Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation

TL;DR

This paper explores the use of barcode annotations, specifically Radon barcodes and local binary patterns, to enhance medical image retrieval accuracy on the IRMA dataset.

Contribution

It introduces barcode annotations as a novel form of visual annotation to improve medical image retrieval performance.

Findings

Barcode annotations can potentially improve retrieval accuracy.

Radon barcodes and LBP-based barcodes are effective for medical images.

Preliminary results show promise for barcode-based annotations.

Abstract

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.