Lung cancer has the highest mortality rate of all cancers in both men and women in the United States. The algorithmic detection, characterization, and diagnosis of abnormalities found in chest CT scan images can aid radiologists by providing additional medically-relevant information to consider in their assessment of medical images. Such algorithms, if robustly validated in clinical settings, carry the potential to improve the health of the general population. In this thesis, we first give an analysis of publicly available chest CT scan annotation data, in which we determine upper bounds on expected classification accuracy when certain radiological features are used as inputs to statistical learning algorithms for the purpose of inferring the likelihood of a lung nodule as being either malignant or benign. Second, a statistical extension of the level set method for image segmentation is introduced and applied to both synthetically-generated and real three-dimensional image volumes of lung nodules in chest CT scans, obtaining results comparable to the current state-of-the-art on the latter. / A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2018. / April 16, 2018. / computer-aided diagnosis, image segmentation, level set method, lung nodule, machine learning / Includes bibliographical references. / Jerry Magnan, Professor Directing Dissertation; Dennis Duke, University Representative; Monica Hurdal, Committee Member; Washington Mio, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_654723 |
| Contributors | Hancock, Matthew C. (Matthew Charles) (author), Magnan, Jeronimo Francisco, 1953- (professor directing dissertation), Duke, D. W. (university representative), Hurdal, Monica K. (committee member), Mio, Washington (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Mathematics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (139 pages), computer, application/pdf |
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