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Computer Vision Techniques for the Diagnosis of Skin Cancer pp 1–22Cite as

Pigment Network Detection and Analysis

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Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

We describe the importance of identifying pigment networks in lesions which may be melanomas, and survey methods for identifying pigment networks (PN) in dermoscopic images. We then give details of how machine learning can be used to classify images into three classes: PN Absent, Regular PN and Irregular PN.

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Author information

Authors and Affiliations

  1. Department of Dermatology and Skin Science, University of British Columbia, Vancouver, Canada

    Maryam Sadeghi, Tim K. Lee, David McLean, Harvey Lui & M. Stella Atkins

  2. School of Computing Science, Simon Fraser University, Vancouver, Canada

    Maryam Sadeghi, Tim K. Lee & M. Stella Atkins

  3. Cancer Control Research Program, BC Cancer Research Center, Vancouver, Canada

    Maryam Sadeghi & Tim K. Lee

  4. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA

    Paul Wighton

Authors
  1. Maryam Sadeghi
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  2. Paul Wighton
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  3. Tim K. Lee
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  4. David McLean
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  5. Harvey Lui
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  6. M. Stella Atkins
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Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. Computer Science dept., Louisiana State University Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

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Sadeghi, M., Wighton, P., Lee, T.K., McLean, D., Lui, H., Atkins, M.S. (2014). Pigment Network Detection and Analysis. In: Scharcanski, J., Celebi, M. (eds) Computer Vision Techniques for the Diagnosis of Skin Cancer. Series in BioEngineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39608-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-39608-3_1

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  • Print ISBN: 978-3-642-39607-6

  • Online ISBN: 978-3-642-39608-3

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