Course Outline (W2024)

BME872: Biomedical Image Analysis

Instructor(s)Alice Rueda [Coordinator]
Office: EPH 417
Phone: TBA
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 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. (1d), (4a), (4b), (5a)
  2. 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)
  3. 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)
  4. Students will perform research on an image analysis algorithm that has practical utility in hospitals. They will identify applications of their technology. (8b)
  5. 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
1.0 hours of lab per week for 12 weeks
1.0 hours of tutorial per week for 12 weeks

Teaching AssistantsTBA
Course Evaluation
Midterm Exam 25 %
Final Exam 45 %
Lab1/Lab2/Lab3 20 %
Project 10 %
TOTAL:100 %

Note: In order for a student to pass a course, a minimum overall course mark of 50% must be obtained. In addition, for courses that have both "Theory and Laboratory" components, 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 above for details on the Theory and Laboratory components (if applicable).

ExaminationsMidterm exam covers all material covered in class up until the examination. Midterm is scheduled for week 7.
 The final exam will cover all course material.
Other Evaluation InformationLaboratory: All labs require final write-ups and submission of working code to generate your results. Requested analysis, images and information that will be assessed are included in the lab description. 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 and lab reports will be due that same week. Images and experimental details will be given on the course website. You may work in partners for the labs (two maximum). The labs will consist of theoretical and practical parts and will require the use of Matlab.
 Project: The project details, data and requirements will be uploaded to the course website. There is a four page (conference-style) write up, demo, and presentation that are assessed. During the last weeks in the semester, the TA will ask you to demo your code, and ask questions about its operation and results. You may work in partners (two maximum). 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 these as well as additional problems without looking at the solutions first.
 Labs/project will be made available on the course web. 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




Midterm covering lectures 1-6



Chapter 4

2D Fourier Transform and Sampling



Chapter 4

Frequency Domain Filtering



Chapter 5 Sections 5.1-5.3, 5.11

Image Restoration



Class Notes

Feature Extraction, Segmentation and Classification



Project Presentations

Laboratory(L)/Tutorials(T)/Activity(A) 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

University Policies & Important Information

Students are reminded that they are required to adhere to all relevant university policies found in their online course shell in D2L and/or on the Senate website

Refer to the Departmental FAQ page for furhter information on common questions.

Important Resources Available at Toronto Metropolitan University


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