PUBLICATION: NUCLEAR SCIENCE AND TECHNIQUES

AUTHORS: Wu, ZH; Bai, JJ; Zhang, DD; Huang, G; Zhu, TB; Chang, XJ; Liu, RD; Lin, J; Sun, JA

 

Helium bubbles, which are typical radiation microstructures observed in metals or alloys, are usually investigated using transmission electron microscopy (TEM). However, the investigation requires human inputs to locate and mark the bubbles in the acquired TEM images, rendering this task laborious and prone to error. In this paper, a machine learning method capable of automatically identifying and analyzing TEM images of helium bubbles is proposed, thereby improving the efficiency and reliability of the investigation. In the proposed technique, helium bubble clusters are first determined via the density-based spatial clustering of applications with noise algorithm after removing the background and noise pixels. For each helium bubble cluster, the number of helium bubbles is determined based on the cluster size depending on the specific image resolution. Finally, the helium bubble clusters are analyzed using a Gaussian mixture model, yielding the location and size information on the helium bubbles. In contrast to other approaches that require training using numerous annotated images to establish an accurate classifier, the parameters used in the established model are determined using a small number of TEM images. The results of the model formulated according to the proposed approach achieved a higher F1 score validated through some helium bubble images manually marked. Furthermore, the established model can identify bubble-like objects that humans cannot facilely identify. This computationally efficient method achieves object recognition for material structure identification that may be advantageous to scientific work.

 

LINKS: http://dx.doi.org/10.1007/s41365-021-00886-y