling and Simulation in Medicine and Biology Minisymposium]. Waterloo, Ontario, Canada (Oral Presentation)
  • [NE15a] E. Ng and M. Ebrahimi (2015). A Discretize-then-Optimize Approach to Super-Resolution Reconstruction and Motion Estimation. Journal of Computational Vision and Imaging Systems Vol. 1, Open Access (Journal Article)
  • [NE15] E. Ng and M. Ebrahimi (2015). Coupled Multi-Frame Super-Resolution Reconstruction and Motion Estimation. UOIT student research showcase(Conference Abstract)

2014

  • [Ebr+14] M. Ebrahimi, P. Siegler, A. Modhafar, C. Holloway, D. Plewes, and A. Martel (2014). Using Surface Markers for MRI Guided Breast Conserving Surgery; a Feasibility Survey, Physics in Medicine and Biology, (59), pp. 1589–1605 (Journal Article)
  • [EK14] M. Ebrahimi and S. Kulaseharan (2014). Deformable Image Registration and Intensity Correction of Cardiac Perfusion MRI. Lecture Notes in Computer Science, Proceedings of Statistical Atlases and Computational Modeling of the Heart (STACOM), 13-20 (Conference Proceedings)

2013

  • [ELM13] M. Ebrahimi, A. Lausch, and A. L. Martel (2013). A Gauss-Newton Approach to Joint Image Registration and Intensity Correction. Computer Methods and Programs in Biomedicine. Vol.112(3) pp. 398-406. (Journal Article)
  • [Hil+13] M. L. Hill, J. G. Mainprize, A.-K. Carton, S. Muller, M. Ebrahimi, R. A. Jong, C. Dromain, and M. J. Yaffe (2013). Anatomical Noise in Contrast-Enhanced Digital Mammography. Part I. Single-Energy Imaging. Medical Physics, 40:051910. (Journal Article)

2012

  • [Ebr+12] M. Ebrahimi, P. Siegler, D. B. Plewes, and A. L. Martel (2012). Evaluating Thin Plate Spline Registration of the Breast in Two Supine Positions. Journal of Computer Assisted Radiology and Surgery, 7(S1). (Journal Article)
  • [Ebr12] M. Ebrahimi (2012). Evaluating Thin Plate Spline Registration of the Breast in Two Supine Positions. AMS Sectional Meeting, Special Session on Mathematical Image Processing. Rochester Institute of Technology, Rochester, NY, USA (Oral Presentation)

  • [Sie+12] P. Siegler, M. Ebrahimi, C. Holloway, G. Thevathasan, D. B. Plewes, and A. L. Martel (2012). Supine Breast MRI and Assessment of Future Clinical Applications. European Journal of Radiology, 81(S1). (Journal Article)
  • [CE12] N. Cahill and M. Ebrahimi (2012). Image Registration in Presence of Discontinuities. Fields-MITACs Industrial Problem Solving Workshop on Medical Imaging, Problem Presented. York University, Toronto, Ontario, Canada (Conference Abstract)

2011

  • [Ebr11] M. Ebrahimi (2011). Mathematical methods for breast image registration. MITACS-Fields Conference on the Mathematics of Medical Imaging. [Invited Speaker to the Special Session on Medical Imaging: Mathematical Methods and Industrial Applications]. Toronto, Ontario, Canada (Oral Presentation)

  • [LEM11] A. Lausch, M. Ebrahimi, and A. L. Martel (2011). Image Registration for Abdominal Dynamic Contrast Enhanced Magnetic Resonance Images. In Proceedings of 8th IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, Illinois, USA, pp. 561–565. (Conference Proceedings)

2010

  • [Ebr10b] M. Ebrahimi (2010). A PDE Approach to Coupled Super-Resolution with Non-Parametric Motion. In: SIAM International Conference on Imaging Science, [Invited Speaker to the Mini-Symposium on Variational Approaches and PDE’s for Imaging Tasks]. Chicago, Illinois, USA. (Oral Presentation)
  • [Ebr10a] M. Ebrahimi (2010). Inverse Problems in Imaging. Sunnybrook Medical Image Analysis Laboratories (SMIAL) Seminar, Sunnybrook health Sciences Centre. Toronto, Ontario, Canada (Conference Abstract)

