SKILL SHEET

BIOLOGICAL IMAGING UNIT

COMPUTATIONAL BIOLOGY COURSES 03-310, 03-510 AND 03-710

This is an organized listing of skills and concepts designed to help you prepare for exams. This list is aimed at focusing your attention on those parts of the course material related to skills you may be required to perform. This is NOT an exhaustive list of concepts - not all material from the lectures is listed here, so you should still review your course notes and assigned reading. SKILLS AND CONCEPTS WHICH ARE NOT LISTED HERE MAY BE INCLUDED ON EXAMS. Unless noted below, all material is relevant to both courses (but the depth of understanding is expected to be greater for 03-510/ 03-710).

• (03-510/03-710) Indexing multimedia databases: R-trees and M-trees
• Image formation and acquisition
• pixel, detector, and display device
• Look up table (LUT)
• Image type
• Light transmission, Electron transmission, X-ray transmission, etc.
• Resolution and ability to image in living specimen
• Properties of light
• Wavelength, Direction, Phase, Polarization, Intensity.
• Point source and point-spread function
• 3D microscopy and 3D image data
• result of convolution of 3D sample distribution with point-spread function
• Widefield fluorescence microscopy and confocal microscopy
• Benefits and drawbacks of confocal microscopy
• Common image file formats: PICT/TIFF/JPEG/GIF
• Image display
• Contrast enhancement
• Lookup tables
• Binary image operations
• Thresholding – manual or automated (Ridler-Calvard) method
• Erosion and Dilation
• Opening and Closing
• Outlining and Skeletonizing
• Basic image processing operations
• Arithmetic Operations
• Kernel Operations
• Smoothing
• Sharpening
• Edge Finding
• Image math
• Division: ratio image
• Logical AND: mask
• Object finding (Particle analysis)
• 1D and 2D Fourier transforms
• Filtering in frequency domain
• Feature extraction
• Morphological Features
• Edge & Hull Features
• Skeleton Features
• Haralick Texture Features
• Transform Features
• Wavelet transforms
• Feature selection
• Feature reduction
• Covariance Matrix
• Principal Component Analysis
• Stepwise Discriminant Analysis
• Fractal Dimension
• Classifiers
• Linear Discriminant Classifiers
• Finding decision boundaries
• Representing results using confusion matrix
• Cross-validation
• Training and using image classifiers using Matlab
• Spatial simulations – Virtual Cell
• Models that consider compartment geometry
• Creating masks for input to Virtual Cell

Prepared by Robert F. Murphy, May 8, 1997

Revised by Robert F. Murphy, May 3, 1999

Revised by Robert F. Murphy, May 3, 2002

Revised by Yanhua Hu and Shann-Chig Chen, April 25, 2005