Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/4879
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dc.contributor.authorCao, Aizeen
dc.date.accessioned2008-09-17T10:00:33Zen
dc.date.available2008-09-17T10:00:33Zen
dc.date.copyright2005en
dc.date.issued2005en
dc.identifier.citationCao, A. Z. (2005). Mammographic mass detection based on robust learning algorithms. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/4879en
dc.description.abstractThis thesis provides an in-depth investigation to develop advanced machine learning algorithms for automatic breast mass detection in digitized mammograms. The work consists of the establishment of software system to process the digitized mammographic images automatically. According to the character of masses and the background breast tissue in digitized mammograms, two image segmentation algorithms based on information theory and a new classifier based on statistical learning theory are proposed. The main contributions of this thesis include: the proposal DACF method in the segmentation of circumscribed mass, the investigation of RIC algorithm in the segmentation of masses that are embedded in glandular or dense glandular breast tissue, the study of VSVM for mass pattern analysis that are embedded in fat, glandular or dense glandular breast tissue with various shapes by a semi-automatic approach. In summary, novel and robust learning algorithms for the approaches of fully and semi-automatic detection of breast masses in digitized mammograms are proposed. The achieved results are hoped to be useful for the further investigation of automatic processing of mammograms and facilitate the clinical decision.en
dc.rightsNanyang Technological Universityen
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronicsen
dc.titleMammographic mass detection based on robust learning algorithmsen
dc.typeThesisen
dc.contributor.supervisorSong Qingen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en
dc.identifier.doi10.32657/10356/4879en
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