Statistical Methods for Medical Image Analysis

BMI/STAT- 768 (2020 spring semester)

Instructor    Moo K. Chung

Lecture Time & Place

2020 Spring semester
T/Th 9:30-10:45am
408 SMI (Service Memorial Institute is a building connected to the Medical Science Center)
Course Webpage:


Moo K. Chung, PhD
Associate Professor of Biostatistics and Medical Informatics
Waisman Laboratory for Brain Imaging and Behavior
University of Wiconsin-Madison

Office: Medical Science Center 4725, 1300 University Ave
Office Hours: T/TR 10:45-12:30am. To set up separate appointments, please email the instructor.

  1. Prerequisite

  2. 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.

Course Topics

MATLAB Programming

Image processing, rendering, regression, classification

Heterogeneous data
Trees, graphs, networks, manifold valued data

Topological Data Analysis (TDA)
persistent homology,
algebraic topology, computational topology

Hilbert Space Methods
Object oriented data analysis (OODA), functional data analysis,
differential equations

Geometric Methods
Riemannian and spectral geometry, manifold learning, regression on manifolds

Big Data Computation
Large-scale computation, scalable computation, online algorithms

Course Materials

There is no required textbook. Lecture slides and additional notes will be provided 5 hours before each lecture in Parts of lectures will be based on the following three text books written by the instructor:

Computational Neuroanatomy: The Methods, 2012 World Scientific Publishing
Statistical and Computational Methods in Brain Image Analsis, 2013 CRC Press
Brain Network Analysis, 2019 Cambridge University Press

Course Evaluation

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 data.

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.

Skeleton representation of lung blood vessel tree obtained from CT. Students will learn advanced  data and image representation and visualization techniques as well as quantification methods used in medical image processing and analysis. Read book chapter Chung et al. 2018 for more detail on the vessel tree modeling.