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.