A robust particle detection algorithm based on symmetry

TL;DR

The paper introduces C-Sym, a new particle detection algorithm leveraging symmetry for high-accuracy tracking in noisy environments, outperforming existing methods in synthetic and real datasets.

Contribution

The novel Circular Symmetry algorithm (C-Sym) improves particle detection accuracy and precision under challenging noisy conditions, with potential applications in environmental tracking.

Findings

C-Sym outperforms four existing methods in accuracy and precision.

C-Sym maintains high performance in synthetic and experimental datasets.

The algorithm is suitable for tracking biota in environmental conditions.

Abstract

Particle tracking is common in many biophysical, ecological, and micro-fluidic applications. Reliable tracking information is heavily dependent on of the system under study and algorithms that correctly determines particle position between images. However, in a real environmental context with the presence of noise including particular or dissolved matter in water, and low and fluctuating light conditions, many algorithms fail to obtain reliable information. We propose a new algorithm, the Circular Symmetry algorithm (C-Sym), for detecting the position of a circular particle with high accuracy and precision in noisy conditions. The algorithm takes advantage of the spatial symmetry of the particle allowing for subpixel accuracy. We compare the proposed algorithm with four different methods using both synthetic and experimental datasets. The results show that C-Sym is the most accurate and precise algorithm when tracking micro-particles in all tested conditions and it has the potential for use in applications including tracking biota in their environment.