ECE 6357 Advanced Image Processing
A graduate course on the mathematical and algorithmic foundations of (medical) image analysis, from statistical intensity models and segmentation to image registration.
The course works through primary literature and hands-on implementation: translating ideas into mathematical models, implementing them correctly, and understanding why and when they work, or break.
Co-taught with Yihao Liu.
Route through the atlas
- Ch 2.1
- Ch 2.2
Multilevel thresholds planned
Reddi, Rudin and Keshavan, 1984
- Ch 2.3
k-means
When each sample carries several numbers, how do we split the samples into groups?
MacQueen, 1967
- Ch 2.4
- Ch 2.5
Mixtures and EM planned
Dempster, Laird and Rubin, 1977
- Ch 2.6
Bias field correction planned
Van Leemput et al., 1999
- Ch 3.1
- Ch 3.2
Snakes
Given scattered evidence of edges, how do we find one closed boundary?
Kass, Witkin and Terzopoulos, 1988
- Ch 3.3
Gradient vector flow
When the snake cannot feel the boundary, can a better force reach it?
Xu and Prince, 1998
- Ch 3.4
Level sets
How can one boundary become two, without anyone deciding where to cut?
- Ch 3.5
Introduction to Modern Techniques for Medical Image Segmentation seminar
Dr. Dingjie Su, Vanderbilt University
Wednesday, October 21, 2026, 11:15 am – 12:05 pm, Central Time · Engineering & Science Building, Room 001
- O. Ronneberger, P. Fischer and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” MICCAI, 2015.
- F. Isensee et al., “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, 2021.
- A. Kirillov et al., “Segment anything,” ICCV, 2023.
- Ch 3.6
Segmentation Evaluation for Medical Image Processing seminar
Dr. Aaron Carass, Johns Hopkins University
Monday, October 26, 2026, 11:15 am – 12:05 pm, Central Time · Engineering & Science Building, Room 001
- Ch 4.1
Interpolation planned
Thévenaz, Blu and Unser, 2000
- Ch 4.2a–c
Registration framework planned
Brown, 1992
- Ch 4.2d
Mutual information planned
Viola and Wells; Collignon et al., 1995
- Ch 4.3
Optical flow planned
Horn and Schunck; Lucas and Kanade, 1981
- Ch 4.4
Demons planned
Thirion, 1998
- Ch 4.5
Modern registration seminar
- B. B. Avants et al., “Symmetric diffeomorphic image registration with cross-correlation (SyN),” Medical Image Analysis, 2008.
- G. Balakrishnan et al., “VoxelMorph: a learning framework for deformable medical image registration,” IEEE TMI, 2019.
- J. Chen et al., “TransMorph: Transformer for unsupervised medical image registration,” Medical Image Analysis, 2022.
- Y. Liu et al., “Vector field attention for deformable image registration,” Journal of Medical Imaging, 2024.
- Ch 4.6
Evaluating registration seminar
See it on the atlas.