Date of Award
12-31-2021
Document Type
Campus Access Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Daniel Haehn
Second Advisor
Dan Simovici
Third Advisor
Funda Durupinar Babur
Abstract
To investigate if adding biological features can improve the existing registration process in state-of-art and deep learning networks, mitochondria masks or lung masks data were used to guide the alignment procedures in real-time. The input datasets consist of unaligned 2D electron microscopy (EM) images that are computationally expensive to map and create 3D volumetric datasets. Feature matching methods and a deep learning framework, MONAI, were implemented to align 2D EM images and 3D lung CT scans, respectively. This approach will guide the registration methods to run faster and with better accuracy for biomedical image analysis.
Recommended Citation
Goyal, Neha, "Image Registration With and Without Labeled Mask" (2021). Graduate Masters Theses. 725.
https://scholarworks.umb.edu/masters_theses/725
Comments
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