|Instructor(s)||April Khademi [Coordinator]|
Phone: (416) 979-5000 x 4037
Office Hours: TBA
|Calendar Description||Introduces the fundamental principles of medical image analysis and visualization. Focuses on the processing and analysis of ultrasound, MR, and X-ray images for the purpose of quantification and visualization to increase the usefulness of modern medical image data. Includes image perception and enhancement, 2-D Fourier transform, spatial filters, segmentation, and pattern recognition.|
|Prerequisites||BME 229 and BME 772|
|Learning Objectives (Indicators)|
At the end of this course, the successful student will be able to:
NOTE:Numbers in parentheses refer to the graduate attributes required by the Canadian Engineering Accreditation Board (CEAB).
3.0 hours of lecture per week for 13 weeks
Note: In order for a student to pass a course with "Theory and Laboratory" components, in addition to earning a minimum overall course mark of 50%, the student must pass the Laboratory and Theory portions separately by achieving a minimum of 50% in the combined Laboratory components and 50% in the combined Theory components. Please refer to the "Course Evaluation" section for details on the Theory and Laboratory components.
|Examinations||Midterm exam covers all material covered in class up until the examination. Closed book exam. Midterm is scheduled for Feb. 24, 2020 - in class.|
The final exam will cover all course material. Closed book exam.
|Other Evaluation Information||Laboratory:|
All labs require final write ups and submission of working code used to generate your results. Requested analysis, images and information that will be assessed is included in the report. During lab times, the TA will ask you to demo your code, and ask questions about its operation and the results. Labs will be demonstrated to the TA during the last week of the lab. Lab reports are due the day of your lab section. All images and experiment details will be given on the course website. You may work in partners for the labs. The labs will consist of theoretical and practical parts and will require the use of Matlab.
The project details, data and requirements will be given in the beginning of the semester. There is a final four page conference paper write up, demo, and presentation that will be assessed. During the last weeks of lab in the semester, the TA will ask you to demo your code, and ask questions about its operation and the results. You may work in partners for the labs. The project is design oriented, and will consist of both theoretical and practical components learned from the course, and will require the use of Matlab.
|Other Information||Practice problems and their solutions will be provided on the course web page. These assignments will neither be collected nor graded; they are provided only as a study guide. You are strongly recommended to attempt to solve the problems on your own without looking at the solutions first. |
Labs/project will be made available on the course web. Due dates and instructions will be posted on the web site also. It is your responsibility to check these and download and submit your work online by the deadlines.
Chapter 1 All Sections
Introduction to Medical Image Analysis
Chapter 2 All Sections
Digital Image Formation
Chapter 3 Sections 3.1-3.3
Chapter 3 Sections 3.4-3.7
Chapter 4 Sections 3.4-3.7
2D Fourier Transform and Sampling
Chapter 4 Sections 4.7-4.9
Frequency Domain Filtering
Chapter 5 Sections 5.1-5.3, 5.11
Feature Extraction, Segmentation and Classification
Project Presentations | Demos
Medical Image Management, Histograms and Point Operations
Contrast Adjustment of Mammogram Images
Vessel Detection in Retinal Images using Edge Detection
Automated Image Quality Assessment in Medical Images
When possible, students are required to inform their instructors of any situation which arises during the semester which may have an adverse effect upon their academic performance, and must request any consideration and accommodation according to the relevant policies as far in advance as possible. Failure to do so may jeopardize any academic appeals.
Ryerson's Policy 60 (the Academic Integrity policy) applies to all students at the University. Forms of academic misconduct include plagiarism, cheating, supplying false information to the University, and other acts. The most common form of academic misconduct is plagiarism - a serious academic offence, with potentially severe penalties and other consequences. It is expected, therefore, that all examinations and work submitted for evaluation and course credit will be the product of each student's individual effort (or an authorized group of students). Submitting the same work for credit to more than one course, without instructor approval, can also be considered a form of plagiarism.
Suspicions of academic misconduct may be referred to the Academic Integrity Office (AIO). Students who are found to have committed academic misconduct will have a Disciplinary Notation (DN) placed on their academic record (not on their transcript) and will normally be assigned one or more of the following penalties:
The unauthorized use of intellectual property of others, including your professor, for distribution, sale, or profit is expressly prohibited, in accordance with Policy 60 (Sections 2.8 and 2.10). Intellectual property includes, but is not limited to:
For more detailed information on these issues, please refer to the Academic Integrity policy(https://www.ryerson.ca/senate/policies/pol60.pdf) and to the Academic Integrity Office website (https://www.ryerson.ca/academicintegrity/).