|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images
||Mingjian Lu, Sameera Nalin Venkat, Jube Augustino, Jayvic Cristian Jimenez, Pawan K. Tripathi, Yinghui Wu, Roger H. French, Laura S. Bruckman
|On-Site Speaker (Planned)
Fluoroelastomer crystallization can be easily observed using atomic force microscopy, AFM, to look at surface properties and macro-scale morphologies. In-situ measurements investigating phase-transition kinetics of fluoropolymers, under isothermal heating, generate a large dataset of time-lapsed image sequences. Interpretation of the resulting images is guided by domain-knowledge and image processing is done manually using software, which is time-consuming. In our work, we integrate automated image detection and image segmentation methods, based on convolutional neural networks in our image processing. The resulting pipeline is an end-to-end framework, which aims to automatically classify and analyze images as part of batch processing. The product of this framework can extract individual crystallites and track their growth throughout the course of the image sequence with only a few training data. Then, statistical analysis can then be incorporated opening opportunities to investigate fluoroelastomer crystallization kinetics.