Lecture Time & Place
2020 Spring semester
408 SMI (Service Memorial Institute is a building connected to the Medical Science Center)
Course Webpage: www.stat.wisc.edu/~mchung/teaching/768
Moo K. Chung, PhD
Associate Professor of Biostatistics and Medical Informatics
Waisman Laboratory for Brain Imaging and Behavior
University of Wiconsin-Madison
Science Center 4725, 1300 University Ave
Office Hours: T/TR 10:45-12:30am. To set up separate appointments, please email the instructor.
- None. The course is self
contained. The course is designed for
graduate students, postdocs and researchers
who wish to learn quantitative mathematical,
statistical and computational techniques in
processing and analyzing medical images.
However, basic understanding of linear algebra
and calculus will be useful to fully
understand lectures. The course material is
applicable to a wide variety of high
dimensional nonstandard data and imaging
problems beyond medical images. Senior
undergraduate students may take the course
after discussion with the instructor.
Image processing, rendering, regression, classification
Trees, graphs, networks, manifold valued data
Data Analysis (TDA)
persistent homology, algebraic topology, computational topology
Object oriented data analysis (OODA), functional data analysis, differential equations
Riemannian and spectral geometry, manifold learning, regression on manifolds
Big Data Computation
Large-scale computation, scalable computation, online algorithms
There is no required textbook. Lecture slides and
additional notes will be provided 5 hours before
each lecture in https://sites.google.com/view/bmi768.
Parts of lectures will be based on the following
three text books written by the instructor:
Statistical and Computational Methods in Brain Image Analsis, 2013 CRC Press
Brain Network Analysis, 2019 Cambridge University Press
Course evaluation is based on a class project.
Students are required submit the final research
project report and do the final oral presentation
at the end of the semester. The project can be 1)
literature review 2) sequence of homework
problems, 3) computer programming or 4) a
research project. For programming and research
project, you can either use your own data for the
project (after consultation with the instructor)
or the instructor will provide the state-of-art
Sample class projects in previous image analysis
course taught by the instructor can be found here.
Each semester, the focus of the course change. So
the sample project reports may not reflect the
current course topics.