Lipid nanoparticles are self-assembling, dynamic structures commonly used as carriers of siRNA, DNA, and small molecular therapeutics. Quantitative analysis of particle characteristics such as morphological features can be very informative as biophysical properties are known to influence biological activity, biodistribution, and toxicity. However, accurate characterization of particle attributes and population distributions is difficult. Cryo-Electron Microscopy (Cryo-EM) is a leading characterization method and can reveal diversity in particle size, shape and lamellarity, however, this approach is traditionally used for qualitative review or low throughput image analysis due to inherent EM micrograph contrast characteristics and artifacts in the images which limit extraction of quantitative feature values. In this paper we describe the development of a semiautomatic image analysis framework to facilitate reliable image enhancement, object segmentation, and quantification of nanoparticle attributes in Cryo-EM micrographs. We apply this approach to characterize two formulations of siRNA-loaded lipid nanoparticles composed of cationic lipid, cholesterol, and poly(ethylene glycol)-lipid, where the formulations differ only by input component ratios. We found Cryo-EM image analysis provided reliable size and morphology information as well as the detection of smaller particle populations that were not detected by standard dynamic light scattering (DLS) analysis.