Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This addresses the problem of analyzing poor-quality imaging data for researchers in fields like biology or materials science, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles particle detection and tracking in noisy fluorescence time-lapse imaging by proposing a probabilistic a contrario model, which outperforms state-of-the-art methods in comparative evaluations.
This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.