title: Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.
abstract: Breast cancer is one of the most perilous diseases among women. Breast screening is a method of detecting breast cancer at a very early stage which can reduce the mortality rate. Mammography is a standard method for the early diagnosis of breast cancer. In this paper, a new algorithm is proposed for breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution (SR). The presented algorithm includes three main parts including pre-processing, feature extraction and classification. In the pre-processing stage, after determining the region of interest (ROI) by an automatic technique, the quality of image is improved using NSCT and SR algorithm. In the feature extraction part, several features of the image components are extracted and skewness of each feature is calculated. Finally, AdaBoost algorithm is used to classify and determine the probability of benign and malign disease. The obtained results on Mammographic Image Analysis Society (MIAS) database indicate the significant performance and superiority of the proposed method in comparison with the state of the art approaches. According to the obtained results, the proposed technique achieves 91.43% and 6.42% as a mean accuracy and FPR, respectively.
authors.affiliation: Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. Electronic address: fatemehpak@qiau.ac.ir.
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Pak
authors.affiliation: Department of Electrical, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. Electronic address: h.rashidykanan@qiau.ac.ir.
authors.forename: Hamidreza Rashidy
authors.initials: HR
authors.lastname: Kanan
authors.affiliation: Department of Radiology, Faculty of medicine, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: a-alikhasi@sina.tums.ac.ir.
authors.forename: Afsaneh
authors.initials: A
authors.lastname: Alikhassi
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D000069550:Machine Learning; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: BI-RADS; Breast cancer; Computer-Aided Diagnosis (CAD) system; Mammography; Non-Subsampled Contourlet Transform (NSCT); Super Resolution (SR)