MinMax radon barcodes for medical image retrieval

H. R. Tizhoosh, Shujin Zhu, Hanson Lo, Varun Chaudhari, Tahmid Mehdi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Sandra Skaff, Daisuke Iwai, Richard Boyle, Darko Koracin, Fatih Porikli, Carlos Scheidegger, Alireza Entezari, Jianyuan Min, Amela Sadagic, Tobias Isenberg
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319508344
StatePublished - 2016
Event12th International Symposium on Visual Computing, ISVC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Symposium on Visual Computing, ISVC 2016
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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