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Algorithmic Lung Nodule Analysis in Chest Tomography Images

Title: Algorithmic Lung Nodule Analysis in Chest Tomography Images: Lung Nodule Malignancy Likelihood Prediction and a Statistical Extension of the Level Set Image Segmentation Method.
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Inaccessible until May 31, 2020 due to copyright restrictions.

Name(s): 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 department
Type of Resource: text
Genre: Text
Doctoral Thesis
Issuance: monographic
Date Issued: 2018
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (139 pages)
Language(s): English
Abstract/Description: 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.
Identifier: 2018_Sp_Hancock_fsu_0071E_14427 (IID)
Submitted Note: A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Spring Semester 2018.
Date of Defense: April 16, 2018.
Keywords: computer-aided diagnosis, image segmentation, level set method, lung nodule, machine learning
Bibliography Note: Includes bibliographical references.
Advisory Committee: Jerry Magnan, Professor Directing Dissertation; Dennis Duke, University Representative; Monica Hurdal, Committee Member; Washington Mio, Committee Member.
Subject(s): Applied mathematics
Diagnostic imaging
Radiography, Medical
Statistics
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/2018_Sp_Hancock_fsu_0071E_14427
Host Institution: FSU

Choose the citation style.
Hancock, M. C. (M. C. ). (2018). Algorithmic Lung Nodule Analysis in Chest Tomography Images: Lung Nodule Malignancy Likelihood Prediction and a Statistical Extension of the Level Set Image Segmentation Method. Retrieved from http://purl.flvc.org/fsu/fd/2018_Sp_Hancock_fsu_0071E_14427