Effective features based on normal linear structures for detecting microcalcifications in mammograms

Many features have been proposed for the detection of microcalcification clusters (MCCs) or classification of benign/malignant MCCs. However, most of them were designed based on the characteristics of MCC. In this paper, 16 features, which have been commonly adopted in many applications, are examined and six new features based on the linear structure are proposed. To evaluate the effectiveness of these six features, 800 suspicious regions detected from 320 full-field mammograms are equally divided into two parts for training and testing respectively. Experiments demonstrate that the area under the receiver operating characteristic (ROC) is increased from 0.86 to 0.89 after the new features are added into the set of feature selection. In the best feature sequence selected by the sequential floating forward search (SFFS) algorithm, the new proposed features take up the half number of features in the sequence.