Student-facing repository for Medical Image Processing at Concordia University.
Official course page: https://users.encs.concordia.ca/~hrivaz/courses/Med_Imag_444_2026/
The official course page and Moodle remain the authoritative sources. If anything here differs from an instructor announcement, follow the instructor/Moodle version.
- Course: ELEC 444 / ELEC 6661 — Medical Image Processing
- Department: Electrical and Computer Engineering, Concordia University
- Instructor: Prof. Hassan Rivaz
- Time: Thursdays, 2:45–5:15 PM
- Undergraduate: ELEC 444, 3 credits
- Graduate: ELEC 6661, 4 credits
- PODs: Yasaman and Omid — see Moodle for POD timing/contact information
The course introduces principles and algorithms for processing and analyzing medical images, including:
- X-ray, CT, MRI, ultrasound and nuclear imaging
- Image representation and manipulation
- Convolution and aliasing
- Denoising and edge detection
- Machine learning for medical imaging
- Segmentation, RANSAC and k-means
- Medical image registration and similarity metrics
- Deep learning, explainable AI, RNN/LSTM
- Transformers and Vision Transformers in medical imaging
| Component | ELEC 444 | ELEC 6661 |
|---|---|---|
| Assignments | 10% | 10% |
| Project | 20% | 40% |
| Midterm | 20% | 20% |
| Final exam | 50% | 30% |
There are three MATLAB assignments and one final project. The final project can be implemented in a language appropriate to the project.
- Project due: December 1, 2026 at 11:59 PM
- Student presentations: December 3, 2026 (Week 12)
- All undergraduate and graduate students must be present for the final class/project presentations.
See Project Guidelines for the recommended workflow and repository structure.
| Assignment | Posted | Due | Points |
|---|---|---|---|
| 1. Matrix and image manipulation | Sep. 10, 5 PM | Sep. 23, 11:59 PM | 1.0 |
| 2. Detecting edges of brain MRI | Oct. 8, 5 PM | Oct. 19, 11:59 PM | 4.5 |
| 3. Segmentation of brain MRI | Nov. 5, 5 PM | Nov. 18, 11:59 PM | 4.5 |
| Week / Date | Topic |
|---|---|
| Week 1 — Sep. 10 | Logistics; introduction to X-ray, CT and nuclear imaging |
| Week 2 — Sep. 17 | Ultrasound and MR imaging; images in MATLAB |
| Week 3 — Sep. 24 | Convolution; aliasing in medical images |
| Week 4 — Oct. 1 | Ultrasound imaging and contrast agents; surgical robotics |
| Week 5 — Oct. 8 | Denoising; edge detection |
| Oct. 15 | Reading week |
| Oct. 22 | Machine learning in medical imaging |
| Week 7 — Oct. 29 | Midterm in class |
| Week 8 — Nov. 5 | Segmentation; RANSAC; k-means |
| Week 9 — Nov. 12 | Registration; SSD, NCC, correlation ratio, joint entropy, mutual information |
| Week 10 — Nov. 19 | From linear filters to deep learning; explainable AI; RNN; LSTM |
| Week 11 — Nov. 26 | Transformers, ViT, breast lesion segmentation, AI in ultrasound |
| Week 12 — Dec. 3 | Student presentations |
The project is an opportunity to apply course concepts to a real medical-imaging problem.
The official course page states that undergraduate students may:
- implement a research paper, or
- complete a project based on RANSAC algorithms.
Students choosing the RANSAC option should follow the specific submission/report requirements stated by the instructor.
Graduate students are expected to complete a significantly more demanding project, consistent with the additional course credit and project weight.
- Pick a clearly defined medical-imaging problem.
- Select a public dataset or a dataset approved by the instructor.
- Identify a reproducible baseline.
- Reproduce the baseline before adding your own idea.
- Introduce a meaningful extension, comparison, ablation or improvement.
- Evaluate with suitable quantitative metrics and qualitative examples.
- Keep the code reproducible and document dependencies and commands.
- Prepare a clear final presentation and project page/report according to course instructions.
A starter project structure is included in project-template/.
The official course page contains an extensive dataset list covering MRI, CT, ultrasound, chest X-ray, mammography, OCT/OCTA and endoscopy.
Start here:
medical-image-processing-2026/
├── README.md
├── docs/
│ ├── PROJECT_GUIDELINES.md
│ └── GITHUB_PAGES_GUIDE.md
├── project-template/
│ ├── README.md
│ ├── requirements.txt
│ ├── src/
│ ├── notebooks/
│ ├── results/
│ └── docs/
├── resources/
│ ├── DATASETS.md
│ └── USEFUL_TOOLS.md
└── .gitignore
Textbook
Wolfgang Birkfellner, Medical Image Processing, CRC Press, 2014.
Reference book
Geoff Dougherty, Medical Image Processing, Cambridge University Press, 2009.
For course policy, deadlines, grading and official submission instructions, use the official course page and Moodle. For project implementation questions, use the POD support information provided on Moodle.