Medical Image Acquisition and Processing
Description:
Overview
This course provides the fundamental concepts of modern medical image acquisitions, processing and analysis. We start with the basics of a digital image and how this can be represented and characterised in terms of the actual data values and metadata. We then look at various types of operations or image manipulation that can be applied to extract relevant information from both 2D and 3D medical images and how to analyse and store them. The course is highly hands-on. Participants are expected to have basic Python programming skills and experience with Jupiter notebooks. The course offers tasks and opportunities for self-assessment. We provide exercises at the end of each topic, and it is strongly recommended the attendees go through them and understand which algorithms are implemented in Python each time to analyse imaging data and solve real-world medical image analysis problems. The team of instructors will offer support and feedback to the students throughout the course and the challenges. Topics include digital images and the anatomy of the Image working with 2-D images. Medical image acquisition of various diagnostic imaging modalities. Introduction to medical image data (e.g., different image types, file formats, higher dimensional arrays, masks, point sets, meshes and visualisation), fundamental 3-D medical image analysis. What are registration and segmentation and hands-on experience in various tasks (e.g., image processing, spatial coordinate transformation, morphological operation). A Structural MRI example will be demonstrated. We offer many Jupyter Notebooks and algorithms that can be used and adapted to the user’s research in medical image analysis.
Prerequisites:
The course has taught materials and it is also highly hands-on. Participants are expected to have basic Python programming skills and experience on Jupiter notebooks. While prior exposure to programming in Python is desirable Desirable Python skills, for programming novices, we might be able to provide a 2 day course in Python prior to Medical imaging. We teach highly transferable skills and it is suitable for students without much programming experience in Python and libraries such as NumPy, Pandas, Scipy, who wish to learn Python programming and use the Notebooks to their research. We provide a full support by the facilitators who help the attendees with the hands-on components. A good knowledge of mathematics is required, such as algebra, probability and statistics.
Leaning Outcomes
By taking this course, the attendees should gain familiarity and experience in:
- Digital imaging data structures and processing techniques.
- Understanding the Medical diagnostic modalities, 2-D and 3-D medical image acquisitions.
- Using Jupyter Notebooks
- Practicing with visualisation, programming and analysis of 2-D and 3-D medical images.
Duration of the course:
This course is meant to be a two-day course with five hours of learning each day. Depending on the needs of attendees, we may offer additional support and training around Python prior to the course.
