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Skin Cancer Segmentation Using a Unified Markov Random Field

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Advances in Visual Computing (ISVC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

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Abstract

Most of the medical institutions still use manual methods to detect the skin cancers tumors. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in the automatic segmentation of skin cancer due to many factors, such as different skin colors and the presence of hair, diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries. To address these factors, a Unified Markov Random Field (UMRF) is used to segment both pixel information and regional information corresponding to skin lesions from the images, where UMRF model lies in two aspects. First, it combines the benefits of the pixel-based and the region-based Markov Random Field (MRF) models by decomposing the likelihood function into the product of the pixel likelihood function and the regional likelihood function. The experimental results show that the employed method has high precision \(83.08\%\) (Jaccard Index).

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Author information

Authors and Affiliations

  1. School of Maths, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Omran Salih & Serestina Viriri

Authors
  1. Omran Salih
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  2. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Salih, O., Viriri, S. (2018). Skin Cancer Segmentation Using a Unified Markov Random Field. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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