MinMax Radon Barcodes for Medical Image Retrieval

TL;DR

This paper introduces MinMax Radon barcodes, a binary image descriptor that improves medical image retrieval accuracy and speed over previous Radon barcode methods and compares favorably with SURF and BRISK techniques.

Contribution

The paper proposes MinMax Radon barcodes, a novel binary descriptor that outperforms existing Radon barcodes in accuracy and efficiency for medical image retrieval.

Findings

MinMax Radon barcodes reduce retrieval error by over 15%.

They are faster and more accurate than thresholded Radon barcodes.

Compared to SURF and BRISK, they offer superior performance on IRMA dataset.

Abstract

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.