Room 133 SMI
Moo K. Chung
mathematical and statistical techniques used in the field of medical
image analysis, with an emphasis on computer implementation. We will
study algorithms and strategies based on the use of various models to
solve the following medical imaging problems: representation,
visualization, feature extraction, denoising, image registration,
morphometry (deformable), quantification and validation.
This course is
designed for researchers and students who wish to analyze and model
medical image data quantitatively. The course material is applicable to
a wide variety of medical and biological imaging problems.
in statistics, mathematics and computer programming.
data, random fields, Hilbert space, Fourier analysis, wavelets, linear
and nonlinear filters, time series, multivariate techniques, pattern
recognition, shape modeling, deformable template, curve and surface
geometry, finite element methods (FEM), similarity measures, image
simulation, image registration (2D surface & 3D volume),
statistical parametric mapping (SPM), multiple comparison correction,
computational statistics, validation techniques.
credits, students are required to submit a project report and do an
oral presentation at the end of the semester. For one or two credits,
consult with the instructor. Students can use their own image data (not
necessarily medical or biological in nature) after the consultation
with the instructor.