Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-23T14:55:53.117Z Has data issue: false hasContentIssue false

Automatic Computer Measurement of Selected Area Electron Diffraction Patterns from Asbestos Minerals

Published online by Cambridge University Press:  06 March 2019

J. C. Russ
Affiliation:
Materials Sci. & Eng. Dept., North Car. State Univ., Raleigh, NC
T. Taguchi
Affiliation:
Hitachi Scientific Instruments, Rockville, MD
P. M. Peters
Affiliation:
Div. Industrial Safety & Health, State of Wash., Olympia, WA
E. Chatfield
Affiliation:
Chatfield Technical Consulting, Mississauga, Ontario, Canada
J. C. Russ
Affiliation:
Biomedical Engineering Dept., University of Texas, Austin, TX
W. D. Stewart
Affiliation:
Dapple Systems, Sunnyvale, CA
Get access

Extract

Conventional selected area diffraction patterns as obtained in the TEM present difficulties for identification of materials such as asbestifonn minerals, although diffraction data is considered to be one of the preferred methods for making this identification. The preferred orientation of the fibers in each field of measurement, and the spotty patterns that are obtained, do not readily lend themselves to measurement of the integrated intensity values for each dspacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. To overcome these problems, we have implemented an automatic method for diffraction pattern measurement. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring.

Type
IX. Qualitative and Quantitative Phase Analysis Diffraction Applications
Copyright
Copyright © International Centre for Diffraction Data 1988

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. Heinrich, K.F.J., ed. (1980) Characterization of Particles National Bureau of Standards Special Publication 533Google Scholar
2. Russ, J.C. (1988) The Analysis and Measurement of Images, Engineering Extension Service, North Carolina State University, Raleigh NC Google Scholar
3. C, P.V.. Hough (1982) Method and Means for Recognizing Complex Patterns Patent, U.S. 3069654, Dec. 18, 1962Google Scholar
4. Duda, R.O., Hart, P.E. (1972) Use of the Hough Transformation to Detect Lines and Curves in Pictures Comm. Assoc. Comput. Mach, 1. 1115 Google Scholar
5. Ballard, D.H. (1981) Generalizing the Hough Transform to Detect Arbitrary ShapesPattern Recognition 13 #2, 111-122Google Scholar
6. Gonzalez, R.C., Wintz, P. (1987) Digital Image Processing, Addison Wesley, Reading MAGoogle Scholar
7. Russ, J.C. (1988) Automatic Methods for the measurement of curvature of lines, features and feature alignment in images Journal of Computer-Assisted Microscopy (in press)Google Scholar
8. Thetaplus+, Dapple Systems, 355 W. Olive Ave. , Sunnyvale, CA 94086Google Scholar
9. JCPDS, International Centre for Diffraction Data, 1601 Park Lane, Swarthmore, PA 19081Google Scholar
10. Russ, J.C., Hare, T.M., Lanzo, M.J. X-ray Diffraction Phase Analysis using Microcomputers, in Advances in X-ray Analysis vol. 25 (Russ, J.C. et. al. , ed,) Plenum Press, 1982 Google Scholar
11. Bright, D.S., Steel, E.B. Automatic Extraction of Regular Arrays of Spots from Electron Diffraction Images, J. of Microscopy, in pressGoogle Scholar
12. Carr, M.J. Automation of Electron Diffraction Analysis in an Analytical EM, in Electron Microscopy 1981 (Geiss, R.H., ed. ) San Francisco Press, p. 139146 Google Scholar