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)


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.


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.


  1. 1.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comp. Medical Imaging and Graphics 33, 148–153 (2009)CrossRefGoogle Scholar
  2. 2.
    Xu, L., Jackowski, M., Goshtasby, A., Roseman, D., Bines, S., Yu, C., Dhawan, A., Huntley, A.: Segmentation of skin cancer images. Image and Vision Computing 17(1), 65–74 (1999)CrossRefGoogle Scholar
  3. 3.
    Iyatomi, H., Oka, H., Saito, M., Miyaka, A., Kimoto, M., Yamagami, J.: Quantitative assessment of tumour area extraction from dermoscopy images and evaluation of the computer-based methods for automatic melanoma diagnostic system. Melanoma Research 16(2), 183–190 (2006)CrossRefGoogle Scholar
  4. 4.
    Chung, D.H., Sapiro, G.: Segmenting skin lesions with partial-differential-equations-bassed image processing algorithms. IEEE Transactions on Medical Imaging 19(7), 763–767 (2000)CrossRefGoogle Scholar
  5. 5.
    Erkol, B., Moss, R., Stanley, R., Stoecker, W., Hvatum, E.: Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Research and Technology 11(17-26) (2005)Google Scholar
  6. 6.
    Tang, J.: A multi-direction GVF snake for the segmentation of skin cancer images. Pattern Recognition 42, 1172–1179 (2009)CrossRefGoogle Scholar
  7. 7.
    Yuan, X., Situ, N., Zouridakis, G.: A narrow band graph partitioning method for skin lesion segmentation. Pattern Recognition 42, 1017–1028 (2009)zbMATHCrossRefGoogle Scholar
  8. 8.
    McDonagh, S.: Skin cancer surface shape based classification. Thesis, School of Informatics, University of Edinburgh (2008)Google Scholar
  9. 9.
    Cremers, D., Rousson, M., Deriche, R.: Reviews of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2), 195–215 (2007)CrossRefGoogle Scholar
  10. 10.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions in Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  11. 11.
    Bertelli, L., Sumengen, B., Manjunath, B.S., Gibou, F.: A variational framework for multiregion pairwise-similarity-based image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(8), 1400–1415 (2008)CrossRefGoogle Scholar
  12. 12.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(9), 891–906 (1991)CrossRefGoogle Scholar
  13. 13.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  14. 14.
    Weeratunga, S.K., Kamath, C.: Pde-based non-linear diffusion techniques for denoising scientific and industrial images: an empirical study. In: Image Processing: Algorithms and Systems Conference, SPIE, vol. 4667, pp. 279–290 (2002)Google Scholar

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

Personalised recommendations