Automatic mineral segmentation in petrographic thin sections using image processing and clustering algorithms

Mineral segmentation in thin sections based on image processing algorithms is one of the popular research topics geosciences. Rocks are the main information resource for geological studies, and mounting thin section from rocks is the most popular method for studying them. Mineral segmentation in thin sections is also the pre-step for further studying on thin sections such as mineral identification and measuring the size of minerals. In this paper, a new method for mineral segmentation based on image processing and clustering algorithms is proposed for mineral segmentation in thin sections. In order to segment minerals, using a polarizer microscope, two images in plane and cross polarized lights are captured from each thin sections, and by extracting the color features from the images, minerals inside each thin section are segmented. Therefore, initially, the color features including RGB and HSI components are extracted for each pixels for both images, and then using image processing and clustering algorithms the pixels are clustered and each cluster is related to a segmented mineral. Experimental results indicate that the proposed algorithm produces accurate and reliable results, especially for those thin sections containing altered minerals. The proposed algorithm can be used in such applications as petroleum geology, mineralogy training and NASA mars exploration.