Use of image processing algorithms for mine originating waste grain size determination

The utilization of mineral wastes from the mining industry is one of the most challenging phases in the raw materials life cycle. In many countries, there are piles of mineral waste materials that date back to the previous century. There is also a constant stream of accompanying mineral matter excavated during everyday mine operations. This stream of waste matter is particularly notable for deep coal mining. Grain size composition of waste mineral matter is one of the most important characteristics of coal originating waste material. This paper presents the use of image analysis for the determination of grain size composition of rock material. Three methods for edge identification have been tested: gradient magnitude, multiscale linear filtering, and Statistical Dominance Algorithm (SDA). Images acquired in laboratory conditions were pre-processed using Gaussian, Median, and Perona-Malik filtration. The image was segmented using a classic watershed algorithm; as a reference, manually segmented images were used. The results show that the SDA algorithm was the best in determining the grain edges. Therefore, the sizes determined after the application of this algorithm were closest to the groundtruth. This method can be used for the assessment of the grain size composition of mineral waste material.