Average Grain Size Determination using Mathematical Morphology and Texture Analysis

Abstract Many industrial processes need information about material grain size. In this work we examined rolled chrome concentrate to determine the average grain size. Test material was sieved into 15 fractions, from 37 µm to 500 µm. The analysis method can be divided in three sections: preprocessing, feature extraction and classification. Mathematical morphology was used as preprocessing method, with gray-scale erosion and opening as operations. Feature extraction was implemented with first and second-order statistics. Finally, classification was performed with k-NN and minimum distance classifiers using leave-out method. We conclude that mathematical morphology with texture analysis can be used to determine average grain size of material. It is computationally easy and fast although less accurate to smaller grain classes. This is due to imaging errors and noise but also the fact that the ratio grain size versus size of structuring element must be large enough. Both opening and erosion operations can be used. Erosion is two times faster than opening to perform. Also the number of preprocessing operations can be, for example, reduced to three without the classification result will have a remarkable change