Content Based medical image retrieval based on BEMD: optimization of a similarity metric

Most medical images are now digitized and stored in patients files databases. The challenge is how to use them for acquiring knowledge or/and for aid to diagnosis. In this paper, we address the challenge of diagnosis aid by Content Based Image Retrieval (CBIR). We propose to characterize images by using the Bidimensional Empirical Mode Decomposition (BEMD). Images are decomposed into a set of functions named Bidimensional Intrinsic Mode Functions (BIMF). Two methods are used to characterize BIMFs information content: the Generalized Gaussian density functions (GGD) and the Huang-Hilbert transform (HHT). In order to enhance results, we introduce a similarity metric optimization process: weighted distances between BIMFs are adapted for each image in the database. Retrieval efficiency is given for different databases (DB), including a diabetic retinopathy DB, a mammography DB and a faces DB. Results are promising: the retrieval efficiency is higher than 95% for some cases.