title: Automatic detection of microcalcifications using mathematical morphology and a support vector machine.
abstract: In this paper, we propose a novel method for the detection of microcalcifications using mathematical morphology and a support vector machine (SVM). First, the contrast in the original mammogram was improved by gamma correction and two carefully designed structural elements were used to enhance any microcalcifications. Next, the potential regions were extracted using our proposed dual-threshold technique. Finally, a SVM classifier was used to reduce the number of false positives. The performance of the proposed method was evaluated using the MIAS database. The experimental results demonstrated the efficiency and effectiveness of our method.
authors.affiliation: Department of Information Science, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China.
authors.forename: Erhu
authors.initials: E
authors.lastname: Zhang
authors.forename: Fan
authors.initials: F
authors.lastname: Wang
authors.forename: Yongchao
authors.initials: Y
authors.lastname: Li
authors.forename: Xiaonan
authors.initials: X
authors.lastname: Bai
mesh_terms: D000465:Algorithms; D001940:Breast / Q000473:pathology; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000473:pathology*; D016208:Databases, Factual; D001330:Electronic Data Processing; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008962:Models, Theoretical; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: feature extraction; mathematical morphology; microcalcification; support vector machine