Course Outline (W2020)

BME872: Biomedical Image Analysis

Instructor(s)April Khademi [Coordinator]
Office: ENG428
Phone: (416) 979-5000 x 4037
Email: akhademi@ryerson.ca
Office Hours: TBA
Calendar DescriptionIntroduces 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.
PrerequisitesBME 229 and BME 772




Compulsory Text(s):
  1. R.C. Gonzalez & R.E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018.
Reference Text(s):
  1. Medical Image Analysis, second edition, by Atam Dhawan, WILEYISBN: 978-0-470-62205-6.
  2. Medical Imaging, Signals and Systems, by J. Prince and J. LinksISBN: 0-13-065353-5.
Learning Objectives (Indicators)  

At the end of this course, the successful student will be able to:

  1. Students will learn to treat digital images as 2D mathematical functions, and to use mathematics to manipulate digital images. Some mathematical methods investigated include convolution, Fourier analysis, filtering, histogram analysis, image enhancement, linear and non-linear systems analysis, and more. (1b)
  2. Students will learn how to formulate an image analysis algorithm from first principles (i.e. block diagrams, mathematics) and learn how to implement, debug and test functionality in Matlab. They will learn how to optimize algorithms for medical imaging. (1c), (1d), (4a), (4b), (5a)
  3. Students will learn about sources of noise in medical images (i.e. acquisition noise, low contrast), and how to reduce their impact through denoising and enhancement. (2a)
  4. Students will learn how to design and implement automated medical analysis algorithms on clinical imaging data using Matlab. They will also learn how to measure success of algorithms, and how to improve designs. (3a), (3b), (5b)
  5. Students will perform research on an image analysis algorithm that has practical utility in hospitals. They will identify applications of their technology. (8b)
  6. Students will learn how to manage their course project. Students will understand the important aspects of the project management, such as time-line, progress report, final delivery of the product, and the deadlines. Since the project works with medical images, the students will also be expected to understand the impact of their designs on healthcare. (11b)

NOTE:Numbers in parentheses refer to the graduate attributes required by the Canadian Engineering Accreditation Board (CEAB).

Course Organization

3.0 hours of lecture per week for 13 weeks
2.0 hours of lab/tutorial per week for 12 weeks

Teaching AssistantsTBA
Course Evaluation
Midterm Exam 25 %
Final Exam 45 %
Lab1/Lab2/Lab3 (3 x 5%) 15 %
Project 15 %
TOTAL:100 %

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.

ExaminationsMidterm 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 InformationLaboratory:
 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 InformationPractice 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.

Course Content



Chapters /

Topic, description



Chapter 1 All Sections

Introduction to Medical Image Analysis



Chapter 2 All Sections

Digital Image Formation



Chapter 3 Sections 3.1-3.3

Intensity Transforms



Chapter 3 Sections 3.4-3.7

Spatial Filtering



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

Image Restoration



Class Notes

Feature Extraction, Segmentation and Classification



Project Presentations | Demos

Laboratory/Tutorials/Activity Schedule






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

Policies & Important Information:

  1. Students are required to obtain and maintain a Ryerson e-mail account for timely communications between the instructor and the students;
  2. Any changes in the course outline, test dates, marking or evaluation will be discussed in class prior to being implemented;
  3. Assignments, projects, reports and other deadline-bound course assessment components handed in past the due date will receive a mark of ZERO, unless otherwise stated. Marking information will be made available at the time when such course assessment components are announced.
  4. Refer to our Departmental FAQ page for information on common questions and issues at the following link: https://www.ee.ryerson.ca/guides/Student.Academic.FAQ.html.

Missed Classes and/or Evaluations

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.

  1. Health certificates - If a student misses the deadline for submitting an assignment, or the date of an exam or other evaluation component for health reasons, they should notify their instructor as soon as possible, and submit a Ryerson Student Health Certificate AND an Academic Consideration Request form within 3 working days of the missed date. Both documents are available at https://www.ryerson.ca/senate/forms/medical.pdf.. If you are a full-time or part-time degree student, then you submit your forms to your own program department or school;
  2. Religious, Aboriginal and Spiritual observance - If a student needs accommodation because of religious, Aboriginal or spiritual observance, they must submit a Request for Accommodation of Student Religious, Aboriginal and Spiritual Observance AND an Academic Consideration Request form within the first 2 weeks of the class or, for a final examination, within 2 weeks of the posting of the examination schedule. If the requested absence occurs within the first 2 weeks of classes, or the dates are not known well in advance as they are linked to other conditions, these forms should be submitted with as much lead time as possible in advance of the absence. Both documents are available at www.ryerson.ca/senate/forms/relobservforminstr.pdf. If you are a full-time or part-time degree student, then you submit the forms to your own program department or school;
  3. Academic Accommodation Support - Before the first graded work is due, students registered with the Academic Accommodation Support office (AAS - www.ryerson.ca/studentlearningsupport/academic-accommodation-support) should provide their instructors with an Academic Accommodation letter that describes their academic accommodation plan.

Academic Integrity

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:

  1. A grade reduction for the work, ranging up to an including a zero on the work (minimum penalty for graduate work is a zero on the work);
  2. A grade reduction in the course greater than a zero on the work. (Note that this penalty can only be applied to course components worth 10% or less, and any additional penalty cannot exceed 10% of the final course grade. Students must be given prior notice that such a penalty will be assigned (e.g. in the course outline or on the assignment handout);
  3. An F in the course;
  4. More serious penalties up to and including expulsion from the University.

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:

  1. Slides
  2. Lecture notes
  3. Presentation materials used in and outside of class
  4. Lab manuals
  5. Course packs
  6. Exams

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/).

Important Resources Available at Ryerson

  1. The Library (https://library.ryerson.ca/) provides research workshops and individual assistance. Inquire at the Reference Desk on the second floor of the library, or go to library.ryerson.ca/guides/workshops
  2. Student Learning Support(https://www.ryerson.ca/studentlearningsupport) offers group-based and individual help with writing, math, study skills and transition support, and other issues.