title: Breast cancer diagnosis in digital mammogram using multiscale curvelet transform.
abstract: This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.
authors.affiliation: Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia. tokhy2478@yahoo.com
authors.forename: Mohamed Meselhy
authors.initials: MM
authors.lastname: Eltoukhy
authors.forename: Ibrahima
authors.initials: I
authors.lastname: Faye
authors.forename: Brahim Belhaouari
authors.initials: BB
authors.lastname: Samir
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity