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Modeling the Dermoscopic Structure Pigment Network Using a Clinically Inspired Feature Set

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Book cover Medical Imaging and Augmented Reality (MIAR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6326))

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Abstract

We present a method to detect and classify the dermoscopic structure pigment network which may indicate early melanoma in skin lesions. We locate the network as darker areas constituting a mesh, as well as lighter areas representing the ‘holes’ which the mesh surrounds. After identifying the lines and holes, 69 features inspired by the clinical definition are derived and used to classify the network into one of two classes: Typical or Atypical. We validate our method over a large, inclusive, ‘real-world’ dataset consisting of 436 images and achieve an accuracy of 82% discriminating between three classes (Absent, Typical or Atypical) and an accuracy of 93% discriminating between two classes (Absent or Present).

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Author information

Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, Canada

    Maryam Sadeghi, Majid Razmara, Paul Wighton, Tim K. Lee & M. Stella Atkins

  2. Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Canada

    Maryam Sadeghi, Paul Wighton & Tim K. Lee

  3. Cancer Control Research Program and Cancer Imaging Department, BC Cancer Research Centre, Canada

    Maryam Sadeghi, Paul Wighton & Tim K. Lee

Authors
  1. Maryam Sadeghi
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  2. Majid Razmara
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  3. Paul Wighton
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  4. Tim K. Lee
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  5. M. Stella Atkins
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Editor information

Editors and Affiliations

  1. Department of Bioengineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo, 113-8656, Tokyo, Japan

    Hongen Liao

  2. Department of Surgery and Cancer, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK

    P. J. "Eddie" Edwards

  3. Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, 60637, Chicago, IL, USA

    Xiaochuan Pan

  4. Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, 100080, Beijing, China

    Yong Fan

  5. Institute of Biomedical Engineering, Imperial College London, 180 Queens Gate, SW7 2BZ, London, UK

    Guang-Zhong Yang

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Sadeghi, M., Razmara, M., Wighton, P., Lee, T.K., Atkins, M.S. (2010). Modeling the Dermoscopic Structure Pigment Network Using a Clinically Inspired Feature Set. In: Liao, H., Edwards, P.J."., Pan, X., Fan, Y., Yang, GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15699-1_49

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  • DOI: https://doi.org/10.1007/978-3-642-15699-1_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15698-4

  • Online ISBN: 978-3-642-15699-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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