title: A new approach to the detection of lesions in mammography using fuzzy clustering.
abstract: Breast cancer is a leading cause of female mortality and its early detection is an important means of reducing this. The present study investigated an approach, based on fuzzy clustering, to detect small lesions, such as microcalcifications and other masses, that are hard to recognize  in breast cancer screening. A total of 180 mammograms were analysed and classified by radiologists into three groups (n = 60 per group): those with microcalcifications; those with tumours; and those with no lesions. Twenty mammograms were taken as training data sets from each of the  groups. The algorithm was then applied to the data not taken for training. Analysis by fuzzy clustering achieved a mean accuracy of 99.7% compared with the radiologists' findings. It was concluded that the fuzzy clustering algorithm allowed for more efficient and accurate detection of breast  lesions and may improve the early detection of breast tumours.
authors.affiliation: Room 631, Mathematics Building, Jilin University, 2699 Qianjin Street, Changchun 130012, China. wyangjlu@gmail.com
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: H
authors.initials: H
authors.lastname: Shi
authors.forename: S
authors.initials: S
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000150:complications / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000150:complications / Q000000981:diagnostic imaging; D016000:Cluster Analysis; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008327:Mammography / Q000379:methods*