"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
Xiao, Hai, Dr.
Stoecker, William V.
Moss, Randy Hays, 1953-
Electrical and Computer Engineering
Ph. D. in Electrical Engineering
Missouri University of Science and Technology
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
© 2012 Beibei Cheng, All rights reserved.
Dissertation - Open Access
Library of Congress Subject Headings
Image processing -- Analysis -- Technique
Multisensor data fusion
Print OCLC #
Electronic OCLC #
Link to Catalog Recordhttp://laurel.lso.missouri.edu/record=b9639079~S5
Cheng, Beibei, "Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification" (2012). Doctoral Dissertations. 1974.