Automating Cavity Analysis in Microscopy Images

by Rithvik Kundarapu
In the world of material sciences study, particularly when looking at the effects of radiation on materials, it is important to be able to reliably and conveniently assess materials for the state of radiation damage that they may have incurred during use. To count out the cavities present in microscopy images of the material would be too tedious, so instead I aimed to build an algorithm to do so automatically by implementing machine learning.
Major: 
Nuclear Engineering
Exhibition Category: 
Engineering
Exhibition Format: 
Poster Presentation
Campus: 
University Park
Faculty Sponsor: 
Xing Wang
Poster Number: 
16061