Automated Single Particle Growth Measurement using Segmentation

Fine-grain imaging is revealing secrets of nature with every passing day and artificial intelligence is reducing the manual effort required for detailed analysis. This work proposes an automated growth measurement of a particle in electron microscopic images in real-time. The particle selected in this study is an Au spiky nanoparticle (SNP) that develops spikes over the course of its growth. In this study, multiple techniques from conventional and sophisticated algorithms are used to segment the particle using supervised and unsupervised learning techniques. A comprehensive analysis of the automated techniques is presented with qualitative and quantitative results.