Modern microscopic imaging devices are able to extract more information regarding the subcellular organization of different molecules and proteins than can be obtained by visual inspection. Predetermined numerical features (descriptors) often used to quantify cells extracted from these images have long been shown useful for discriminating cell populations (e.g., normal vs. diseased). Direct visual or biological interpretation of results obtained, however, is often not a trivial task. We describe an approach for detecting and visualizing phenotypic differences between classes of cells based on the theory of optimal mass transport. The method is completely automated, does not require the use of predefined numerical features, and at the same time allows for easily interpretable visualizations of the most significant differences. Using this method, we demonstrate that the distribution pattern of peripheral chromatin in the nuclei of cells extracted from liver and thyroid specimens is associated with malignancy. We also show the method can correctly recover biologically interpretable and statistically significant differences in translocation imaging assays in a completely automated fashion.optimal transport | cell morphometry | high content screening Q uantitative analysis of cell images is extensively used in several health sciences applications (1). Scientists wishing to quantify the effects of certain drugs, genes, and other perturbations (e.g., benign vs. malignant cancer cells) routinely make use of numerical software that are capable of evaluating statistical differences between two populations of cells captured under the microscope (2). Beyond simple automation facilitating the analysis of thousands of cells, the purpose of such software is to attempt to extract information that the human visual system is unable to cope with. A well-known drawback of existing methods is that the visual interpretation of any differences found is usually hidden from the user. The popular numerical features used to quantify and compare cells, such as form factor, Gabor and Haralick texture features, color histograms, etc. (3-5), usually do not have a direct biological interpretation. The situation is even more complicated when multiple features are needed simultaneously to characterize differences between cells, given that the physical interpretation of a combination of features with different units is a nontrivial task. Consequently, statistical tests are limited to determining whether or not two or more cell populations are different. Visual interpretation of any obtained result is usually nonintuitive and difficult.Here we describe a method, which we call transport-based morphometry (TBM), that takes as input a database of presegmented cell images and outputs a representation for the same data, which can be used for simultaneous visualization and quantitative evaluation in commonplace biological domains. An a priori set of numerical features is not needed as all calculations for comparing cells are done using the entire inform...