Doctoral Dissertations


Peng Guo

Keywords and Phrases

Cervical Cancer; Data Fusion; Feature Extraction; Machine Learning


"Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms"--Abstract, page iv.


Stanley, R. Joe

Committee Member(s)

Moss, Randy Hays, 1953-
Stoecker, William V.
Wunsch, Donald C.
Shrestha, Bijaya
Samaranayake, V. A.


Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering


U.S. National Institute of Health Intramural Research Program
National Library of Medicine (U.S. )
Lister Hill National Center for Biomedical Communications


This work was supported by NLM under contract number 276200800413P


Missouri University of Science and Technology

Publication Date

Spring 2018

Journal article titles appearing in thesis/dissertation

  • Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification
  • Enhancements in localized classification for uterine cervical cancer digital histology image assessment
  • Features advances to automatically find images for application to clinical decision support


xiv, 121 pages

Note about bibliography

Includes bibliographic references.


© 2018 Peng Guo, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11281

Electronic OCLC #