\"Data fusion is a technique for combining data obtained from multiple sources for an enhanced detection or decision. Fusion of data can be done at the raw-data level, feature level or decision level. Applications of data fusion include defense (such as battlefield surveillance and autonomous vehicle control), medical diagnosis and structural health monitoring. Techniques for data fusion have been drawn from areas such as statistics, image processing, pattern recognition and computational intelligence. This dissertation includes investigation and development of methods to perform data fusion for nondestructive evaluation (NDE) and medical imaging applications. The general framework for these applications includes region-of-interest (ROI) detection followed by feature extraction and classification of the detected ROI. Image processing methods such as edge detection and projection-based methods were used for ROI detection. The features extracted from the detected ROIs include texture, color, shape/geometry and profile-based correlation. Analysis and classification of the detected ROIs was performed using feature- and decision-level data fusion techniques such as fuzzy-logic, statistical methods and voting algorithms\"--Abstract, page iv.
","abstract_html":"<p>"Data fusion is a technique for combining data obtained from multiple sources for an enhanced detection or decision. Fusion of data can be done at the raw-data level, feature level or decision level. Applications of data fusion include defense (such as battlefield surveillance and autonomous vehicle control), medical diagnosis and structural health monitoring. Techniques for data fusion have been drawn from areas such as statistics, image processing, pattern recognition and computational intelligence. This dissertation includes investigation and development of methods to perform data fusion for nondestructive evaluation (NDE) and medical imaging applications. The general framework for these applications includes region-of-interest (ROI) detection followed by feature extraction and classification of the detected ROI. Image processing methods such as edge detection and projection-based methods were used for ROI detection. The features extracted from the detected ROIs include texture, color, shape/geometry and profile-based correlation. Analysis and classification of the detected ROIs was performed using feature- and decision-level data fusion techniques such as fuzzy-logic, statistical methods and voting algorithms"--Abstract, page iv.</p>","abstract_has_math":false,"creators":["De, Soumya"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Electrical Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:19:38Z","subjects":["Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/2428","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["De, Soumya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Electrical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/2428"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"Data fusion is a technique for combining data obtained from multiple sources for an enhanced detection or decision. Fusion of data can be done at the raw-data level, feature level or decision level. Applications of data fusion include defense (such as battlefield surveillance and autonomous vehicle control), medical diagnosis and structural health monitoring. Techniques for data fusion have been drawn from areas such as statistics, image processing, pattern recognition and computational intelligence. This dissertation includes investigation and development of methods to perform data fusion for nondestructive evaluation (NDE) and medical imaging applications. The general framework for these applications includes region-of-interest (ROI) detection followed by feature extraction and classification of the detected ROI. Image processing methods such as edge detection and projection-based methods were used for ROI detection. The features extracted from the detected ROIs include texture, color, shape/geometry and profile-based correlation. Analysis and classification of the detected ROIs was performed using feature- and decision-level data fusion techniques such as fuzzy-logic, statistical methods and voting algorithms\"--Abstract, page iv.
"]},{"key":"dc:title","label":"Title","values":["Data fusion techniques for nondestructive evaluation and medical image analysis"]}]}],"canonical_facts":{"dc:creator":["De, Soumya"],"dc:date.available":["2016-02-10T08:00:00Z"],"dc:description.abstract":["\"Data fusion is a technique for combining data obtained from multiple sources for an enhanced detection or decision. Fusion of data can be done at the raw-data level, feature level or decision level. Applications of data fusion include defense (such as battlefield surveillance and autonomous vehicle control), medical diagnosis and structural health monitoring. Techniques for data fusion have been drawn from areas such as statistics, image processing, pattern recognition and computational intelligence. This dissertation includes investigation and development of methods to perform data fusion for nondestructive evaluation (NDE) and medical imaging applications. The general framework for these applications includes region-of-interest (ROI) detection followed by feature extraction and classification of the detected ROI. Image processing methods such as edge detection and projection-based methods were used for ROI detection. The features extracted from the detected ROIs include texture, color, shape/geometry and profile-based correlation. Analysis and classification of the detected ROIs was performed using feature- and decision-level data fusion techniques such as fuzzy-logic, statistical methods and voting algorithms\"--Abstract, page iv.
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/2428"],"dc:subject":["Electrical and Computer Engineering"],"dc:title":["Data fusion techniques for nondestructive evaluation and medical image analysis"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Electrical Engineering"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:19:38Z"}