A Cross Saliency Approach to Asymmetry-Based Tumor Detection

Identifying regions that break the symmetry within an organ or between paired organs is widely used to detect tumors on various modalities. Algorithms for detecting these regions often align the images and compare the corresponding regions using some set of features. This makes them prone to misalignment errors and inaccuracies in the estimation of the symmetry axis. Moreover, they are susceptible to errors induced by both inter and intra image correlations. We use recent advances in image saliency and extend them to handle pairs of images, by introducing an algorithm for image cross-saliency. Our cross-saliency approach is able to estimate regions that differ both in organ and paired organs symmetry without using image alignment, and can handle large errors in symmetry axis estimation. Since our approach is based on internal patch distribution it returns only statistically informative regions and can be applied as is to various modalities. We demonstrate the application of our approach on brain MRI and breast mammogram.