Image Processing and Analysis in Diagnostic Imaging
RADDI 514 A1

(Fall Term 2019)


General Information

Instructor: Kumar Punithakumar
Tel: (780) 407-1871

Office: RTF 4-103A
Office hours: By appointment only

Lectures: Tuesdays and Thursdays, 10:30 AM - 11:50 AM at ED B 76

Course Description:
The course aims to cover medical image processing and analysis techniques, including de-noising, registration, segmentation, 3D reconstruction, applicable in diagnostic imaging modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). Clinical examples in cardiovascular, musculoskeletal and brain imaging will be discussed.

Linear algebra and knowledge in MATLAB programming or consent of the department.

Lecture Notes

Lecture date Topic Slide Extras
W1: Sep 3 Introduction to medical imaging L01  
W1: Sep 5 The basics of medical imaging L02  
W2: Sep 10 Common Imaging Modalities L03  
W2: Sep 12 Image storage and transfer L04  
W3: Sep 17 Introduction to image enhancement L05  
W3: Sep 19 Resolution and edge enhancement L06  
W4: Sep 24 Noise reduction L07  
W4: Sep 26 Feature extraction L08 Assignment 1
W5: Oct 1 Introduction to image segmentation L09  
W5: Oct 3 Segmentation in feature space L10  
W6: Oct 8 Active contours L11  
W6: Oct 10 Graph cuts L12  
W7: Oct 15 Evaluation Frameworks for Image Segmentation L13 Assignment 2
W7: Oct 17 Introduction to image registration L14  
W8: Oct 22 Cardiac imaging
Guest lecture by Dr. Michelle Noga
W8: Oct 24 Nonrigid image registration methods L15  
W9: Oct 29 Introduction to Machine Learning L16 Assignment 3
W9: Oct 31 Convolutional Neural Networks L17  
W10: Nov 5 Autoencoders and RNNs L18  
W10: Nov 7 Course project proposal presentations    
W11: Nov 12 to Nov 15 Reading week    
W12: Nov 19 No class   Assignment 4
W12: Nov 21 Machine Learning in Medical Image Analysis Applications    
W13: Nov 26 Musculoskeletal imaging
Guest lecture by Dr. Jacob Jaremko
W13: Nov 28 Neuroimaging
Guest lecture by Dr. Ravi Bhargava
W14: Dec 3 No class    
W15: Dec 5 Final course project presentations    

Course Grade:
Assignments: 50% (Assignment 1 - 4)
Final project: 50%

Reading list
The following books are recommended for additionally support the course, but they are not essential.