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Depth Data Improves Skin Lesion Segmentation

  • Xiang Li
  • Ben Aldridge
  • Lucia Ballerini
  • Robert Fisher
  • Jonathan Rees
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.

Keywords

Structure Tensor Initial Contour Pigment Lesion Lesion Region Dermoscopy Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xiang Li
    • 1
  • Ben Aldridge
    • 2
  • Lucia Ballerini
    • 1
  • Robert Fisher
    • 1
  • Jonathan Rees
    • 2
  1. 1.School of InformaticsUniversity of EdinburghUK
  2. 2.DermatologyUniversity of EdinburghUK

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