BMI/CS 567 Medical Image Analysis

2016 Spring Semester. This webpage is downloaded from

Effect of family income on the children's hippocampus, which regulates memory: example of longitudinal MRI study.


Tuesday/Thursday 2:30-3:45pm
Engineering Hall 2535


Moo K. Chung
Jeanette A. Mumford

Department of Biostatistics and Medical Informatics
Waisman Laboratory for Brain Imaging and Behavior
University of Wisconsin-Madison

Office Hours

Moo Chung: every Thursday1:00-2:00pm (MSC 5785) except during the weeks of January 27-Febuary 3.

Jeanette A.Mumford: Every Wednesday 3:00-4:00pm (Wasiman Center T233) except during the week of February 23-25.

For other dates, make an appointment with us.


Undergraduate and graduate students from biological, medical, and computer science departments who wish to learn the basics of medical image analysis. The student enrollment is limited to 25.


CS 367 (or programming experience with some high-level programming language), Math 221, and some familiarity with introductory concepts in probability and linear algebra. or by consent of the instructor.

Course Objectives

Present introductory medical image processing and analysis techniques.  Basic concepts without extensive technical details or proofs will be covered. Students and researchers should gain an easy to follow understanding of various processing and analytic issues in medical image analysis.  The focus will be on awareness of processing and analysis concepts used in the field and how some of these ideas can be implemented in Matlab to facilitate image analysis.

Textbook & Language of Instruction

There is no textbook but reading materials will be assigned and lecture notes will be distributed online through the dropbox. Homework will be distributed online. Link to the Dropbox folder:

MATLAB is the language of instruction. Free MATLAB and other commercial software packages can be downloaded from for Univ. of Wisconsin system students.

Course Evaluation

Students will be assigned written and programming homework assignments (4 homework 30%), and two quizzes (60%) and final exam or project (take home 10%). The grading scheme is as follows: A (above 85%), AB (above 80%), B (above 75%), BC (above 70%), C (above 65%), D (above 60%) and F (below 60%).

Sample homework problems:

1.    Given longitudinally collected CT images of the head, describe a sequence of processing and analysis procedures for modeling mandible volume change. Implement in Matlab a morphological procedure (e.g., open/close filtering) for correcting topological defects in the volume computation due, for example, to teeth fillings.

2.   Given an existing implementation of a generalized Hough transform, write code to adapt it for detecting circular objects in microscopy images. Using training data, perform evaluations assessing the accuracy of your method.

3. 0% tolerance for plagiarism. Wikipedia defines it as "plagiarism is the wrongful appropriation and stealing and publication of another author's language, thoughts, ideas, or expressions and the representation of them as one's own original work". This includes computer codes. Students are required to work independently from each other and expected NOT to work together for homework and exam problems.

Course Topics/schedule and additional supplementary reading list

Topics marked by blue will be taught by Jeanette.

19-Jan    Moo    MATLAB: iterations, functions, random numbers
21-Jan    Moo    MATLAB: matrices, matrix inversion
26-Jan    Moo    Imaging modalities - microscope, CT, EEG: simple histogram tresholding
28-Jan    Jeanette    Imaging modalities - MRI/fMRI: simple statistical operations: median, mean
2-Feb    Jeanette    Image registration - basics, affine
4-Feb    Jeanette    Image registration - nonlinear

9-Feb    Moo    Image integration: 1D signal filtering
11-Feb    Moo    Image integration: 2D &3D image filtering
16-Feb    Moo    Image differentiation: edge detection
18-Feb    Moo    Image differentiation: image diffusion
23-Feb    Moo    Image topology : image morphology
25-Feb    Moo    Image topology
1-Mar    Jeanette    Image segmentation: adaptive threshold
3-Mar    Jeanette    Image segmentation:
8-Mar    Jeanette    Image segmentation:
k-means clustering
10-Mar    Moo    Surface processing: isosufaces
15-Mar    Moo    Surface processing: mesh regularization
17-Mar    Quiz 1
22-Mar    Spring break
24-Mar    Spring break

29-Mar    Moo    Image complexity
31-Mar    Moo    Image complexity
5-Apr    Moo    Multiple image analysis: binary images
7-Apr    Moo    Multiple image analysis: general linear models
12-Apr    Moo    Multiple image analysis: general linear models
14-Apr    Moo    Multiple image analysis: voxel-wise statistics
19-Apr    Moo    Image resampling: Jackknife, leave k-out
21-Apr    Jeanette    Image classification: k-means clustering, linear discriminant
26-Apr    Jeanette    Image classification:  logistic
28-Apr    Jeanette    Image classification: logistic
3-May    Jeanette    Multiple comparisons

