A new approach to apply texture features in minerals identification in petrographic thin sections using ANNs

Identification of minerals in petrographic thin sections using intelligent methods is very complex and challenging task which, mineralogists and computer scientists are faced with it. Textural features have very important role to identify minerals, and undoubtedly without using these features, recognition minerals in thin sections yield to many miss classification results. Thin sections have been studied applying plane-polarized and cross-polarized lights. In this paper, in order to extract textural features of minerals in thin section, co-occurrence matrix is used, and six features as Entropy, Homogeneity, Energy, Correlation and Maximum Probability are extracted from each image. Then, ANNs are used for identifying in complex situation and experimental results have shown that using textural features in mineral identification, significant improve classification result in petrographic thin sections.