Curvelet-based locality sensitive hashing for mammogram retrieval in large-scale datasets

Content-based image retrieval (CBIR) is a primordial task to provide the most similar images especially in the context of medical imaging for diagnosis aid. In this paper, we propose a CBIR method for a large-scale mammogram datasets. In fact, to extract region of interest (ROI) signatures, four moment descriptors were defined after computing the curvelet coefficients for each level of the ROI. Then, an unsupervised technique based on locality sensitive hashing was adopted for indexing the extracted signatures. The main contribution of the suggested method resides in the variance-based filtering within the retrieval phase in order to extract the suitable buckets in the shortest time, while optimizing the memory requirement. After that, an accurate searching in Hamming space is performed in order to identify the similar ROIs to the query case. Realized experiments on the challenging Digital Database for Screening Mammography (DDSM) dataset proved the performance of the proposed method for the retrieval of the most relevant mammograms in a large-scale dataset. It achieves a mean retrieval precision rate of 97.1% over a total of 11218 mammogram ROIs.