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Course Description

Instructor:
Ivo D. Dinov, Ph.D.
Assistant Professor in Statistics,
Research Scientist, Department of Neurology,
UCLA School of Medicine
E-mail:

Tentative schedule of topics to be covered

Data (2-3 wks):
- Intro to non-invasive tomographic imaging - PET, fMRI, MRI, DTI, CT of the human body will be provided.
- Mathematical formulations of data acquisition and representation.
- Data visualization & mathematical modeling.
Brief review of common general statistical techniques (1 wk)
- Probabilities, Hypothesis testing, confidence intervals
- Parametric vs. distribution-free tests
- ANOVA
- Regression (simple/multiple linear)
- Model goodness-of-fit
Advanced Statistical methods for analysis of medical images. (4 wks)
- Decision theory and signal estimation.
- Wavelets, signal denoising and efficient data representation.
- Statistics in the wavelet domain.
- Probabilistic (Bayesian, deformable, random field, hidden Markov), variational (PDE) and computational models for Image Segmentation and Object Recognition.
- Others
- Morphology and 3D modeling
- Multidimensional tensor mapping
Integration, Visualization, Interrogation and Interpretation of Imaging and non-imaging Meta-data (1-2 wks)
- PCA
- General Linear Model
- Data mining
Student Paper Presentations

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