NCSI: Machine Learning Enhanced Micro-CT Image Segmentation and Visualizations

Status	Completed
Mentor Name	Jun Li
Mentor's XSEDE Affiliation	Research Allocation
Mentor Has Been in XSEDE Community	Less than 1 year
Project Title	Machine Learning Enhanced Micro-CT Image Segmentation and Visualizations
Summary	This project will conduct Micro-CT image segmentation and visualizations enhanced by machine learning techniques. A key challenge in using the X-ray CT imaging technique is to accurately segment material phases in contact so as to faithfully visualize the morphology, which provides the basis for image-based finite element modeling and analysis.
Job Description	The student will learn and apply the open-source ImageJ software package to conduct Micro-CT image segmentation and visualizations. In particular, the Trainable Weka Segmentation (TWS) machine learning technique as a Fiji plugin in ImageJ will be investigated to enhance image segmentation of material phases over the conventional watershed-based method. The student will transform the reconstructed images to finite element models for future analysis.
Computational Resources	We will use XSEDE Comet and superMIC to perform image segmentation and visualizations
Contribution to Community
Position Type	Apprentice
Training Plan	Training will be provided for the student to use ImageJ software, both using the conventional watershed-based method and the TWS machine learning technique.
Student Prerequisites/Conditions/Qualifications	Must have programming skills such as in Matlab or Python to develop user scripts. Computational modeling skills are desired in order to transform the images into finite element models.
Duration	Semester
Start Date	09/01/2019
End Date	11/30/2019