![]() Campane Vidal, Segmentation of sandstone thin section images with separation of touching grains using optimum path forest operators, Comput. Scholz, Particle-size distribution and microstructures within simulated fault gouge, J. Guo, GIS-based detection of grain boundaries, J. Bouchez, Mineral recognition in digital images of rocks a new approach using multichannel classification, Can. Parametrisierung und Regionalisierung der Lithologie des Mittleren Buntsandsteins im Bereich des Göttinger Waldes (unpublished B.Sc. Heilbronner, Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images, J. Ye, Detecting grain boundaries in deformed rocks using a cellular automata approach, Comput. Fueten, Edge detection in petrographic images using the rotating polarizer stage, Comput. In: Proceedings of the 7th World Petroleum Congress, 2, pp. Influence of different types of diagenesis on sandstone porosity. Dobereiner, Geotechnical properties of weak sandstones, Géotechnique, 36 (1986) 79-94. Francus, An image-analysis technique to measure grain-size variation in thin sections of soft clastic sediments, Sediment. Böhner, System for Automated Geoscientific Analyses (SAGA) v. Universität Göttingen, Göttingen, Germany, 221 pp. Funktionsumfang und Anwendung eines Systems für Automatisierte Geowissenschaftliche Analysen. Mulchrone, Automated grain boundary detection by CASRG, J. Grieve, A methodology for the semi¿automatic digital image analysis of fragmental impactites, Meteorit. Tarquini, Influence of granitoid textural parameters on sediment composition: implications for sediment generation, Sediment. Rihosek, Sandstone landforms shaped by negative feedback between stress and erosion, Nat. Geographisches Institut, Goltze, Göttingen, Germany. SAGA-analysis and modelling applications, Göttinger geographische Abhandlungen: GGA/Ed. Barraud, The use of watershed segmentation and GIS software for textural analysis of thin sections, J. Bischof, Seeded region growing, IEEE Trans. We present a new semi-automatic image segmentation workflow for the quantitative analysis of microscopic grain fabrics.The workflow uses an automated seeded region growing algorithm.The workflow is implemented in the open-source Geographic Information System (GIS) software SAGA. The results of the image analysis are utilized to assess the weathering susceptibility of the sandstone samples and point to the importance of cementation determining the geotechnical properties of a given sandstone sample. Based on the segmentation results obtained from the samples, a number of parameters, including modal composition, geometry of grain contacts, porosity, and grain size distribution were determined and statistically evaluated. To demonstrate the effectiveness of the workflow, 39 transmitted light images of 13 weathered sandstone samples of the Buntsandstein Formation in northwestern Germany were analyzed. Specifically, grain contacts are automatically identified by lines of intersection and manually classified by contact type to characterize the mineral fabric of petrographic thin sections. SAGA's capabilities for vector data analysis offer instant calculation and visualization of the compiled geometric database within a GIS environment. It also features a graphical user interface that allows the user to simultaneously display and link multiple images and, thus, facilitates manual post-processing of the images. SAGA provides all required tools for image analysis and geographic referencing. The workflow is implemented in the open-source Geographic Information System (GIS) software SAGA (System for Automated Geoscientific Analyses). The workflow uses an automated seeded region growing algorithm, which is based on variance analysis of five or more RGB images. We present a new semi-automatic image segmentation workflow for the quantitative analysis of microscopic grain fabrics. Accurate imaging of minerals in petrographic thin sections using (semi)-automatic image segmentation techniques remains a challenging task chiefly due to the optical similarity of adjacent grains or grain aggregates rendering definition of grain boundaries difficult.
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