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Polony technology is a low-cost, high-throughput platform employed in several applications such as DNA sequencing, haplotyping and alternative pre-mRNA splicing analysis. Owing to their random placement, however, overlapping polonies occur often and may result in inaccurate or unusable data. Accurately identifying polony positions and sizes is essential for maximizing the quantity and quality of data aquired in an image, however, most existing identification algorithms do not handle overlapping polonies well. In this paper, we present a novel polony identification approach combining both a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. Experiments on simulated and real images of highly overlapping polonies show that our algorithm has a 10% to 20% increase in recall compared with the existing algorithms, while keeping precision at the same level.