Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

TL;DR

This paper introduces a probabilistic a contrario method for detecting and tracking particles in noisy fluorescent time-lapse images, eliminating the need for training and tuning, and demonstrating superior performance over existing methods.

Contribution

The paper presents a novel a contrario-based framework for particle detection and tracking that is robust to noise and does not require prior learning or extensive parameter tuning.

Findings

Outperforms state-of-the-art methods in noisy conditions

Does not require prior training or parameter tuning

Robust to poor-quality data

Abstract

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.