Skin Lesion Images Segmentation: A Survey of the State-of-the-Art

  • Adegun Adekanmi AdeyinkaEmail author
  • Serestina Viriri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)


This paper presents a detailed and robust survey of the state-of-the-art algorithms and techniques for performing skin lesion segmentation. The approach used is the comparative analysis of the existing methods for skin lesion analysis, critical review of the performance evaluation of some recently developed algorithms for skin lesion images segmentation, and the study of current evaluating metrics used for performance analysis. The study highlights merits and demerits of the algorithms examined, observing the strength and weakness of each algorithm. An inference can thus be made from the analysis about the best performing algorithms. It is observed that the advancement of technology and availability of a large and voluminous data set for training the machine learning algorithms encourage the application of machine learning techniques such as deep learning for performing skin lesion images segmentation. This work shows that most deep learning techniques out-perform some existing state-of-the arts algorithm for skin lesion images segmentation.


Segmentation Skin lesion Evaluation metrics Deep learning 


  1. 1.
    Celebi, M.E., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)CrossRefGoogle Scholar
  2. 2.
    Oliveira, R.B., Filho, M.E., Ma, Z., Papa, J.P., Pereira, A.S., Tavares, J.M.R.S.: Computational methods for the image segmentation of pigmented skin lesions: a review. Comput. Methods Programs Biomed. 131, 127–141 (2016)CrossRefGoogle Scholar
  3. 3.
    Okuboyejo, D.A., Olugbara, O.O.: A review of prevalent methods for automatic skin lesion diagnosis. Open Dermatol. J. 12, 14–53 (2018)CrossRefGoogle Scholar
  4. 4.
    Cavalcanti, P.G., Scharcanski, J.: Macroscopic pigmented skin lesion seg-mentation and its inuence on the lesion classication and diagnosis. In: Celebi, M., Schaefer, G. (eds.) Color Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol. 6, pp. 15–39. Springer, Dordrecht (2013). Scholar
  5. 5.
    Pathan, S., Prabhu, K.G., Siddalingaswamy, P.C.: Techniques and algorithms for computer aided diagnosis of pigmented skin - a review. Biomed. Signal Process. Control 39, 237–262 (2018)CrossRefGoogle Scholar
  6. 6.
    Revathi, V., Chithra, A.: A review on segmentation techniques in skin lesion images. Int. Res. J. Eng. Technol. (2015). e-ISSN 2395-0056Google Scholar
  7. 7.
    Hsu, T.-Y., Fuh, C.-S.: Pedestrian Contour Detection Based on Image Segmentation, pp. 1–6 (2010)Google Scholar
  8. 8.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph 33(2), 148–153 (2010)CrossRefGoogle Scholar
  9. 9.
    Lin, B.S., Michael, K., Kalra, S., Tizhoosh, H.R.: Skin lesion segmentation: U-Nets versus clustering. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, SSCI 2017, pp. 1–7 (2018)Google Scholar
  10. 10.
    Alvarez, D., Iglesias, M.: k-Means clustering and ensemble of regressions: an algorithm for the ISIC 2017 skin lesion segmentation challenge. ArXiv preprint arXiv:1702.07333 (2017)
  11. 11.
    Attia, M., Hossny, M., Nahavandi, S., Yazdabadi, A.: Spatially aware melanoma segmentation using hybrid deep learning techniques. arXiv preprint arXiv:1702.07963 (2017)
  12. 12.
    Wen, H.: II-FCN for skin lesion analysis towards melanoma detection. In: ISIC 2017. Accessed 13 Aug 2018
  13. 13.
    Jahanifar, M., Tajeddin, N.Z., Asl, B.M.: Supervised saliency map driven segmentation of the lesions in dermoscopic images. Accessed 13 Aug 2018
  14. 14.
    Berseth, M.: Skin lesion analysis towards melanoma detection. Accessed 13 Aug 2018
  15. 15.
    Chang, H.: Skin cancer reorganization and classification with deep neural network. Accessed 13 Aug 2018
  16. 16.
    Ramachandram, D., Devries, T.: LesionSeg: semantic segmentation of skin lesions using deep convolutional neural network. In: ISIC 2017. Accessed 13 Aug 2018
  17. 17.
    Li, Y., Shen, L.: Skin lesion analysis towards melanoma detection using deep learning network. Accessed 13 Aug 2018
  18. 18.
    Galdran, A., Alvarez, A.: Data-driven color augmentation techniques for deep skin image analysis. Accessed 13 Aug 2018
  19. 19.
    Guarracino, M.R., Maddalena, L.: Segmenting dermoscopic images. Accessed 13 Aug 2018
  20. 20.
    Garcia-Arroyo, J.L., Garcia-Zapirain, B.: Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding. Accessed 13 Aug 2018
  21. 21.
    Yuan, Y.: Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. Accessed 13 Aug 2018
  22. 22.
    Bi, L., Kim, J., Ahn, E., Feng, D.: Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. Accessed 13 Aug 2018
  23. 23.
    Menegola, A., Tavares, J., Fornaciali, M., Li, L.T., Avila, S., Valle, E.: RECOD Titans at ISIC challenge 2017. Accessed 13 Aug 2018
  24. 24.
    Qi, J., Le, M., Li, C., Zhou, P.: Global and local information based deep network for skin lesion segmentation. Accessed 13 Aug 2018
  25. 25.
    Jaisakthi, S.M., Chandrabose, A., Mirunalini, P.: Automatic skin lesion segmentation using semi-supervised learning technique. Accessed 13 Aug 2018
  26. 26.
    Wiselin Jiji, G., Johnson Durai Raj, P.: An extensive technique to detect and analyze melanoma. Accessed 13 Aug 2018
  27. 27.
    Kawahara, J., Hamarneh, G.: Fully convolutional networks to detect clinical dermoscopic features. Accessed 13 Aug 2018
  28. 28.
    Gutiérrez-Arriola, J.M., Gómez-Álvarez, M., Osma-Ruiz, V., Sáenz-Lechón, N., Fraile, R.: Skin lesion segmentation based on preprocessing, thresholding and neural networks. Accessed 13 Aug 2018
  29. 29.
    Andre, E., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Maths, Statistics and Computer ScienceUniversity of KwaZulu-NatalDurbanSouth Africa

Personalised recommendations