Particle tracking in fluorescent microscopy images improved by morphological source separation
Particle detection and tracking methods generally assume a simplistic image model that is rarely valid when imaging biological processes in fluorescence microscopy. The tracking task may become nearly impossible when complex biological structures are visible and interfere with the signal of interest. To address this limitation we have adapted a source separation technique based on sparsity principles to the characteristics of fluorescent biological images. Since it allows the discrimination of objects with different morphologies, we present an approach to detect and track particles that exploits its results. The tracking algorithm resolves particles that temporarily aggregate by exploiting the proposed model of image. We prove in a real case the ability of the method to track numerous particles in a complex and dynamic background, something which was not feasible until now, hence offering new tools to document interactions between cellular compartments.