Computer-aided diagnosis of mammographic masses using vocabulary tree-based image retrieval

Computer-aided diagnosis of masses in mammograms is important to the prevention of breast cancer. Many approaches tackle this problem through content-based image retrieval (CBIR) techniques. However, most of them fall short of scalability in the retrieval stage, and their diagnostic accuracy is therefore restricted. To overcome this drawback, we propose a scalable method for retrieval and diagnosis of mam-mographic masses. Specifically, for a query mammographic region of interest (ROI), SIFT descriptors are extracted and searched in a vocabulary tree, which stores all the quantized descriptors of previously diagnosed mammographic ROIs. In addition, to fully exert the discriminative power of SIFT descriptors, contextual information in the vocabulary tree is employed to refine the weights of tree nodes. The retrieved ROIs are then used to determine whether the query ROI contains a mass. This method has excellent scalability due to the low spatial-temporal cost of vocabulary tree. Retrieval precision and diagnostic accuracy are evaluated on 5005 ROIs extracted from the digital database for screening mammography (DDSM), which demonstrate the efficacy of our approach.