Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection
Region-based stellate features for spiculated mass classification are proposed. A novel approach for selecting an optimal set of feature variables is proposed. Proposed features outperform other mammographic spiculated mass features. Investigating contributions of subregions of ROIs to extract discriminant features.
adaboost computer-aided detection region-based stellate features spiculated masses mammography variable selection metric