Doctoral Dissertations


Beibei Cheng


"Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest' detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification"--Abstract, page v.


Stanley, R. Joe

Committee Member(s)

Xiao, Hai, Dr.
Yin, Zhaozheng
Stoecker, William V.
Moss, Randy Hays, 1953-


Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering


Missouri University of Science and Technology

Publication Date

Fall 2012

Journal article titles appearing in thesis/dissertation

  • Analysis of clinical and dermoscopic features for basal cell carcinoma neural network
  • Automatic dirt trail analysis in dermoscopy images
  • Automatic telangiectasia analysis in dermoscopy images using adaptive cirtic [sic] design
  • Automatic detection of arrow annotation overlays in biomedical images
  • Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images
  • Graphical figure classification using data fusion for integrating text and image features
  • Graphical image classification combining an evolutionary algorithm and binary particle swarm optimization
  • Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval
  • Hybrid computational intelligence algorithm for automatic skin lesion segmentation in dermoscopy images
  • Novel computational intelligence-based approach for medical image artifacts detection


xix, 182 pages

Note about bibliography

Includes bibliographical references.


© 2012 Beibei Cheng, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Subject Headings

Computational intelligence
Genetic algorithms
Image processing -- Analysis -- Technique
Machine learning
Multisensor data fusion
Swarm intelligence

Thesis Number

T 10135

Print OCLC #


Electronic OCLC #