2009

  • [Ebr09] M. Ebrahimi (2009). A Necessary and Sufficient Contractivity Condition for the Fractal Transform Operator. Journal of Mathematical Imaging and Vision, Vol.35 (3), pp. 186–192. (Journal Article)
  • [LaT+09] D. La-Torre, E. R. Vrscay, M. Ebrahimi, and M. F. Barnsley (2009). Measure-Valued Images, Associated Fractal Transforms, and the Affine Self-Similarity of Images. SIAM Journal on Imaging Sciences, Vol.2 (2), pp. 470–507. (Journal Article)
  • [EM09a] M. Ebrahimi, A. L. Martel (2009). A General PDE Framework for Registration of Contrast Enhanced Images. In: Lecture Notes in Computer Science, Proceedings of Medical Image Computing and ComputerAssisted Intervention (MICCAI). Ed. by D. Hawkes, D. Rueckert, and G. Z. Yang. Vol. 5761. London,UK: Springer-Verlag, pp. 811–819. (Conference Proceedings)
  • [EM09b] M. Ebrahimi and A. L. Martel (2009). A PDE Approach to Coupled Super-Resolution with Non-Parametric Motion. Lecture Notes in Computer Science, Proceedings of Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Bonn, Germany (Conference Proceedings)
  • [EM09c] M. Ebrahimi and A. L. Martel (2009). Image Registration under Varying Illumination. In: Lecture Notes in Computer Science, Proceedings of Energy Minimization Methods inComputer Vision and Pattern Recognition (EMMCVPR). Ed. by D. Cremers, Y. Boykov, A. Blake, andF. R. Schmidt. Vol. 5681. Bonn, Germany: Springer-Verlag, pp. 303–316. (Conference Proceedings)
  • [EVM09] M. Ebrahimi, E. R. Vrscay, and A. L. Martel (2009). Coupled Multi-Frame Super-Resolution with Diffusive Motion Model and Total Variation Regularization. In: Proceedings of the International Work-shop on Local and Non-Local Approximation in Image Processing (LNLA). Ed. by J. Astola, K. Egiazarian, and V. Katkovnik. Tampere International Center for Signal Processing. Tuusula, Finland, pp. 62–69. (Conference Proceedings)
  • [Ebr09] M. Ebrahimi (2009). Inverse Problems and Self-Similarity in Imaging. In: Interdisciplinary Workshop on Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Banff International Research Center, Banff, Alberta, Canada. (Oral Presentation)

2008

  • [EV08] M. Ebrahimi and E. R. Vrscay (2008). Non-Local Approaches to Image and Video Resolution Enhancement. In: SIAM International Conference on Imaging Science. San Diego, California, USA. (Conference Proceedings)
  • [EV08a] M. Ebrahimi, E. R. Vrscay (2008). Examining the Role of Scale in the Context of the Non-Local-Means Filter. In: Lecture Notes in Computer Science, Proceedings of the International Conference on Image Analysis and Recognition ICIAR, Povoa de Varzim, Portugal, pp. 170–181. (Conference Proceedings)
  • [EV08b] M. Ebrahimi, E. R. Vrscay (2008). Multi-Frame Super-Resolution with no Explicit Motion Estimation. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV. Las Vegas, Nevada, USA, pp. 455–459. (Conference Proceedings)

  • [EV08c] M. Ebrahimi, E. R. Vrscay (2008). Self-Similarity in Imaging, 20 Years after “Fractals Everywhere”. In: Proceedings of The International Workshop on Local and Non-Local Approximation in Image Processing, LNLA. Lausanne, Switzerland, pp. 165–172. (Conference Proceedings)
  • [OEW08] J. Orchard, M. Ebrahimi, and A. Wong (2008). Efficient Non-Local-Means Denoising using the SVD. Proceedings of The IEEE International Conference on Image Processing (ICIP). San Diego, California, USA (Conference Proceedings)

2007

  • [EV07a] M. Ebrahimi and E. R. Vrscay (2007). Nonlocal-means Single-Frame Image Zooming. Proceedings in Applied Mathematics and Mechanics (PAMM), 6th International Congress on Industrial and Applied Mathematics, ICIAM. Zurich, Switzerland (Conference Proceedings)

  • [EV07b] M. Ebrahimi, E. R. Vrscay (2007). Regularization Schemes involving Self-similarity in Imaging Inverse Problems. In: Proceedings of Applied Inverse Problems (AIP), DOI:10.1088/1742-6596/124/1/012021, 12 pages. (Conference Proceedings)
  • [EV07c] M. Ebrahimi, E. R. Vrscay (2007). Solving the Inverse Problem of Image Zooming using “Self-Examples”. In: Lecture Notes in Computer Science, Proceedings of the International Conference on Image Analysis and Recognition ICIAR. Vol. 4633, pp. 117–130. (Conference Proceedings)

