Multi-view Score Fusion for Content-Based Mammogram Retrieval

Screening mammography provides two views for each breast: Medio-Lateral Oblique (MLO) and Cranial-Caudal (CC) views. However, current content based image retrieval (CBIR) systems analyze each view independently, in spite of their complementarities. To further improve the retrieval performance, this paper introduces a two-view CBIR system that combines retrieval results of MLO and CC views. First, we computed the similarity scores between MLO (resp. CC) ROIs in the database and the MLO (resp. CC) query ROI. These ROIs are characterized using curvelet moments. Then, a new linear weighted sum scheme combines MLO and CC scores; it assigns weights for each view according to the distribution of the classes of its neighbors. The ROIs having the highest fused scores are displayed to the radiologist and used to compute the malignancy likelihood of the lesion. Experiments performed on mammograms from the Digital Database for Screening Mammography (DDSM) show the effectiveness of the proposed method.