Automated dynamic image analysis for particle size and shape classification in three dimensions
Year: 2025
Type: article
Abstract: We introduce OCULAR, an innovative hardware and software solution for three-dimensional dynamic image analysis of micron-sized particles. Current state-of-the art instruments for dynamic image analysis are largely limited to two-dimensional imaging. However, extensive literature has demonstrated that relying on a single two-dimensional projection for particle characterisation can lead to inaccuracies in many applications. Existing three-dimensional imaging technologies, such as computed tomography, laser scanning, and orthophotography, are limited to static objects. These methods are often not statistically representative and come with significant post-processing requirements, as well as the need for specialised imaging and computing resources. OCULAR addresses these challenges by providing a cost-effective solution for imaging continuous particle streams using a synchronised array of optical cameras. Particle shape characterisation is achieved through the reconstruction of their three-dimensional surfaces. This paper details the OCULAR methodology, evaluates its repeatability, and compares its results against X-ray micro-computed tomography, highlighting its potential for efficient and reliable particle analysis. • We introduce OCULAR, a 3D dynamic image analysis for micron-sized particle characterisation. • OCULAR outperforms 2D methods by capturing the full particle morphology, improving accuracy. • The repeatability of OCULAR is verified via consistent characterisation results. • Agreement of OCULAR with μCT highlights its reliability for particle analysis.
Source: Powder Technology
Institutions: Newcastle University, Queen's University Belfast
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,610
Awards: G1872828699, G2211786999, G4610536793
OpenAlex ID: W4408978449