Intelligent mineral segmentation in thin section images of rocks still remains a challenging task in modern computational mineralogy. The objective of the paper is segmenting minerals in geological thin section’s images with special attention on altered mineral segmentation. In this paper, an efficient incremental-dynamic clustering algorithm is developed for segmentation of minerals in thin sections containing altered and non-altered minerals. In the clustering algorithm, there is no need for determining the number of clusters (minerals) existed in thin section images, and also it is able to deal with color changing and non-evident boundaries in altered minerals. We have solved two main existing limitations: segmentation of mineral pixels that are frequently labeled as background pixels, and segmentation of thin sections containing altered minerals. Moreover, we created an open database (Alborz Mineralogical Database), as a benchmark database in computational geosciences regarding image studies of mineral. The proposed method is validated based on the results provided by the segmentation maps, and experimental results indicate that the proposed method is very efficient and outperforms previous segmentation methods for altered minerals in thin section images. The proposed method can be applied in mining engineering, rock mechanics engineering, geotechnique engineering, mineralogy, petrography, and applications such as NASA’s Mars Rover Explorations (MRE).