# MinMax Radon Barcodes for Medical Image Retrieval

arXiv: https://arxiv.org/abs/1610.00318 · HTML: https://mlsanity.com/paper/1610.00318/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2016.

## Built on by (most cited first, 9 of 9)

- [Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search](https://mlsanity.com/paper/2208.13653/) (2022)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'](https://mlsanity.com/paper/2304.08297/) (2023)
- [Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images](https://mlsanity.com/paper/2311.09902/) (2023)
- [Multimodal Learning for Scalable Representation of High-Dimensional Medical Data](https://mlsanity.com/paper/2409.13115/) (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)
- [Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data](https://mlsanity.com/paper/2605.00902/) (2026)
- [Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval](https://mlsanity.com/paper/2606.26157/) (2026)

## Abstract (arXiv)

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 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.