5-May    Quiz 2
13-May   7:45-9:45am Final exam (take home)

  1. Introduction to Matlab. The basics on Matlab programming will be covered so that students can do homework. Reading: Martinez, W.L, Martinez, A.R. 2007. Computational Statistics Handbook with MATLAB – Chapter 5 Exploratory data analysis, 2nd edition. Chapman & Hall/CRC. 3 hours
  1.  Introduction to various imaging modalities (EEG, PET, MRI, fMRI, DTI, CT and microscope images and their simple manipulation. Data storage formats for different imaging modalities. This may utilize invited guest lectures by experts in each imaging modality. Reading: Dhawan, A.P. 2011 Medical image analysis - Chapter 1 Introduction. John Wiley & Sons. Nichols, T., Poldrack, R.A., Mumford, J.A. Handbook of functional MRI data analysis. Dhawan, A.P. 2011 Medical image analysis - Chapter 2 Image formation. John Wiley & Sons; Chung, M.K. 2013 Statistical and Computational Methods in Brain Image Analysis – Chapter 5 Introduction to Brain and Medical Images, Chapman & Hall/CRC. 3 hours
  1. Basic image processing and quantification (Integration, differentiation, topology). High-pass and low-pass filtering. Spatial smoothing, temporal smoothing, morphological operations, etc. Simple quality control and validation methods such as using image filtering for denoising, kappa index, and image correlations. Reading: Yao, T.S. Insight into images: principles and practice for segmentation, registration and image analysis – Chapter 1 Introduction and Basics. A K Peters/CRC Press.  9 hours
  1.  Surface processing. 3D volume to surface meshes. Simple mesh processing and manipulation. Reading: Chung, M.K., Hanson, J.L., Pollak, S.D. 2016. Statistical analysis on brain surfaces, Handbook of Modern Statistical Methods: Neuroimaging Data Analysis. 3 hours
  1.  Image complexityMeasure of image complexity, fractional dimensions in images and signals etc. 3 hours

  1.  Image segmentation methods. Thresholding, intelligent scissors, mean-shift, and random-walk and graph-based methods. Reading: Yao, T.S. Insight into images: principles and practice for segmentation, registration and image analysis – Chapter 1 Introduction and Basics, Chapter 2 Segmentation. A K Peters/CRC Press. 4.5 hours
  1. Quantification of multiple images. We will present a simple statistical methodology that is covered in BMI 541. A simple example is explaining how to construct a t-statistic at each voxel and how it is affected by image filtering, outliers, etc. General linear model (GLM) will be extensively used. Reading: Chung, M.K. 2013 Statistical and Compuational Methods in Brain Image Analysis – Chapter 3 General Linear Models, Chapman & Hall/CRC.
  1. Image registration. Using Procrustes or iterative closest point transform. Demonstrate use of some advanced image registration methods implemented in Matlab and used by the community (e.g., diffeomorphic, mutual information based). Reading: Yao, T.S. Insight into images: principles and practice for segmentation, registration and image analysis – Chapter 3 Registration. A K Peters/CRC Press. 3 hours
  1. Image classification/categorization. In many medical imaging studies, classification accuracy is used as an alternative quantitative measure instead of p-value. Will go over basics on linear discriminant analysis and logistic regression. Support vector machine. Present simple examples where the classification accuracy can be computed by hand. Describe precision-recall and AUC curves. Reading: Flury, B. A. 1997. First course in multivariate statistics - Chapter 5 Linear Discriminant Analysis, Chapter 7 Logistic Regression, Springer. 4.5 hours
  1.  Interpretation of p-values and multiple comparisons. For clinically oriented studies, it is often necessary to interpret the medical imaging literature where the results are displayed in p-value maps with multiple comparisons correction (mostly Bonferroni correction, FDR and random field theory).  We will only cover how to compute them in Matlab, and how to interpret them, and will not cover theoretical issues. Reading: Genovese, C.R., Lazar, N.A., Nichols, T.E., 2002. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage 15, 870–878; Nichols, T., Hayasaka, S. 2003. Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res. 12:419-46. 1.5 hours