Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Abstract

We developed an approach, called Cega, to analyze the motility of kinesin motors translocating on microtubules in complex and noisy systems. Cega separates the fluorescent signatures of moving motors from background noise using the Kullback-Leibler divergence and produces coordinates of moving particles for downstream single particle tracking.