Skin Hair Removal in Dermoscopic Images Using Soft Color Morphology

  • Pedro Bibiloni
  • Manuel González-Hidalgo
  • Sebastia Massanet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Dermoscopic images are useful tools towards the diagnosis and classification of skin lesions. One of the first steps to automatically study them is the reduction of noise, which includes bubbles caused by the immersion fluid and skin hair. In this work we provide an effective hair removal algorithm for dermoscopic imagery employing soft color morphology operators able to cope with color images. Our hair removal filter is essentially composed of a morphological curvilinear object detector and a morphological-based inpainting algorithm. Our work is aimed at fulfilling two goals. First, to provide a successful yet efficient hair removal algorithm using the soft color morphology operators. Second, to compare it with other state-of-the-art algorithms and exhibit the good results of our approach, which maintains lesion’s features.

Keywords

Dermoscopy Hair removal Soft color morphology Black top-hat Curvilinear objects Inpainting 

References

  1. 1.
    Argenziano, G., Longo, C., Cameron, A., Cavicchini, S., et al.: Blue-black rule: a simple dermoscopic clue to recognize pigmented nodular melanoma. Br. J. Dermatol. 165(6), 1251–1255 (2011)CrossRefGoogle Scholar
  2. 2.
    Baczyński, M., Jayaram, B.: Fuzzy Implications. Studies in Fuzziness and Soft Computing, vol. 231. Springer, Heidelberg (2008)MATHGoogle Scholar
  3. 3.
    Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners, vol. 221. Springer, Heidelberg (2007)MATHGoogle Scholar
  4. 4.
    Bibiloni, P., González-Hidalgo, M., Massanet, S.: Soft color morphology. In: Submitted to IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017) (2017)Google Scholar
  5. 5.
    Informe de conclusiones. MELANOMA VISIÓN 360\({}^\circ \): Diálogos entre pacientes y profesionales. Madrid (2015). http://fundacionmasqueideas.org/documentos/. Accessed 20 July 2016
  6. 6.
    González-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D.: A fuzzy filter for high-density salt and pepper noise removal. In: Bielza, C., Salmerón, A., Alonso-Betanzos, A., Hidalgo, J.I., Martínez, L., Troncoso, A., Corchado, E., Corchado, J.M. (eds.) CAEPIA 2013. LNCS, vol. 8109, pp. 70–79. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40643-0_8 CrossRefGoogle Scholar
  7. 7.
    Kerre, E.E., Nachtegael, M.: Fuzzy Techniques in Image Processing. Studies in Fuzziness and Soft Computing, vol. 52. Physica, Heidelberg (2013)MATHGoogle Scholar
  8. 8.
    Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D.: Dullrazor®: a software approach to hair removal from images. Comput. Biol. Med. 27(6), 533–543 (1997)CrossRefGoogle Scholar
  9. 9.
    Mendonça, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: PH 2 - a dermoscopic image database for research and benchmarking. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5437–5440. IEEE (2013)Google Scholar
  10. 10.
    Serra, J.: Image Analysis and Mathematical Morphology, vol. 1. Academic Press, Cambridge (1982)MATHGoogle Scholar
  11. 11.
    Toossi, M.T.B., Pourreza, H.R., Zare, H., Sigari, M.H., et al.: An effective hair removal algorithm for dermoscopy images. Skin Res. Technol. 19(3), 230–235 (2013)CrossRefGoogle Scholar
  12. 12.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics GEMS IV, pp. 474–485. Academic Press Professional, Inc. (1994)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pedro Bibiloni
    • 1
  • Manuel González-Hidalgo
    • 1
  • Sebastia Massanet
    • 1
  1. 1.SCOPIA, Department of Mathematics and Computer ScienceUniversity of the Balearic IslandsPalmaSpain

Personalised recommendations