Global versus Hybrid Thresholding for Border Detection in Dermoscopy Images

  • Rahil Garnavi
  • Mohammad Aldeen
  • Sue Finch
  • George Varigos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)


In this paper we demonstrate the superiority of the automated hybrid thresholding approach to border detection in dermoscopy images over the global thresholding method through a newly introduced evaluation metric: Performance Index. The approach incorporates optimal color channels into the hybrid thresholding method, which is a combination of global and adaptive local thresholding, to determine the closest border to that drawn by dermatologists. Statistical analysis and optimization procedure are used and shown to be convergent in determining the optimal parameters for the local thresholding procedure in order to obtain the most accurate borders. The effectiveness of the approach is tested on 55 high resolution dermoscopy images of patients, with manual borders drawn by three expert dermatologists, and the union is used as the ground truth. The results demonstrate the significant advantages of the automated hybrid approach over the global thresholding method.


Border detection Histogram thresholding Dermoscopy Melanoma Computer-aided diagnosis 


  1. 1.
    Australia skin cancer facts and figures, (accessed September 2009)
  2. 2.
    Perrinaud, A., Gaide, O., French, L.E., Saurat, J.H., Marghoob, A.A., Braun, R.P.: Can automated dermoscopy image analysis instruments provide added benefit for the dermatologist? British Journal of Dermatology 157, 926–933 (2007)CrossRefGoogle Scholar
  3. 3.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.: Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics 33, 148–153 (2009)CrossRefGoogle Scholar
  4. 4.
    Iyatomi, H., Oka, H., Celebi, M.E., Hashimoto, M., Hagiwara, M., Tanaka, M., Ogawa, K.: An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Computerized Medical Imaging and Graphics 32, 566–579 (2008)CrossRefGoogle Scholar
  5. 5.
    Melli, R., Grana, C., Cucchiara, R.: Comparison of color clustering algorithms for segmentation of dermatological images. In: SPIE Medical Imaging, vol. 6144, pp. 3S1–3S9 (2006)Google Scholar
  6. 6.
    Celebi, M.E., Kingravi, H., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H., et al.: Border detection in dermoscopy images using statistical region merging. Skin Research and Technology 14, 347–353 (2008)CrossRefGoogle Scholar
  7. 7.
    Zhou, H., Chen, M., Zou, L., Gass, R., Ferris, L., Drogowski, L., Rehg, J.M.: Spatially constrained segmentation of dermoscopy images. In: 5th IEEE International Symposium on Biomedical Imaging, pp. 800–803 (2008)Google Scholar
  8. 8.
    Garnavi, R., Aldeen, M., Celebi, M.E., Finch, S., Varigos, G.: Border detection in dermoscopy images using hybrid thresholding on optimized color channels. To appear in Computerized Medical Imaging and Graphics, Special Issue: Skin Cancer ImagingGoogle Scholar
  9. 9.
    Garnavi, R., Aldeen, M., Celebi, M.E., Bhuiyan, A., Dolianitis, C., Varigos, G.: Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels. International Journal of Medicine and Medical Sciences 1, 126–134 (2010)Google Scholar
  10. 10.
    Lee, T., Ng, V., Gallagher, R., et al.: Dullrazor: A software approach to hair removal from images. Computers in Biology and Medicine 27, 533–543 (1997)CrossRefGoogle Scholar
  11. 11.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rahil Garnavi
    • 1
  • Mohammad Aldeen
    • 1
  • Sue Finch
    • 2
  • George Varigos
    • 3
  1. 1.NICTA Victoria Research Laboratory, Department of Electrical and Electronic EngineeringThe University of MelbourneMelbourneAustralia
  2. 2.Department of Mathematics and StatisticsThe University of MelbourneMelbourneAustralia
  3. 3.Department of DermatologyRoyal Melbourne HospitalMelbourneAustralia

Personalised recommendations