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MinMax Radon Barcodes for Medical Image Retrieval

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

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.

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Notes

  1. 1.

    Matlab code taken from http://tizhoosh.uwaterloo.ca/.

  2. 2.

    http://irma-project.org/.

  3. 3.

    http://www.rad.rwth-aachen.de/.

  4. 4.

    http://www.imageclef.org/.

  5. 5.

    Matlab code: http://goo.gl/vFYvVJ.

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Author information

Authors and Affiliations

  1. KIMIA Lab, University of Waterloo, Waterloo, Canada

    H. R. Tizhoosh

  2. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Shujin Zhu

  3. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

    Hanson Lo

  4. Department of Mathematics, University of Waterloo, Waterloo, Canada

    Varun Chaudhari

  5. Centre for Computational Mathematics in Industry and Commerce, University of Waterloo, Waterloo, Canada

    Tahmid Mehdi

Authors
  1. H. R. Tizhoosh
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  2. Shujin Zhu
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  3. Hanson Lo
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  4. Varun Chaudhari
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  5. Tahmid Mehdi
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Corresponding author

Correspondence to H. R. Tizhoosh .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Tizhoosh, H.R., Zhu, S., Lo, H., Chaudhari, V., Mehdi, T. (2016). MinMax Radon Barcodes for Medical Image Retrieval. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_55

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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