Bag of visual words based approach for the classification of benign and malignant masses in mammograms using voting-based feature encoding

Classification of benign and malignant masses in mammograms is a challenging problem. It has wide applications in the development of Computer Aided Diagnosis (CAD) systems, however many challenges still need to be addressed. Due to the risk associated with segmenting the mass region, focus is shifting from selecting the features just from the mass area, to the whole Region of Interest (RoI) containing that mass. Bag of Visual Words (BoVW) techniques are gaining attention for classification tasks in medical imaging by considering RoI as a set of local features. In general BoVW aims to construct a global descriptor based on the extracted local features. In this work, we investigate the performance of BoVW for the classification of benign and malignant mammographic masses. Several features have been explored as the local features and different methods are applied for building the code-book. Subsequently we propose a voting-based approach to encode the features. The proposed approach is evaluated on a subset of DDSM dataset. Initial results reveal classification accuracy as high as 87% and Area Under the Curve (AUC) as 0.93, which are better than the current state-of-the-art approaches applied to the same problem.