Screening mammography has been successful in early detection of breast cancer, which has been one of the leading causes of death for women worldwide. Among commonly detected symptoms on mammograms, mass detection is a challenging problem as the task is affected by high complexity of breast tissues, the presence of pectoral muscles as well as varying shape and size of masses. In this research, a novel framework is proposed which automatically detects mass(es) from mammogram(s) even in the presence of pectoral muscles. The framework uses saliency based segmentation which does not require removal of pectoral muscles, if present. From segmented regions, different features are extracted followed by Support Vector Machine classification for mass detection. The experiments are performed using an existing experimental protocol on the MIAS database and the results show that the proposed framework with saliency based region segmentation outperforms the state-of-art algorithms. © 2013 Elsevier B.V.