Quantitative Big Imaging

Kevin Mader
17 March 2016

ETHZ: 227-0966-00L

Advanced Segmentation

Course Outline

  • 25th February - Introduction and Workflows
  • 3rd March - Image Enhancement (A. Kaestner)
  • 10th March - Basic Segmentation, Discrete Binary Structures
  • 17th March - Advanced Segmentation
  • 24th March - Analyzing Single Objects
  • 7th April - Analyzing Complex Objects
  • 14th April - Spatial Distribution
  • 21st April - Statistics and Reproducibility
  • 28th April - Dynamic Experiments
  • 12th May - Scaling Up / Big Data
  • 19th May - Guest Lecture - High Content Screening
  • 26th May - Guest Lecture - Machine Learning / Deep Learning and More Advanced Approaches
  • 2nd June - Project Presentations

Literature / Useful References

  • Jean Claude, Morphometry with R
  • John C. Russ, “The Image Processing Handbook”,(Boca Raton, CRC Press)
    • Available online within domain ethz.ch (or proxy.ethz.ch / public VPN)

Advanced Segmentation

Lesson Outline

  • Motivation
    • Many Samples
    • Difficult Samples
    • Training / Learning
  • Thresholding
    • Automated Methods
    • Hysteresis Method
  • Feature Vectors
    • K-Means Clustering
    • Superpixels
    • Probabalistic Models
  • Working with Segmented Images
    • Contouring
  • Beyond
    • Fuzzy Models
    • Component Labeling

Previously on QBI

  • Image acquisition and representations
  • Enhancement and noise reduction
  • Understanding models and interpreting histograms
  • Ground Truth and ROC Curves
  • Choosing a threshold
    • Examining more complicated, multivariate data sets
  • Improving segementation with morphological operations
    • Filling holes
    • Connecting objects
    • Removing Noise
  • Partial Volume Effect

Where segmentation fails: Inconsistent Illumination

With inconsistent or every changing illumination it may not be possible to apply the same threshold to every image.