Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors.

Automatic segmentation of mammographic mass is an important yet challenging task. Despite the great success of shape prior in biomedical image analysis, existing shape modeling methods are not suitable for mass segmentation. The reason is that masses have no specific biological structure and exhibit complex variation in shape, margin, and size. In addition, it is difficult to preserve the local details of mass boundaries, as masses may have spiculated and obscure boundaries. To solve these problems, we propose to learn online shape and appearance priors via image retrieval. In particular, given a query image, its visually similar training masses are first retrieved via Hough voting of local features. Then, query specific shape and appearance priors are calculated from these training masses on the fly. Finally, the query mass is segmented using these priors and graph cuts. The proposed approach is extensively validated on a large dataset constructed on DDSM. Results demonstrate that our online learned priors lead to substantial improvement in mass segmentation accuracy, compared with previous systems.