Medical content based image retrieval by using the Hadoop framework

Most medical images are now digitized and stored in large image databases. Retrieving the desired images becomes a challenge. In this paper, we address the challenge of content based image retrieval system by applying the MapReduce distributed computing model and the HDFS storage model. Two methods are used to characterize the content of images: the first is called the BEMD-GGD method (Bidimensional Empirical Mode Decomposition with Generalized Gaussian density functions) and the second is called the BEMD-HHT method (Bidi-mensional Empirical Mode Decomposition with Huang-Hilbert Transform HHT). To measure similarity between images we compute the distance between signatures of images, for that we use the Kullback-Leibler Divergence (KLD) to compare the BEMD-GGD signatures and the Euclidean distance to compare the HHT signatures. Through the experiments on the DDSM mammography image database, we confirm that the results are promising and this work has allowed us to verify the feasibility and efficiency of applying the CBIR in the large medical image databases.