Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise. We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification.

Periodic Step size Adaptation (PSA) is an on-line learning algorithm that can reach near-optimal empirical performance in a single-pass through training data. PSA can be applied to train a wide variety of models, including CRF and SVM. Source codes are released on October 15, 2009. Description of PSA can be found in NIPS*2009 [PDF] and Machine Learning Journal [PDF] .