2006

  • [EV06a] M. Ebrahimi, E. R. Vrscay (2006). Fractal Image Coding as Projections onto Convex Sets, In: Lecture Notes in Computer Science, Proceedings of the International Conference on Image Analysis and Recognition ICIAR. Berlin, Heidelberg, pp. 493–506. (Conference Proceedings)
  • [EV06b] M. Ebrahimi and E. R. Vrscay (2006). Regularized Fractal Image Decoding. Proceedings of the Canadian Conference on Electrical and Computer Engineering CCECE. Ottawa, Canada (Conference Proceedings)
  • [EV06a] M. Ebrahimi and E. R. Vrscay (2006). Generalized Fractal Image Coding using Projections onto Convex Sets. SIAM International Conference on Imaging Science. Minneapolis, Minnesota, USA (Conference Abstract)
  • [EV06b] M. Ebrahimi and E. R. Vrscay (2006). Regularized Fractal Image Decoding. CAIMS-MITACS Joint Annual Conference. York University, Toronto, Ontario, Canada (Conference Abstract)

2003

  • [Bah+03] W. Bahsoun, P. Gora, A. Boyarsky, and M. Ebrahimi (2003). Filtering Entropy , Physica D: Nonlinear Phenomena, Vol. 183(3-4), pp. 260–272.(Journal Article)

Canadian Breast Cancer Foundation (CBCF) Postdoctoral Fellowship

NSERC Postdoctoral Fellowship (2009-2010)

Canadian Applied and Industrial Mathematics Society

Society for Industrial and Applied Mathematics

Medical Image Computing and Computer Assisted Intervention (MICCAI)

  • Advanced Topics in Mathematical Modelling (Mathematics of Medical Imaging) (MCSC 6210G)
    This course builds on the core course Mathematical Modelling and elaborates on some of its topics in greater detail. In addition, it introduces a variety of special topics in applied mathematics with a focus on industrial and natural processes and phenomena. The topics are chosen according to the needs and demands of the students and the available faculty resources. Topics and application may include auto-correlation of data sets, bifurcations in time-series, embedding time series, modelling stochastic systems, perturbation methods for partial differential equations, travelling wave phase plane, advanced reaction-diffusion phenomena and transition layers, Hausdorff measures, fractal dimension, Belousov-Zhabotinsky reaction, analysis of heartbeat time-series, fractals in science and medicine, chaotic dynamics in symmetric coupled cell systems, time series in the stock market and other financial products.
  • Numerical Analysis (MCSC 6020G)
    Numerical analysis is the study of computer algorithms developed to solve the problems of continuous mathematics. Students taking this course gain a foundation in approximation theory, functional analysis and numerical linear algebra from which the practical algorithms of scientific computing are derived. A major goal of this course is to develop skills in analyzing numerical algorithms in terms of their accuracy, stability and computational complexity. Topics include best approximations, least squares problems (continuous, discrete and weighted), eigenvalue problems and iterative methods for systems of linear and nonlinear equations. Demonstrations and programming assignments are used to encourage the use of available software tools for the solution of modelling problems that arise in physical, biological, economic or engineering applications
  • Calculus II (MATH 1020U)
    A continuation of Calculus I or Introductory Calculus emphasizing integral calculus: problem solving, calculations and applications. Applications to volumes, arc length, polar co-ordinates and functions of two or more variables. Multivariable calculus: partial derivatives, differential equations, Taylor and MacLauren series, double integrals.
  • Real Analysis (MATH 3020U)
    This course provides the foundation for real analysis, and prepares students for other branches of mathematics, mathematical statistics and quantum mechanics. Students study the construction of real and complex number systems; partial and total order relations; countable and uncountable sets; mathematical induction and other techniques of proof; numerical sequences and series; absolute and conditional convergence; basic topological notions in a metric space; continuous functions; continuity and compactness; continuity and connectedness; uniform continuity; sequences and series of functions; uniform convergence; the Riemann-Stieltjes integral; rectifiable curves; fixed points and the contraction principle; introduction to one-dimensional discrete dynamical systems.
  • Complex Analysis (MATH 3060U)
    Introduces some classical theorems and applications of complex analysis. Students study basic properties of complex numbers; the Cauchy-Riemann equations; analytic and harmonic functions; complex exponential and logarithmic functions; branches of multi-valued functions; contour integrals; the Cauchy-Goursat Theorem and the Cauchy Integral Formula; the maximum modulus principle; Taylor and Laurant series; the residue theorem.
  • Optimization (MATH 3040U)
    This course introduces linear and nonlinear optimization problems and offers the concepts and techniques required for their solution. Students study: linear programming (simplex method, duality, integer programming), nonlinear programming (Lagrange multipliers, KKT optimality conditions), approximation techniques (line search methods, gradient methods, conjugate gradient methods), variational problems (Euler-Lagrange equation), dynamic programming, and optimal control.
  • Mathematical Modelling (MATH 3050U)
    This course provides an overview of the mathematical modelling of discrete, continuous and stochastic systems. Problems arising in physics, chemistry, biology, industry, economics, and social science serve as examples to demonstrate model development, implementation, solution and analysis. Methods of solution and physical interpretation of results are stressed. Computer software such as Maple and Matlab will be used to facilitate the modelling process.