Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize wheathered subarkose sandstone

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. We present a new semi-automatic...

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Vydané v:Computers & geosciences Ročník 83; s. 89 - 99
Hlavní autori: Asmussen, Pascal, Conrad, Olaf, Günther, Andreas, Kirsch, Moritz, Riller, Ulrich
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.10.2015
Predmet:
algorithms
automation
computer software
computers
Fabric analysis
Geographic Information System (GIS)
Geographic information systems
Germany
Grain object detection
Grains
image analysis
minerals
particle size distribution
porosity
quantitative analysis
Samples
Sandstone
Sedimentary rocks
Seeded region growing
Statistical analysis
Statistical methods
Thin section images
user interface
vector data
weathering
Workflow
Germany
Seeded region growing
Fabric analysis
Thin section images
Grain object detection
Geographic Information System (GIS)
Sedimentary rocks
ISSN:0098-3004, 1873-7803
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Popis
Shrnutí: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. 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, which is based on variance analysis of five or more RGB images. The workflow is implemented in the open-source Geographic Information System (GIS) software SAGA (System for Automated Geoscientific Analyses). SAGA provides all required tools for image analysis and geographic referencing. 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's capabilities for vector data analysis offer instant calculation and visualization of the compiled geometric database within a GIS environment. 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. 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. 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. 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. •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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2015.05.001