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Texture-based image retrieval for computerized tomography databases

In this paper we propose a content-based image retrieval (CBIR) system for retrieval of normal anatomical regions present in computed tomography (CT) studies of the chest and abdomen. We implement and compare eight similarity measures using local and global cooccurrence texture descriptors. The preliminary results are obtained using a CT database consisting of 344 CT images representing the segmented heart and great vessels, liver, renal and splenic parenchyma, and backbone from two different patients. We evaluate the results with respect to the retrieval precision metric for each of the similarity measures when calculated per organ and overall.