Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-16T19:51:44.889Z Has data issue: false hasContentIssue false

Edge Sharpening For Unbiased Edge Detection in Field Emission Scanning Electron Microscope (FESEM) Images

Published online by Cambridge University Press:  02 July 2020

P. Markondeya Raj
Affiliation:
Department of Ceramic and Materials Science and Engineering, Piscataway, NJ, 08855
Stanley M. Dunn
Affiliation:
Department of Biomedical Engineering Rutgers - The State University of New Jersey, Piscataway, NJ, 08855
W. Roger Cannon
Affiliation:
Department of Ceramic and Materials Science and Engineering, Piscataway, NJ, 08855
Get access

Extract

Mathematical representation of particle orientation and shape can show quantitative differences in particle morphology and microstructures of ceramic green bodies with apparently equiaxed powders. In this work, fourier descriptors and moment invariants were used to quantify the shape of ceramic particles and identify their orientation. Edges constitute a significant portion of the information contained in these images, and hence edge detection forms an important issue in these image processing algorithms.

A Field Emission Scanning Electron Microscope (FESEM, Leo 982) was used to collect images of various ceramic green bodies in order to assess the ceramic particle shape and orientation. The images were collected and displayed as rectangular pixels on the microscope monitor which were then stored as square pixels (square-pixel-interpolation). Application of various edge detection algorithms on the stored images show strong bias to edges in the direction perpendicular to the scanning direction.

Type
Applied Image Processing: What it Can do for Digital Imaging
Copyright
Copyright © Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Kuhl, Frank P. and Giardina, Charles R., Computer Graphics Image Processing, 18 (1982) 236258.CrossRefGoogle Scholar
2.Haralick, Robert M. and Shapiro, Linda G., Computer and Robot Vision, Addison-Wesley Pub. Co., c 1992.Google Scholar
3.Hardie, Russell C. and Boncelet, Charles G., IEEE Trans. Image Processing, 4 (11) (1995) 15721577.CrossRefGoogle Scholar