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For more than two decades, research has been performed on content-based imageretrieval (CBIR). By combining Radon projections and the support vectormachines (SVM), a content-based medical image retrieval method is presented inthis work. The proposed approach employs the normalized Radon projections withcorresponding image category labels to build an SVM classifier, and the Radonbarcode database which encodes every image in a binary format is also generatedsimultaneously to tag all images. To retrieve similar images when a query imageis given, Radon projections and the barcode of the query image are generated.Subsequently, the k-nearest neighbor search method is applied to find theimages with minimum Hamming distance of the Radon barcode within the same classpredicted by the trained SVM classifier that uses Radon features. Theperformance of the proposed method is validated by using the IRMA 2009 datasetwith 14,410 x-ray images in 57 categories. The results demonstrate that ourmethod has the capacity to retrieve similar responses for the correctlyidentified query image and even for those mistakenly classified by SVM. Theapproach further is very fast and has low memory requirement.

Radon Features and Barcodes for Medical Image Retrieval via SVM