Course Title: Biomedical Signal and Image Processing

Part A: Course Overview

Course Title: Biomedical Signal and Image Processing

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:

Please read the Student website for additional requirements of in-person attendance: 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 


Course Code




Learning Mode

Teaching Period(s)


City Campus


125H Electrical & Computer Engineering


Sem 2 2016


Bundoora Campus


172H School of Engineering


Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 1 2021,
Sem 1 2022

Course Coordinator: Prof. Margaret Lech

Course Coordinator Phone: +61 3 9925 1028

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

It is desirable but not compulsory to have completed the following courses or equivalent before commencing this course:

  • EEET2369 Signals and Systems 1
  • EEET2494 Biomedical Signal Analysis

It is also desirable to have basic MATLAB, Python or C/C++ programming skills.

Course Description

Biomedical Signal and Image Processing will introduce you to biomedical applications of image enhancement, image restoration, adaptive filtering of audio signals, and machine learning.

The theory presented in the pre-recorded lectures will be applied to solve practical problems in laboratory sessions. The laboratory assignments will introduce advanced biomedical imaging and analysis methods using MATLAB biomedical signal processing tools.

Topics to be investigated include:

  1. Introduction to biomedical image processing
  2. Colour images
  3. Image filtering
  4. Image enhancement and de-blurring
  5. Image restoration
  6. Image segmentation and edge detection
  7. Adaptive filtering and source separation
  8. Hearing aids
  9. Machine learning and unsupervised clustering

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes of the Bachelor of Engineering (Honours):

1.3 In-depth understanding of specialist bodies of knowledge within the engineering discipline.

2.1 Application of established engineering methods to complex engineering problem-solving.

2.2 Fluent application of engineering techniques, tools and resources.

On completion of this course, you should be able to:

  1. Explain basic concepts of biomedical image processing
  2. Explain principles of image enhancement, segmentation, filtering, and restoration
  3. Explain principles of hearing aids design
  4. Explain and apply principles of audio signal separation in the context of medical applications
  5. Understand the principles of machine learning
  6. Apply image processing and machine learning techniques to basic biomedical applications
  7. Design and test image processing and machine learning algorithms
  8. Communicate your designs and test findings through oral and written reports.

Overview of Learning Activities

Learning activities include a mixture of pre-recorded lectures, practice tests, practical laboratory exercises, and progress assessment tests.

Key concepts and their application will be explained in PowerPoint presentations and video recordings, with practice examples used to demonstrate possible solutions.

Laboratory assignments will help you to develop problem-solving skills and provide systematic feedback on your progress.

Laboratory reports and progress tests are designed to develop communication skills through written reports and to guide you through a real-world design and verification methodology.

Overview of Learning Resources

All learning resources for this course are available on Canvas, the University Learning Management System.

These resources include:

  • Weekly step by step guide to your study on Canvas.
  • PowerPoint slides and video recordings of lectures.
  • Instructions and video recordings of lectures and assignments.
  • Access to MATLAB software.
  • Consultations and meetings with the Course Coordinator via video links.
  • Access to the University Library resources.

Laboratory assignment work has been designed to develop your group and communication skills through written reports and to guide you through a real-world design and verification methodology.

Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Lecture Test
Weighting: 20%
This assessment task supports CLOs 1-2

Assessment Task 2: Individual Written Assignments
Weighting: 35% (15% + 20%)
This assessment task supports CLOs 3-4

Assessment Task 3: Laboratory Team Projects Reports
Weighting: 25%
This assessment task supports CLOs 1-8

Assessment Task 4: Individual Final Timed Assessment
Weighting 20%
This assessment task supports CLOs 5-7

The final timed assessment will be a 1.5-hour test that may be taken any time within a 24-hour period.