title: Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression.
abstract: In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases-including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.
authors.affiliation: Department of Electronics and Telecommunication Engineering, G. H. Raisoni College of Engineering, CRPF Gate No. 3 Hingna Road, Nagpur, 440016, Maharashtra, India. vibha.bora@raisoni.net.
authors.forename: Vibha Bafna
authors.initials: VB
authors.lastname: Bora
authors.affiliation: Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440010, Maharashtra, India. ashwinkothari@ece.vnit.ac.in.
authors.forename: Ashwin G
authors.initials: AG
authors.lastname: Kothari
authors.affiliation: Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440010, Maharashtra, India. agkeskar@ece.vnit.ac.in.
authors.forename: Avinash G
authors.initials: AG
authors.lastname: Keskar
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010369:Pectoralis Muscles / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results
keywords: Computer aided diagnosis; Euclidean distance regression; Hough transform; Mammograms; Pectoral muscle detection; Texture gradient