Information-theoretic CAD system in mammography: Improved mass detection by incorporating a Gaussian saliency map

We are presenting continuing development of an information-theoretic (IT) CADe system for location-specific interrogation of screening mammograms to detect masses. IT-CADe relies on a knowledge library of mammographic cases with known ground truth and an evidence-based approach to make a decision regarding a query case. If the query is more similar to abnormal cases stored in the library, then the query is deemed also abnormal. Case similarity is measured using mutual information (MI). MI takes into account only the probabilities of the underlying image pixels but not their relative significance in the image. To address this limitation, we investigated a novel modification of the MI similarity measure by incorporating the saliency of image pixels. Specifically, a Gaussian saliency map was applied where central image pixels were given a higher weight and pixels' importance degraded progressively towards the image periphery. This map makes intuitively sense. If a mass is suspected at a particular location, then image pixels surrounding this location should be given higher importance in the MI calculation than pixels further away from this specific location. The new MI measure was tested with a leave-one-out scheme on a database of 1,820 mammographic regions (901 with masses and 919 normal). Further validation was performed on additional datasets of mammographic regions deemed as suspicious by a computer algorithm and by expert mammographers. Incorporation of the Gaussian saliency map resulted in consistent and often significant improvement of IT-CADe performance across all but one datasets.