In nano-medicine, mesoporous silicon particles provide efficient vehicles for the dissemination and delivery of key proteins at the micron scale. We propose a new quality-control method for the nanopore structure of these particles, based on image analysis software developed to automatically inspect scanning electronic microscopy (SEM) images of nanoparticles in a fully automated fashion. Our algorithm first identifies the precise position and shape of each nanopore, then generates a graphic display of these nanopores and of their boundaries. This is essentially a texture segmentation task, and a key quality-control requirement is fast computing speed. Our software then computes key shape characteristics of individual nanopores, such as area, outer diameter, eccentricity, etc., and then generates means, standard deviations, and histograms of each pore-shape feature. Thus, the image analysis algorithms automatically produce a vector from each image which contains relevant nanoparticle quality control characteristics, either for comparison to pre-established acceptability thresholds, or for the analysis of homogeneity and the detection of outliers among families of nanoparticles.