Automated image segmentation-assisted flattening of atomic force microscopy images
| Autor: | Tongda Lu, Xiaolai Li, Yuliang Wang, Huimin Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Polynomial
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Physics and Astronomy 02 engineering and technology Astrophysics::Cosmology and Extragalactic Astrophysics sliding window 010402 general chemistry lcsh:Chemical technology 01 natural sciences lcsh:Technology Flattening Full Research Paper Image (mathematics) Sliding window protocol Nanotechnology General Materials Science Segmentation Computer vision lcsh:TP1-1185 Electrical and Electronic Engineering lcsh:Science Astrophysics::Galaxy Astrophysics atomic force microscopy business.industry lcsh:T polynomial fitting Process (computing) Image segmentation 021001 nanoscience & nanotechnology contour expansion lcsh:QC1-999 0104 chemical sciences Nanoscience Data point image flattening Computer Science::Computer Vision and Pattern Recognition lcsh:Q Artificial intelligence 0210 nano-technology business lcsh:Physics |
| Zdroj: | Beilstein Journal of Nanotechnology, Vol 9, Iss 1, Pp 975-985 (2018) Beilstein Journal of Nanotechnology |
| ISSN: | 2190-4286 |
| Popis: | Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method. |
| Databáze: | OpenAIRE |
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