title: Mammogram-based discriminant fusion analysis for breast cancer diagnosis.
abstract: Mammogram-based classification is an important and effective way for computer-aided diagnosis (CAD)-based breast cancer diagnosis. In this paper, we present a novel discriminant fusing analysis (DFA)-based mammogram classification CAD-based breast cancer diagnosis. The discriminative breast tissue features are exacted and fused by DFA, and DFA achieves the optimal fusion coefficients. The largest class discriminant in the fused feature space is achieved by DFA for classification. Beside the detailed theory derivation, many experimental evaluations are implemented on Mammography Image Analysis Society mammogram database for breast cancer diagnosis.
authors.affiliation: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China. junbaolihit@gmail.com
authors.forename: Jun-Bao
authors.initials: JB
authors.lastname: Li
authors.forename: Yun-Heng
authors.initials: YH
authors.lastname: Wang
authors.forename: Lin-Lin
authors.initials: LL
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique