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.

In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masses from normal tissues on mammograms. In the proposed method, a given region of interest (ROI) for feature extraction is divided into three individual subregions, namely core, inner, and outer parts. The proposed region-based stellate features are then extracted to encode the different and complementary stellate pattern information by computing the statistical characteristics for each of the three different subregions. To further maximize classification performance, a novel variable selection algorithm based on AdaBoost learning is incorporated for choosing an optimal subset of variables of region-based stellate features. In particular, we develop a new variable selection metric (criteria) that effectively determines variable importance (ranking) within the conventional AdaBoost framework. Extensive and comparative experiments have been performed on the popular benchmark mammogram database (DB). Results show that our region-based stellate features (extracted from automatically segmented ROIs) considerably outperform other state-of-the-art features developed for mammographic spiculated mass detection or classification. Our results also indicate that combining region-based stellate features with the proposed variable selection strategy has an impressive effect on improving spiculated mass classification and detection.