BMI 210-768/STAT 932-768
Statistical Methods for Medical Image Analysis

Time: TR 9:30-10:45AM, September 3 - December 12, 2013
Location: Engineering Hall

Course Webpage:

Structural brain network obtained from diffusion tensor images (DTI). Students will learn a sparse regression framework necessary for modeling complex biological networks.


Moo K. Chung
Associate Professor
Department of Biostatistics and Medical Informatics

Waisman Laboratory for Brain Imaging and Behavior
University of Wisconsin-Madison

Office: Waisman Center #281
Personal Webpage:
Office Hour: 10:45-11:45AM. Right after class. To set up separate appointments, email me.


Target Audience
The course is designed for graduate students, and researchers who wish to learn quantitative techniques in analyzing medical images and set up statistical models. The course material is applicable to a wide variety of statistical problems in medical and biological images.

Motivations for Course

The demand from students, staff and faculty is exploding as many are increasingly involved in collecting various medical imaging data, but there is no course to train students and researchers on how to analyze medical images quantitatively. However, there is no statistics course focused on imaging data in the campus. Most imaging courses from other department deal mainly with image acquisition or processing but do not provide relevant in-depth statistical content to students.

Course Aims
Present statistical and other quantitative techniques used in analyzing various medical images. A concise review of relevant methodological background will be presented. Basic concepts of key methods will be developed with considerable attention to analysis of real medical maging data of various types and problems. Students and researchers should gain a deeper understanding of statistical methods used in medical image analysis. Course projects will be designed to apply methods learned from classes to medical images provided by an instructor or students themselves.

Course Evaluation
Students are required to submit (1) research proposal and (2) final research project report and do an oral presentation at the end of the semester. Students can use their own medical images for the final project after consultation with the instructor. For students without their own data set, the final project topics and data set will be provided. Sample A-grade level final project reports can be found  here.

The grading is based on the research proposal (10%), oral presentation (30%) and final project report (60%).
The grading scheme is as follows: A (85%), AB (80%), B (75%), BC (70%), C (65%), D (60%) and F (below 60%).

Text Book

The recommended text book is
Computational Neuroanatomy: The Methods written by the instructor. It is not a required textbook thought but each module of lectures (2-3 lectures) will be based on each chapter of the textbook.

Three hour lectures/week will contain four parts: 1) presentation of statistical theory/concepts via PowerPoint, 2) presentation of MATAB routintes demonstrating the methods, 3) presentation of real medical imaging applications, 4) open discussion with students. Lectures given in 2012 are found at here. MATLAB demonstration done in 2012 class can be found at here.

Course Content
This course will give a concise review of relevant statistical background needed in advanced medical image analysis. Students will be introduced to key concepts and methods used in analyzing various medical images and imaging related problems. Some of topics coverd in the class are:

Previous imaging courses taught by the instructor

2003 fall: Stat 992: Statistical Methods in Signal and Image Analysis.

2007 spring:
Stat 692: Medical Image Analysis

2010 fall: Computational Methods in NeuroImage Analysis
:Course given in Seoul National Univeristy

2012 fall: BMI/STAT 768: Statistical Methods for Medical Image Analysis