A Novel Image Segmentation Approach for Microstructure Modelling
AbstractMicrostructure models are used to investigate bulk properties of a material given images of its microstructure. Through their use the effect of microstructural features can be investigated independently. Processes can then be optimised to give the desired selection of microstructural features. Currently automatic methods of segmenting SEM images either miss cracks leading to large overestimates of properties or use unjustifiable methods to select a threshold point which class cracks as porosity leading to over estimates of porosity. In this work, a novel automatic image segmentation method is presented which produces maps for each phase in the microstructure and an additional phase of cracks. The selection of threshold points is based on the assumption that the brightness values for each phase should be normally distributed. The image segmentation method has been compared to other available methods and shown to be as or more repeatable with changes of brightness and contrast of the input image than relevant alternatives. The resulting modelling route is able to predict density and specific heat to within experimental error, while the expected under predictions for thermal conductivity are observed. View Full-Text
- Supplementary File 1:
Supplementary (ZIP, 5 KB)
Externally hosted supplementary file 1
Description: Code provided though links in the text and the above link
Share & Cite This Article
Watson, M.; Marshall, M. A Novel Image Segmentation Approach for Microstructure Modelling. Coatings 2017, 7, 166.
Watson M, Marshall M. A Novel Image Segmentation Approach for Microstructure Modelling. Coatings. 2017; 7(10):166.Chicago/Turabian Style
Watson, Michael; Marshall, Matthew. 2017. "A Novel Image Segmentation Approach for Microstructure Modelling." Coatings 7, no. 10: 166.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.