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

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

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

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

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

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

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

Results – TypIC1 Correlation coefficient: -0.52708

Results – TypIC2 Correlation coefficients for varying numbers of principal components (PC): Num. of PC Cor. Coeff. 1 -0.37177 2 -0.30753 3 -0.30598 4 -0.25877 5 -0.28973

Results – TypIC2 Correlation coefficient: -0.37177

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

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

Results - Set Size