Performance evaluation of multiresolution texture analysis of stem cell chromatin

We apply texture image analysis to automated classification of stem cell nuclei, based on the observation that chromatin in human embryonic stem cells becomes more granular during differentiation. Using known probability models for texture multiresolution decompositions, we derive likelihood ratio test statistics. We also derive the probability density functions of these non-Gaussian statistics and use them to evaluate the performance of the classification test. Results indicate that the test can distinguish with probability 0.95 between nuclei that are pluripotent and those with varying degrees of differentiation. The test recognizes nuclei with similar differentiation level even if prior information says the contrary. This approach should be useful for classifying genome-wide epigenetic changes and chromatin remodeling during human development. Finally, the test statistics and their density functions are applicable to a general texture classification problem.