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A correlation-enhanced similarity matching framework for medical image retrieval is presented in a local concept-based feature space. In this framework, images are presented by vectors of concepts that comprise of local color and texture patches of image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical models are built using a probabilistic multi-class support vector machine (SVM). For the similarity search, the concept correlations in the collection as a whole are analyzed as a global thesaurus-like structure and incorporated in a similarity matching function. The proposed scheme overcomes some limitations of the “bag of concepts” model, such as the assumption of feature independence. A systematic evaluation of image retrieval on a biomedical image collection of different modalities demonstrates the advantages of the proposed retrieval framework in terms of precision-recall.