Robust Nanoparticles Detection From Noisy Background by Fusing Complementary Image Information.

This paper studies the problem of detecting the presence of nanoparticles in noisy transmission electron microscopic (TEM) images and then fitting each nanoparticle with an elliptic shape model. In order to achieve robustness while handling low contrast and high noise in the TEM images, we propose an approach to fuse two kinds of complementary image information, namely, the pixel intensity and the gradient (the first derivative in intensity). Our approach entails two main steps: 1) the first step is to, after necessary pre-processing, employ both intensity-based information and gradient-based information to process the same TEM image and produce two independent sets of results and 2) the subsequent step is to formulate a binary integer programming (BIP) problem for conflict resolution among the two sets of results. Solving the BIP problem determines the final nanoparticle identification. We apply our method to a set of TEM images taken under different microscopic resolutions and noise levels. The empirical results show the merit of the proposed method. It can process a TEM image of $1024\\times 1024$ pixels in a few minutes, and the processed outcomes appear rather robust.