This study aimed to implement an unsupervised classification
method through the Gaussian mixture model to classify different
materials using the scatter diagram of the linear attenuation
coefficients acquired from dual-energy micro-CT imaging. This method
estimates each cluster's distribution parameters and performs
classification based on the posterior probability with a
pre-determined cluster number. Our studies on dual-energy images of
a phantom showed that the distribution of linear attenuation
coefficient of different materials on the scatter diagram has a
Gaussian distribution, and clusters can be classified using
model-based clustering. The result of this classification method is
related to the actual materials in the phantom, where a specific
cluster represents each material. This classification method can be
potentially used when the clusters are overlapped and the material
is separated with high accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.