Breast masses classification using contour shape descriptors based on Beam Angle Statistics

Mammography is an important diagnostic tool for early detection of breast cancer. Masses in mammogram are characterized by their shape and margin of their contours. A typical contour of benign mass is round and oval, whereas a malignant mass has irregular and spiculated. Many studies in literature have shown that features extracted from 1D signature of 2D mass contour can distinguish benign and malignant lesion. The features extracted from 1D signature results in low dimensional feature vector and in turn doesn't require feature selection step for classification. The proposed research study applies a Beam angle statistics (BAS) to convert 2D contour into 1D signature. Further, the features are extracted from the 1D BAS signatures in order to classify masses. K-nearest neighbor (KNN), support vector machines (SVM), Adaboost decision tree and artificial neural networks (ANN) are employed to categorize breast masses into benign and malignant using features of BAS signature. The proposed methodology was tested on 147 contours, obtaining accuracy of 88.8% accuracy. The proposed system was also compared with recent literature described related breast cancer analysis.