# Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation

arXiv: https://arxiv.org/abs/1505.05212 · HTML: https://mlsanity.com/paper/1505.05212/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2015.

## Built on by (most cited first, 5 of 5)

- [On Image Search in Histopathology](https://mlsanity.com/paper/2401.08699/) (2024)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [SPLICE -- Streamlining Digital Pathology Image Processing](https://mlsanity.com/paper/2404.17704/) (2024)
- [Histopathology Slide Indexing and Search: Are We There Yet?](https://mlsanity.com/paper/2306.17019/) (2023)
- [Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings](https://mlsanity.com/paper/2507.17025/) (2025)

## Abstract (arXiv)

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.