Model organisms with compact genomes, such as yeast and Caenorhabditis elegans, are particularly useful for understanding organism growth and life/cell cycle. Organism morphology is a critical parameter to measure in monitoring growth and stage in the life cycle. However, manual measurements are both time consuming and potentially inaccurate, due to variations among users and user fatigue. In this paper we present an automated method to segment bright-field images of fission yeast, budding yeast, and C. elegans roundworm, reporting a wide range of morphometric parameters, such as length, width, eccentricity, and others. Comparisons between automated and manual methods on fission yeast reveal good correlation in size values, with the 95% confidence interval lying between −0.8 and +0.6 μm in cell length, similar to the 95% confidence interval between two manual users. In a head-to-head comparison with other published algorithms on multiple datasets, our method achieves more accurate and robust results with substantially less computation time. We demonstrate the method's versatility on several model organisms, and demonstrate its utility through automated analysis of changes in fission yeast growth due to single kinase deletions. The algorithm has additionally been implemented as a stand-alone executable program to aid dissemination to other researchers.