Image segmentation method for coal particle size distribution analysis

Abstract

Particle size distribution is extremely important in the coal preparation industry. It is traditionally analysed by a manual screening method, which is relatively time-consuming and cannot immediately guide production. In this paper, an image segmentation method for images of coal particles is proposed. It employs the watershed algorithm, k-nearest neighbour algorithm, and convex shell method to achieve preliminary segmentation, merge small pieces with large pieces, and split adhered particles, respectively. Comparing the automated segmentation using this method with manual segmentation, it is found that the results are comparable. The size distributions obtained by the automated and manual segmentation methods are nearly identical, and the standard deviation is less than 3%, indicating good reliability. This automated image segmentation method provides a new approach for rapidly analysing the size distribution of coal particles with size fractions defined according to consumer requirements.