title: Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms.
abstract: The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.
authors.affiliation: Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, Republic of Korea.
authors.forename: Woong Bae
authors.identifier: 0000-0002-3901-5192
authors.initials: WB
authors.lastname: Yoon
authors.affiliation: Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, Republic of Korea.
authors.forename: Ji Eun
authors.identifier: 0000-0002-1953-9845
authors.initials: JE
authors.lastname: Oh
authors.affiliation: Department of Radiology, College of Medicine, Ulsan University, Seoul, Republic of Korea.
authors.forename: Eun Young
authors.initials: EY
authors.lastname: Chae
authors.affiliation: Department of Radiology, College of Medicine, Ulsan University, Seoul, Republic of Korea.
authors.forename: Hak Hee
authors.initials: HH
authors.lastname: Kim
authors.affiliation: Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.
authors.forename: Soo Yeul
authors.identifier: 0000-0002-7902-0690
authors.initials: SY
authors.lastname: Lee
authors.affiliation: Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, Republic of Korea.
authors.forename: Kwang Gi
authors.identifier: 0000-0001-9714-6038
authors.initials: KG
authors.lastname: Kim
mesh_terms: D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000209:etiology; D002114:Calcinosis / Q000150:complications / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D010369:Pectoralis Muscles / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity