Masters Theses
Keywords and Phrases
Cervical cancer; feature extraction; image processing; neural network
Abstract
"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development from vertical segment partitions of the epithelium region for the same digitized histology images. Using features developed in this thesis with prior work, a particle swarm optimization and Receiver Operating Characteristic curve (ROC) explored for CIN classification showing exact grade labeling accuracy as high as 90%."--Abstract, page iii.
Advisor(s)
Stanley, R. Joe
Committee Member(s)
Moss, Randy Hays, 1953-
Stoecker, William V.
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Electrical Engineering
Sponsor(s)
National Library of Medicine (U.S.)
Publisher
Missouri University of Science and Technology
Publication Date
Summer 2014
Pagination
x, 83 pages
Note about bibliography
Includes bibliographical references (pages 81-82).
Rights
© 2014 Peng Guo, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Cervix uteri -- CancerCancer -- HistopathologyImage processing
Thesis Number
T 10510
Electronic OCLC #
894579438
Recommended Citation
Guo, Peng, "Cervical cancer histology image feature extraction and classification" (2014). Masters Theses. 7302.
https://scholarsmine.mst.edu/masters_theses/7302
Included in
Bioimaging and Biomedical Optics Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons, Radiology Commons