Application of Texture Features and Machine Learning Methods to Grain Segmentation in Rock Material Images


  • Karolina Nurzynska Silesian University of Technology
  • Sebastian Iwaszenko Central Mining Institute



classification, grain sizes, object segmentation, texture features


The segmentation of rock grains on images depicting bulk rock materials is considered. The rocks’ material images are transformed by selected texture operators, to obtain a set of features describing them. The first order features, second-order features, run-length matrix, grey tone difference matrix, and Laws’ energies are used for this purpose. The features are classified using k-nearest neighbours, support vector machines, and artificial neural networks classifiers. The results show that the border of rocks grains can be determined with above 75% accuracy. The multi-texture approach was also investigated, leading to an increase in accuracy to over 79% for the early-fusion of features. Attempts were made to reduce feature space dimensionality by manually picking features as well as by the use of principal component analysis. The outcomes showed a significant decrease in accuracy. The obtained results have been visually compared with the ground truth. The compliance observed can be considered to be satisfactory.


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How to Cite

Nurzynska, K., & Iwaszenko, S. (2020). Application of Texture Features and Machine Learning Methods to Grain Segmentation in Rock Material Images. Image Analysis and Stereology, 39(2), 73–90.



Original Research Paper