Abstract
Plasmonic nanoparticles have been widely applied in cell imaging, disease diagnosis, and photothermal therapy owing to their unique scattering and absorption spectra based on localized surface plasmon resonance (LSPR) property. Recently, it is still a big challenge to study the detailed scattering properties of single plasmonic nanoparticles in living cells and tissues, which have dynamic and complicated environment. The conventional approach for measuring the scattering light is based on a spectrograph coupled to dark-field microscopy (DFM), which is time-consuming and limited by the small sample capacity. Alternatively, RGB-based method is promising in high-throughput analysis of single plasmonic nanoparticles in dark-field images, but the limitation in recognition of nanoparticles hinders its application for intracellular analysis. In this paper, we developed an automatic and robust method for recognizing the plasmonic nanoparticles in dark-field image for RGB-based analysis. The method involves a bias-modified fuzzy C-means algorithm, through which biased illumination in the image could be eliminated. Thus, nearly all of the gold nanoparticles in the recorded image were recognized both on glass slide and in living cells. As confirmed, the distribution of peak wavelength obtained by our method is well agreed to the result measured by conventional method. Furthermore, we demonstrated that our method is profound in cell imaging studies, where its advantages in fast and high-throughput analysis of the plasmonic nanoparticles could be applied to confirm the presence and location of important biological molecules and provide efficiency information for cancer drug selection.