Keywords and Phrases
Cervical cancer; feature extraction; image processing; neural network
"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.
Stanley, R. Joe
Moss, Randy Hays, 1953-
Stoecker, William V.
Electrical and Computer Engineering
M.S. in Electrical Engineering
National Library of Medicine (U.S.)
Missouri University of Science and Technology
x, 83 pages
© 2014 Peng Guo, All rights reserved.
Thesis - Open Access
Cervix uteri -- Cancer
Cancer -- Histopathology
Electronic OCLC #
Guo, Peng, "Cervical cancer histology image feature extraction and classification" (2014). Masters Theses. 7302.