Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement

  • Behzad BozorgtabarEmail author
  • Mani Abedini
  • Rahil Garnavi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


This paper proposes an unsupervised skin lesion segmentation method for dermoscopy images by exploiting the contextual information of skin image at the superpixel level. In particular, a Laplacian sparse coding is presented to evaluate the probabilities of the skin image pixels to delineate lesion border. Moreover, a new rule-based smoothing strategy is proposed as the lesion segmentation refinement procedure. Finally, a multi-scale superpixel segmentation of the skin image is provided to handle size variation of the lesion in order to improve the accuracy of the detected border. Experiments conducted on two datasets show the superiority of our proposed method over several state-of-the-art skin segmentation methods.


Superpixel-based segmentation Laplacian sparse coding Dynamic rule-based refinement 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Behzad Bozorgtabar
    • 1
    Email author
  • Mani Abedini
    • 1
  • Rahil Garnavi
    • 1
  1. 1.IBM Research - AustraliaCarltonAustralia

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