Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
This addresses medical image retrieval for healthcare professionals, but it is incremental as it builds on existing feature-based paradigms with a novel annotation approach.
The paper tackles the problem of improving medical image retrieval accuracy by proposing barcode annotations, such as Radon barcodes, for images and regions of interest, and tests this on the IRMA x-ray dataset with 12,677 training and 1,733 test images, showing potential to enhance retrieval when combined with existing feature-based methods.
This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types. A multitude of efficient feature-based image retrieval methods already exist that can assign a query image to a certain image class. Visual annotations may help to increase the retrieval accuracy if combined with existing feature-based classification paradigms. Whereas with annotations we usually mean textual descriptions, in this paper barcode annotations are proposed. In particular, Radon barcodes (RBC) are introduced. As well, local binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test images is used to verify how barcodes could facilitate image retrieval.