The use of Transmission Electron Microscopy (TEM) to characterize the microstructure of a material continues to grow in importance as technological advancements become increasingly more dependent on nanotechnology1 . Since nanoparticle properties such as size (diameter) and size distribution are often important in determining potential applications, a particle analysis is often performed on TEM images. Traditionally done manually, this has the potential to be labor intensive, time consuming, and subjective2. To resolve these issues, automated particle analysis routines are becoming more widely accepted within the community3. When using such programs, it is important to compare their
performance, in terms of functionality and cost. The primary goal of this study was to apply one such software package, ImageJ to grayscale TEM images of nanoparticles with known size. A secondary goal was to compare this popular open-source general purpose image
processing program to two commercial software packages. After a brief investigation of performance and price, ImageJ was identified as the software best suited for the particle analysis conducted in the study. While many ImageJ functions were used, the ability to break agglomerations that occur in specimen preparation into separate particles using a watershed algorithm was particularly helpful4.
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 decreased size may give rise to certain properties that are very different from those
exhibited by bulk materials. Further advancement of nanotechnology cannot be realized
without an increased understanding of nanoparticle properties such as size (diameter) and
size distribution. Frequently, these parameters are evaluated using numerous imaging
modalities including transmission electron microscopy (TEM) and atomic force
microscopy (AFM). In the past, these parameters have been obtained from digitized
images by manually measuring and counting many of these nanoparticles, a task that is
highly subjective and labor intensive.
Recently, computer imaging particle analysis routines that count and measure objects in a binary image1 have emerged as an objective and rapid alternative to manual techniques.
In this paper a procedure is described that can be used to preprocess a set of gray scale images so that they are correctly thresholded into binary images prior to a particle analysis ultimately resulting in a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Particle analysis was performed on two types of calibration samples imaged using AFM and TEM. Additionally, results of particle analysis can be used for identifying and removing small noise particles from the image. This filtering technique is based on identifying the location of small particles in the binary image, assessing their size, and removing them without affecting the size of other larger particles.
Thresholding is an image processing procedure used to convert an image consisting of
gray level pixels into a black and white binary image. One application of thresholding is
particle analysis. Once foreground objects are separated from the background, a
quantitative analysis that characterizes the number, size and shape of particles is obtained
which can then be used to evaluate a series of nanoparticle samples.
Numerous thresholding techniques exist differing primarily in how they deal with
variations in noise, illumination and contrast. In this paper, several popular thresholding
algorithms are qualitatively and quantitatively evaluated on transmission electron
microscopy (TEM) and atomic force microscopy (AFM) images. Initially, six
thresholding algorithms were investigated: Otsu, Riddler-Calvard, Kittler, Entropy, Tsai
and Maximum Likelihood. The Riddler-Calvard algorithm was not included in the
quantitative analysis because it did not produce acceptable qualitative results for the
images in the series.
Two quantitative measures were used to evaluate these algorithms. One is based on
comparing object area the other on diameter before and after thresholding. For AFM
images the Kittler algorithm yielded the best results followed by the Entropy and
Maximum Likelihood techniques. The Tsai algorithm yielded the top results for TEM
images followed by the Entropy and Kittler methods.
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.
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