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.

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)

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