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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

BMET5933: Biomedical Image Analysis

Biomedical imaging technology is a fundamental element of both clinical practice and biomedical research, enabling the visualisation of biological characteristics and function often in a non-invasive fashion. The advancement of digital scanning technologies alongside the development of computational tools has driven significant progress in medical image analysis tools that support clinical decisions and the analysis of data from biological experiments. The focus of this unit will be the development of fundamental computational skills and knowledge in biomedical imaging, including data acquisition, formats, visualisation, segmentation, feature extraction, and machine learning based image analysis. On completion of this unit, students will be able to engineer and develop solutions for different biomedical imaging tasks encountered across a variety of use cases: clinical practice (e.g., computerised disease detection and diagnosis), research (e.g., cell video analysis), and industry (e.g., fabrication of customised implants from patient image data).

Code BMET5933
Academic unit Biomedical Engineering
Credit points 6
Assumed knowledge:
An understanding of biology (1000-level), experience with programming (ENGG1801, ENGG1810, BMET2922 or BMET9922).

At the completion of this unit, you should be able to:

  • LO1. Understand the context, sources, and applications of biomedical imaging and image analysis.
  • LO2. Understand and apply a variety of fundamental image processing techniques across a variety of biomedical imaging contexts.
  • LO3. Appraise the effectiveness of different biomedical image analysis algorithms and tools using standard performance metrics.
  • LO4. Create solutions for prediction and classification tasks in biomedical imaging through the combination of image processing and machine learning techniques.
  • LO5. Implement prototype software solutions for biomedical image analysis tasks using existing software packages and libraries.
  • LO6. Assess the strengths and limitations of emerging biomedical image analysis algorithms from research literature.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.