Medical Imaging Informatics

Course Information | Bibliography | Syllabus & Materials

Course Information

Course #: MED 170.03
Course Type: Lecture
Dates: Winter Quarter, 1/8 - 3/25
Time: Tuesday, 9-11 AM
Location: Parnassus Campus, Various Rooms

The Department of Radiology is offering a 12 weeks course in novel concepts of medical imaging informatics. The emphasis will be on image classification, registration, statistical processing, and data mining of multiparametric imaging data with specific emphasis on brain MRI. However, the techniques are general and applicable to a wide range of other medical imaging modalities. Two units credit can be earned for successfully attending the course.

• Norbert Schuff (415) 221-4810 x 4904
• Karl Young (415) 221-4810 x 3114

• John Kornak
• Ashish Raj
• Colin Studholme
• Wang Zhan
• XiaoPing Zhu

Galen accounts (needed to access course web page):
http://www.library.ucsf.edu/info/accounts.html ***

R language (to download basic R language and add on packages):
http://cran.r-project.org/ ***

Weka Data Mining Package (download):
http://www.cs.waikato.ac.nz/ml/weka/ ***

Preliminary Bibliography

• Magnetic resonance imaging : physical principles and sequence design
  Haacke, M. ... [et al.].
  Wiley & Sons, 1999
• Protein NMR spectroscopy : principles and practice
  Cavanagh, J. ... [et al.].
  Academic Press, 1996.
• MRI in practice
  Westbrook, C., Kaut C, R.
  Blackwell Pub., 2005

• Fundamentals of statistical signal processing
  Kay, S. M.
  Prentice-Hall PTR, 1993-1998
• Fundamentals of digital image processing
  Jain, A. K.
  Prentice-Hall, 1989

• Data analysis and graphics using R : an example-based approach
  Maindonald, J., Braun J.
  Cambridge University Press, 2003
• Modern applied statistics with S
  Venables, W.N., Ripley, B. D.
  Springer, 2002

• Elements of information theory
  Cover, T. M.,Thomas, J.A.
  Wiley, 1991.

• The elements of statistical learning : data mining, inference, and prediction
  Hastie, T., Tibshirani, R., Friedman J.
  Springer, 2001.
• Data mining : practical machine learning tools and techniques
  Witten, I. H., Frank E.
  Morgan Kaufman, 2005.

Syllabus & Materials