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International Workshop on Breast Imaging

IWDM 2016: Breast Imaging pp 351–358Cite as

Non-expert Classification of Microcalcification Clusters Using Mereotopological Barcodes

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9699))

Abstract

This paper investigates the use of mereotopological barcodes to help non-experts classify microcalcification clusters as either benign or malignant. When compared against classification using the microcalcification cluster segmentation maps, the use of barcodes is able to see a significant improvement in classification performance with the AUC significantly increasing (\(p < 0.01\)) from 0.62 for images to 0.82 for barcodes on the MIAS dataset. This shows that barcodes could prove useful to aid clinicians with interpreting and classifying mammographic microcalcifications.

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Acknowledgments

The authors would like to thank the organisers of the Science Café for the opportunity to conduct this experiment. They would also like to extend their gratitude to all the participants for their willingness to be involved.

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Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WE12 1HG, UK

    Harry Strange

  2. Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK

    Reyer Zwiggelaar

Authors
  1. Harry Strange
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  2. Reyer Zwiggelaar
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Corresponding author

Correspondence to Harry Strange .

Editor information

Editors and Affiliations

  1. Lund Univ. and Skåne University Hospital, Sweden

    Anders Tingberg

  2. Lund Univ. and Unilabs, Malmö, Sweden

    Kristina Lång

  3. Lund Univ. and Skåne University Hospital, Malmö, Sweden

    Pontus Timberg

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© 2016 Springer International Publishing Switzerland

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Strange, H., Zwiggelaar, R. (2016). Non-expert Classification of Microcalcification Clusters Using Mereotopological Barcodes. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_44

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  • DOI: https://doi.org/10.1007/978-3-319-41546-8_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41545-1

  • Online ISBN: 978-3-319-41546-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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