1. Typical Image Selection
Jennifer Lin, James Gajnak,
Robert F. Murphy
Cytometry Development Workshop 2000

2. Background
Fluorescence microscopy is commonly used for digital image collection
Current analysis on such images is done by visual inspection which is not quantitative, and not feasible for large data sets
We have been working on the automated and objective interpretation of fluorescence images
Murphy lab numerical analysis

3. Previous work
Typical Image Chooser (TypIC) – method for ranking a set of images in order of typicality using Haralick texture features to describe images
84 features used for TypIC are FROM classification

4. TypIC1 Uses only 13 texture features to describe images
Uses robust estimation of mean and covariance matrix to eliminate outliers
Requires a minimum of 35 images
Spatial relationship of different (gray tones) tones and textures

5. TypIC2
Principal components allow the use of more features without requiring an extremely large number of images
Collapses feature set into a smaller number of dimensions
Robust or non-robust estimations of mean and covariance matrix also reduces the number of images needed
Non robust estimations assumes there are no outliers

6. Results Compare TypIC2 with TypIC1 Results for mixed sets
Stability of rankings for sets of decreasing size
Necessary any more?

7. Mixed Sets
Assembled a biased test set of images from five classes: zero time (30), 10 min (25), 30 min (20), 60 min (15), basal (10)
Hypothetically images from the largest (most biased) classes will be ranked as the most typical

8. Results – TypIC1
Correlation coefficient:

9. Results – TypIC2
Correlation coefficients for varying numbers of principal components (PC):
Num. of PC Cor. Coeff.

10. Results – TypIC2
Correlation coefficient:

11. Results - Mixed Sets
TypIC2 performs best with only 1 principal component (2 and 3 similar)
TypIC1 is better at distinguishing between the protein classes than TypIC2

12. Results - Set Size Use ten classes of images
Rank full set with various methods
Rank subsets of decreasing size
Measure correlation between rankings

13. Results - Set Size