NCSI: Machine Learning Enhanced Biomedical 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 Biomedical Image Segmentation and Visualizations
Summary	This project will conduct biomedical 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 computational modeling or diagnostic analysis.
Job Description	The student will learn and apply open source ImageJ software package to conduct biomedical 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.
Computational Resources	We will use XSEDE Comet and superMIC to perform image segmentation and visualizations
Contribution to Community
Position Type	Learner
Training Plan	A training will be provided for the student to use ImageJ software, both using the the conventional watershed-based method and the TWS machine learning technique.
Student Prerequisites/Conditions/Qualifications
Duration
Start Date	05/20/2019
End Date	08/20/2019