Recognition of Minerals in Thin Sections Using Color Image Processing

In the traditional methods of analyzing minerals in thin sections, the boundaries of the minerals were manually separated and each section was labeled. This approach is expensive and requires high expertise and experience. Therefore, an automatic identification system is essential in this field. Such a system can increase the accuracy and reduce human error, cost and time of mineral identification. The aim of this study is to propose an automated identification system which uses image processing to identify and classify existing minerals.The main steps of the proposed method include collecting images from thin sections, segmentation, feature extraction and classification. After creating the image database, the JSEG algorithm is applied for segmentation. Then, the color and texture features in both RGB and HSI color spaces are extracted from each region and are sent to the classifier for classification, which labels each segment as a mineral. In this study, the efficiency of six different classifiers has been evaluated. According to the results, the Bagged Tree classifier has the highest accuracy of 95.52% and the lowest Mean Absolute Error of 0.04. Also, all classifiers have accuracies of over 93%, which indicates that the proposed feature extraction method is able to properly identify minerals.