Please use this identifier to cite or link to this item:
Title: Mammographic mass detection based on robust learning algorithms
Authors: Cao, Aize
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Issue Date: 2005
Source: Cao, A. Z. (2005). Mammographic mass detection based on robust learning algorithms. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This 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.
DOI: 10.32657/10356/4879
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
EEE-THESES_87.pdf6.58 MBAdobe PDFThumbnail

Page view(s) 50

Updated on Jun 20, 2021

Download(s) 5

Updated on Jun 20, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.