A New Local Radon Descriptor for Content-Based Image Search

Morteza Babaie, Hany Kashani, Meghana D. Kumar, H. R. Tizhoosh

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

Abstract

Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of local Radon projections. We also propose a very fast convolution-based local Radon estimator to overcome the slow process of Radon projections. We performed our experiments using pathology images (KimiaPath24) and lung CT patches and test our proposed solution for medical image processing. We achieved superior results compared with other histogram-based descriptors such as LBP and HoG as well as some pre-trained CNNs.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
EditorsMartin Michalowski, Robert Moskovitch
PublisherSpringer Science and Business Media Deutschland GmbH
Pages463-472
Number of pages10
ISBN (Print)9783030591366
DOIs
StatePublished - 2020
Event18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Minneapolis, United States
Duration: Aug 25 2020Aug 28 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12299 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Medicine, AIME 2020
Country/TerritoryUnited States
CityMinneapolis
Period8/25/208/28/20

Keywords

  • Image retrieval
  • Local radon
  • Medical imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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