The benefits emanating from the application of content- based approaches to medical image retrieval range from clinical decision support to medical education and research

In this paper we propose a novel scheme for efficient content-based medical image retrieval, formalized according to the PANDA (PAtterns for Next generation Database systems) framework. The proposed scheme involves low-level feature extraction from image regions followed by clustering of the feature space to form higher-level patterns. The components of each pattern include a cluster representation and a measure that quantifies the quality of the image content representation achieved by the pattern. The similarity between two patterns is estimated as a function of the similarity between both the structure and the measure components of the patterns. Experiments were performed on a reference set of radiographic images, using standard wavelet domain image features. The results show that the proposed scheme can be more efficient than the common ground schemes for medical image retrieval, as it does not involve exhaustive, nearest neighbor searching over the whole set of the available feature vectors. Keeping the patterns in a unified form facilitates further processing and analysis by mining or visualization algorithms.