Abstract
As the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for researching the ability to recognize GBM is that the underlying cause of the mutation is presently unclear, leading to the operative that GBM is only detectable through a combination of MRI and CT brain scans, cooperatively, along with a resection biopsy. Since astrocytoma only becomes evident at critical mass, when the cellular structure of the neoplasm becomes visible within the image, this research seeks to achieve earlier identification and segmentation of the neoplasm by evaluating the malignant area via a volumetric voxel approach to removing noise artifacts and analyzing voxel differentials. In order to investigate neoplasm continuity, a differential approach has been implemented utilizing a multi-polynomial/multi-domain regression algorithm, thus, ultimately, providing a graphical and mathematical analysis of the differentials within critical mass and non-critical mass images. Given these augmentations to MRI and CT image rectifications, we theorize that our approach will improve on astrocytoma recognition and segmentation, along with achieving greater accuracy in diagnostic evaluations of the malignant area.
Notes
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Graduation Date
2018
Semester
Fall
Advisor
Hughes, Charles
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Degree Program
Modeling and Simulation
Format
application/pdf
Identifier
CFE0007336
URL
http://purl.fcla.edu/fcla/etd/CFE0007336
Language
English
Release Date
December 2019
Length of Campus-only Access
1 year
Access Status
Masters Thesis (Open Access)
STARS Citation
Higgins, Lyn, "The Identification and Segmentation of Astrocytoma Prior to Critical Mass, by means of a Volumetric/Subregion Regression Analysis of Normal and Neoplastic Brain Tissue" (2018). Electronic Theses and Dissertations. 6254.
https://stars.library.ucf.edu/etd/6254