Statistical Methods for Medical Image Analysis

BMI/STAT- 768 (2021 spring semester)

Instructor    Moo K. Chung


Lecture Time & Place

2021 Spring semester
T/Th 9:30-10:45am
Completely online Zoom class.
Course Webpage: www.stat.wisc.edu/~mchung/teaching/768

Instructor

Moo K. Chung, PhD     mkchung@wisc.edu
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. After class. To set up separate Zoom appointments, please email the instructor.

  1. Prerequisite

  2. The course is designed for graduate students who wish to learn quantitative mathematical, statistical and computational techniques in processing and analyzing medical images. The 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

Introduction to MATLAB, Imaging data types, trees, graphs, networks, manifold valued data

Functional Data Analysis (FDA)

Hilbert space methods, basis function methods, differential equations, regression

Geometric Data Analysis (GDA)

Riemannian and spectral geometry, manifold learning, regression on manifolds


Topological Data Analysis (TDA) 

Persistent homology, algebraic topology, computational topology, topological learning, dynamic-TDA

Big Data Computation & Learning

Online algorithms, scalable computation, clustering and classification


Lectures & Course materials

There is no required textbook. Lecture slides, additional notes, computer codes will be provided before each lecture in https://canvas.wisc.edu. 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 following options. Students are required submit the final research project report and do an optional final oral presentation at the end of the semester. The project can be 1) literature review or method survey, 2) extended homework problems, 3) computer programming project, or 4)  a research project. Well written 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. 


The course workload estimator Rice University: Center for Teaching Excellence will help you estimate the amount of work you need to put for the class.





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.