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Credit: 1 course unit
Elective course
Catalog Description
This course covers the fundamentals of advanced quantitative image analysis that apply to all of the major and emerging modalities in biological/biomaterials imaging and in vivo biomedical imaging. While traditional image processing techniques will be discussed to provide context, the emphasis will be on cutting edge aspects of all areas of image analysis (including registration, segmentation, and high-dimensional statistical analysis). Significant coverage of state-of-the-art biomedical research and clinical applications will be incorporated to reinforce the theoretical basis of the analysis methods.
Prerequisites:
Mathematics through multivariate calculus (Math 241), programming experience, as well as some familiarity with linear algebra, basic physics, and statistics.
Textbook(s) and/or
Other Required Materials:
Course notes, handouts, journal articles
Reference Books:
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis, T.S. Yoo, A.K. Peters, 2004
Introduction to Applied Mathematics, G. Strang, Wellesley-Cambridge Press, 1986
Human Brain Function, Second Edition: C. J. Price, S. Zeki, J.T.Ashburner, W. D. Penny, K. J. Friston, C. D. Frith, R. J. Dolan, Academic Press, 2003 (available online)
Course objectives:
The goal of this course is to introduce the basic principles of contemporary biomedical image analysis, including the relevant mathematics, statistics, or signal processing as needed for background, and to highlight clinical and basic science applications that draw on these techniques.
Topics Covered:
- Basic image processing and linear operators
- Pattern theory
- Segmentation
- Basics: edge detection, Bayesian framework, active snakes
- Geodesic active contours and level sets
- Active shape and appearance models
- EM algorithms and Markov random field models
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Registration
- Rigid and Affine methods
- Deformable registration
- Non-parametric approaches
- Human Brain Mapping
- Structural morphometry
- Functional analysis, statistical parametric mapping
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Developing image analysis applications with the Insight Toolkit
Class/Laboratory
Schedule:
Lecture: 3 hr/week
Contribution towards Professional Component
100% Engineering Science
Contribution towards Program Outcomes:
Multidisciplinary Ability |
High |
Problem Solving Approach |
High |
Problem Solving Methods |
High |
Experimentation |
Low |
Design |
Low |
Professional Orientation |
Med |
Person(s) Preparing
Description and Date:
James Gee
July 2007
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