ELEC 498/691 Medical Image Processing
Department of Electrical and Computer Engineering
Concordia University
Instructor: Hassan Rivaz
Course description: This course covers the principles and algorithms used in the processing and analysis of medical images. Topics include denoising, machine learning, image registration and similarity metrics. Image analysis methods on the most common medical imaging modalities (X-ray, MRI, CT, ultrasound) will be covered. Projects and assignments will provide students experience working with actual medical imaging data.
Credits: Undergraduate course ELEC 498: 3 credits. Graduate course ELEC 691: 4 credits. The graduate course requires carrying out a significantly more demanding project, which accounts for the exra credit.
Location & time: FG B055 SGW, Tuesdays 5:45PM to 8:15PM
Text Book: Medical Image Processing, 2014, By Wolfgang Birkfellner, CRC Press, ISBN: 978-1466555570.
Reference Book: Medical Image Processing, 2009, By Geoff Dougherty, Cambridge University Press, ISBN: 9780521860857.
Assignments: Three assignments + one final project (in MATAB or another language based on student preference). Marker: Mr. Majid Masoumi. Email: ma_masou AT encs.concordia.ca
Grading:
Undergraduate (3 credits)Graduate (4 credits)
Assignments1010
Project2040
Midterm exam2020
Final exam5030

AssignmentsPostDue Points
Assignment 1Tuesday Sept 20, 9AMTuesday Sept 27, 9AM 2 points
Assignment 2Tuesday Oct 11, after midterm in classTuesday Oct 18, 9AM4 points
Assignment 3Tuesday Oct 25, 9AMTuesday Nov 1, 9AM4 points

Outline (all classes are on Tuesday, except for the class on Oct 20. There is no class on Tuesday Oct 18):
Week Topic
1, Sep 6 Logistics, introduction to X-ray, CT and nuclear imaging
2, Sep 13 Introduction to ultrasound and Magnetic Resonance (MR) imaging, images in Matlab
3, Sep 20 More on MR, convolution, aliasing in medical images
4, Sep 27 Denoising techniques in medical imaging, edge detection in medical images
5, Oct 4 Machine learning in medical imaging
6, Oct 11 Midterm in class
7, Oct 20, Dr. Grova (note the date: Thursday not Tuesday) Electroencephalography (EEG), Magnetoencephalography (MEG), inverse problem
8, Oct 25 Introduction to image registration, similarity metrics (sum of squared differences, normalized cross correlation), Correlation ratio, joint entropy
9, Nov 1 Continuing similarity metrics (Mutual Information, MI). Case study 1: Computer vision and motion estimation in ultrasound elastography
10, Nov 8 Case study 1: Computer vision and motion estimation in ultrasound elastography
11, Nov 15 Case study 2: Registration (fusion) of medical images
12, Nov 22 Review
13, Nov 29 Student presentations
Prerequisites:

 Undergraduate students: ELEC 364 or ELEC 342 Signals and Systems or instructor's permission. Graduate students: none.

Database of images possibly useful for projects:

 1) BrainWeb MRI link ,
2) Ultrasound RF data link ,
3) 3D MRI and ultrasound link ,
4) More 3D MRI and ultrasound link ,
5) Microscopy link
6) CT link