Mineral segmentation is an equally important and difficult task in the quantification of mineral composition. Difficulties come from the process of determining boundaries of distinctive mineral grains necessary for further analysis and mineral identification. Done by hand, the task is very time-consuming and higher accuracies are burdened with the possible human fatigue factor. The presented method is a fully automated solution to the problem that uses a superpixel approach and feature-based merging. The method is validated by comparison with the manual approach. Analyzed data consist of photos taken by a Nikon Eclipse LV100N POL polarizing microscope at 200× magnification, in transmitted light, with crossed polarizers. Images are first prepared by Gaussian filter and meanshift operations, then the initial segmentation is provided by the superpixel algorithm. Oversegmentation is resolved by feature-based merging. The last step consists of counting the individual grain boundaries and preparing the results as easily readable visual data.
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