Mammographic Mass Retrieval Using Multi-view Information and Laplacian Score Feature Selection

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among women all over the world. Content based mammographic mass retrieval can assist radiologists to retrieve biopsy-proven masses content similar with the diagnostic ones, which can help radiologists to improve the diagnostic performance. However, existing mammographic mass retrieval methods are based on single-view information although one mass has two different views in mammograms. In this paper, we propose a new multi-view based mammographic mass retrieval approach integrated with feature selection method. In our retrieval paradigm, the query example is a multi-view mass pair different from a single view mass in previous studies. Accordingly, in order to extract significant characteristics from the mass, a total of 99 handcrafted features are computed, and an optimal feature set is determined by Laplacian Score (LS) feature selection method. Initial experimental results show that the retrieval performance based on our approach is better than that based on single-view method.