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Proceedings Paper

Application of particle analysis to transmission electron microscopy (TEM)
Author(s): J. DaPonte; T. Sadowski; C. C. Broadbridge; D. Day; A. H. Lehman; D. Krishna; L. Marinella; P. Munhutu; M. Sawicki
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Paper Abstract

Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their size may give them properties that are very different from bulk materials. Further advancement of nanotechnology cannot be obtained without an increased understanding of nanoparticle properties such as size (diameter) and size distribution frequently evaluated using transmission electron microscopy (TEM). In the past, these parameters have been obtained from digitized TEM images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. More recently, computer imaging particle analysis has emerged as an objective alternative by counting and measuring objects in a binary image. This paper will describe the procedures used to preprocess a set of gray scale TEM images so that they could be correctly thresholded into binary images. This allows for a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Several preprocessing methods including pseudo flat field correction and rolling ball background correction were investigated with the rolling ball algorithm yielding the best results. Examples of particle analysis will be presented for different types of materials and different magnifications. In addition, a method based on the results of particle analysis for identifying and removing small noise particles will be discussed. This filtering technique is based on identifying the location of small particles in the binary image and removing them without affecting the size of other larger particles.

Paper Details

Date Published: 25 April 2007
PDF: 8 pages
Proc. SPIE 6575, Visual Information Processing XVI, 65750H (25 April 2007); doi: 10.1117/12.714749
Show Author Affiliations
J. DaPonte, Southern Connecticut State Univ. (United States)
T. Sadowski, Southern Connecticut State Univ. (United States)
C. C. Broadbridge, Southern Connecticut State Univ. (United States)
D. Day, Southern Connecticut State Univ. (United States)
A. H. Lehman, Southern Connecticut State Univ. (United States)
D. Krishna, Southern Connecticut State Univ. (United States)
L. Marinella, Southern Connecticut State Univ. (United States)
P. Munhutu, Southern Connecticut State Univ. (United States)
M. Sawicki, Southern Connecticut State Univ. (United States)


Published in SPIE Proceedings Vol. 6575:
Visual Information Processing XVI
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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