A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms.

ABSTRACT: Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59

SUBMITTER: Karale VA 

PROVIDER: S-EPMC6737166 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature