Nilanjan Ray, PhD

Professor, Faculty of Science - Computing Science


Professor, Faculty of Science - Computing Science
406 Athabasca Hall
9119 - 116 St NW
Edmonton AB
T6G 2E8


Area of Study / Keywords

Computer vision Image analysis Medical image processing Deep learning


I joined Computing Science, University of Alberta as an Assistant Professor in 2006. I was promoted to Associate Professor in 2013.


  • B.Engg., Mechanical Egnineering, Jadavpur University, India, 1995 
  • M.Tech., Computer Science, Indian Statistical Institute, India, 1997 
  • Ph.D., Electrical Engineering, University of Virginia, USA, 2003

Associate Editor

  • IEEE Transactions on Image Processing (2013-2017)
  • IET Image Processing (2016-2022)

General Co-chair

  • 2017 AI/GI/CRV Conference, Edmonton

Books Co-authored

  • Scott T. Acton and Nilanjan Ray, Biomedical image analysis: Tracking. Morgan & Claypool Publishers, 2006.
  • Scott T. Acton and Nilanjan Ray, Biomedical image analysis: Segmentation. Morgan & Claypool Publishers, 2009.



Computer vision and deep learning; image and video analysis, medical image processing


Medical image and video analysis and general computer vision problems including segmentation, registration, object detection and classification.


  • Pedestrian and vehicle counting
  • Microscopy image and video segmentation and registration
  • Breast cancer histopathology image analysis
  • Oilsand slurry image and video analysis
  • Non-invasive blood pressure monitoring
  • Brain tumor localization


CMPUT 206 - Introduction to Digital Image Processing

An introduction to basic digital image processing theory, and the tools that make advanced image manipulation possible for ordinary users. Image processing is important in many applications: editing and processing photographs, special effects for movies, drawing animated characters starting with photographs, analyzing and enhancing images captured by the Mars Rover or the Hubble telescope, and detecting suspects from surveillance cameras. Image processing concepts are introduced using tools like Photoshop and GIMP. Exposure to simple image processing programming with Java and MATLAB. This course is preparation for more advanced courses in the Digital Media area. Prerequisites: Any 100-level Computing Science course, plus knowledge of first-year level MATH, STAT; and introductory Java, C, or similar programming experience; or consent of the instructor or SCI 100. Open to students in the Faculty of Arts, Engineering and Sciences; others require consent of the instructor.

Winter Term 2021
CMPUT 307 - 3D Modeling and Animation

An introductory course on the theory and applications of computer based 3D modeling and animation. The course will cover a selection of topics from overview of tools supporting modeling and animation, automatically generating 3D models, and animation of skeleton based models through algorithms and software. Applications of 3D modeling and animation in games, virtual/augmented environments, movies, and emerging video transmission algorithms will be discussed. Prerequisites: Some background in image processing or graphics, e.g., CMPUT 206 or CMPUT 311; knowledge of first or preferably second-year level MATH/STAT, e.g., STAT 141/151/252 or 266, and MATH 214 or 225; experience in programming, e.g., CMPUT 174 or 100. Consent of the instructor needed if some background courses are lacking.

Winter Term 2021
MM 803 - Image and Video Processing

Quality assessment of image and video (or 3D data) is essential in many applications, which deliver educational content, medical images, games, movies, video-on-demand and so on. In order to generate high quality image and video, especially given the sheer volume of consumer demand and under constrained resources, e.g., time and bandwidth, it is necessary to understand the image and video processing pipeline from the initial creation limitations to the final display at the receiver. This course focuses on reviewing various image/video processing techniques, as well as the quality assessment metrics proposed in the literature. Sections offered at an increased rate of fee assessment; refer to the Tuition and Fees page in the University Regulations sections of the Calendar.

Fall Term 2021

Browse more courses taught by Nilanjan Ray


Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net
Author(s): Sayan Ghosal, Nilanjan Ray
Publication: Pattern Recognition Letters
Volume: 94
Page Numbers: 81–86
External Link:
MISTICA: Minimum Spanning Tree-based Coarse Image Alignment for Microscopy Image Sequences
Author(s): Nilanjan Ray, Sara McArdle, Scott T. Acton, Klaus Ley
Publication: IEEE Journal of Biomedical and Health Informatics.
Volume: 20
Issue: 6
Page Numbers: 1575 - 1584
External Link:
Quick detection of brain tumors and edemas: A bounding box method using symmetry
Author(s): Baidya Nath Saha, Nilanjan Ray, Russell Greiner, Albert Murtha, Hong Zhang
Publication: Computerized medical imaging and graphics
Volume: 36
Issue: 2
Page Numbers: 95-107
External Link:
Robust people counting using sparse representation and random projection
Author(s): Homa Foroughi, Nilanjan Ray, and Hong Zhang
Publication: Pattern Recognition
Volume: 48
Issue: 10
Page Numbers: 3038-3052
External Link:
Unique people count from monocular videos
Author(s): Satarupa Mukherjee, Stephani Gil, Nilanjan Ray
Publication: The Visual Computer
Volume: 31
Issue: 10
Page Numbers: 1405-1417
External Link: