A Skin Lesion Segmentation Method Based on Saliency and Adaptive Thresholding in Wavelet Domain

  • Kai HuEmail author
  • Si Liu
  • Yuan Zhang
  • Chunhong Cao
  • Fen Xiao
  • Wei Huang
  • Xieping Gao
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


Segmentation is the essential requirement in automated computer-aided diagnosis (CAD) of skin diseases. In this paper, we propose an unsupervised skin lesion segmentation method to challenge the difficulties existing in the dermoscopy images such as low contrast, border indistinct, and skin lesion is close to the boundary. Our method combines the enhanced fusion saliency with adaptive thresholding based on wavelet transform to get the lesion regions. Firstly, the saliency map increases the contract of the skin lesion and healthy skin, and then an adaptive thresholding method based on wavelet transform is used to obtain more accurate lesion regions. Experiments on dermoscopy images demonstrate the effectiveness of the proposed method over several state-of-the-art methods in terms of quantitative results and visual effects.


Saliency map Adaptive thresholding Wavelet transform Dermoscopy images Segmentation 



This work was supported by the National Natural Science Foundation of China under Grants no. 61802328 and 61771415, and the Cernet Innovation Project under Grant no. NGII20170702.


  1. 1.
    Navarro, F., Escudero-Vinolo, M., Bescos, J.: Accurate segmentation and registration of skin lesion images to evaluate lesion change. IEEE J. Biomed. Health Inform. (99), 1 (2018)Google Scholar
  2. 2.
    Stewart, B.W., Wild, C.P.: World Cancer Report 2014, p. 953. World Health Organization (2014)Google Scholar
  3. 3.
    Jahanifar, M., Tajeddin, N.Z., Asl, B.M., Gooya, A., et al.: Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE J. Biomed. Health Inform. (2018)Google Scholar
  4. 4.
    Silveira, M., Nascimento, J.C., Marques, J.S., et al.: Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J. Sel. Top. Signal Process. 3(1), 35–45 (2009)CrossRefGoogle Scholar
  5. 5.
    Ahn, E., Kim, J., Bi, L., et al.: Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J. Biomed. Health Inform. 21(6), 1685–1693 (2017)CrossRefGoogle Scholar
  6. 6.
    Pathan, S., Prabhu, K.G., Siddalingaswamy, P.C.: Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—a review. Biomed. Signal Process. Control 39, 237–262 (2018)CrossRefGoogle Scholar
  7. 7.
    Lu, H., Li, B., Zhu, J., et al.: Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput. Pract. Exp. 29(6), e3927 (2017)CrossRefGoogle Scholar
  8. 8.
    Xu, X., He, L., Lu, H., et al.: Deep adversarial metric learning for cross-modal retrieval. In: World Wide Web, pp. 1–16 (2018)Google Scholar
  9. 9.
    Yüksel, M.E., Borlu, M.: Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 17(4), 976–982 (2009)CrossRefGoogle Scholar
  10. 10.
    Emre Celebi, M., Kingravi, H.A., Iyatomi, H., et al.: Border detection in dermoscopy images using statistical region merging. Ski. Res. Technol. 14(3), 347–353 (2008)CrossRefGoogle Scholar
  11. 11.
    Serikawa, S., Lu, H.: Underwater image dehazing using joint trilateral filter. Comput. Electr. Eng. 40(1), 41–50 (2014)CrossRefGoogle Scholar
  12. 12.
    Kasmi, R., Mokrani, K., Rader, R.K., et al.: Biologically inspired skin lesion segmentation using a geodesic active contour technique. Ski. Res. Technol. 22(2), 208–222 (2016)CrossRefGoogle Scholar
  13. 13.
    Fan, H., Xie, F., Li, Y., et al.: Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold. Comput. Biol. Med. 85, 75–85 (2017)CrossRefGoogle Scholar
  14. 14.
    Cheng, M.M., Mitra, N.J., Huang, X., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)CrossRefGoogle Scholar
  15. 15.
    Lu, H., Li, Y., Mu, S., et al.: Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J. (2017)Google Scholar
  16. 16.
    Lu, H., Li, Y., Chen, M., et al.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23(2), 368–375 (2018)CrossRefGoogle Scholar
  17. 17.
    Zhu, W., Liang, S., Wei, Y., et al.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)Google Scholar
  18. 18.
    Lee, T., Ng, V., Gallagher, R., et al.: Dullrazor®: a software approach to hair removal from images. Comput. Biol. Med. 27(6), 533–543 (1997)CrossRefGoogle Scholar
  19. 19.
    Ahn, E., Bi, L., Jung, Y.H., et al.: Automated saliency-based lesion segmentation in dermoscopic images. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3009–3012. IEEE (2015)Google Scholar
  20. 20.
    Zhang, X.P., Desai, M.D.: Segmentation of bright targets using wavelets and adaptive thresholding. IEEE Trans. Image Process. 10(7), 1020–1030 (2001)CrossRefGoogle Scholar
  21. 21.
    Hu, K., Gao, X., Li, F.: Detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in mammograms. IEEE Trans. Instrum. Meas. 60(2), 462–472 (2011)CrossRefGoogle Scholar
  22. 22.
    Flores, E., Scharcanski, J.: Segmentation of melanocytic skin lesions using feature learning and dictionaries. Expert Syst. Appl. 56, 300–309 (2016)CrossRefGoogle Scholar
  23. 23.
    Mendonça, T., Ferreira, P.M., Marques, J.S., et al.: 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
  24. 24.
    Abuzaghleh, O., Barkana, B.D., Faezipour, M.: Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J. Transl. Eng. Health Med. 3, 1–12 (2015)CrossRefGoogle Scholar
  25. 25.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kai Hu
    • 1
    • 2
    Email author
  • Si Liu
    • 1
  • Yuan Zhang
    • 1
  • Chunhong Cao
    • 1
  • Fen Xiao
    • 1
  • Wei Huang
    • 3
  • Xieping Gao
    • 1
  1. 1.Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityXiangtanChina
  2. 2.Postdoctoral Research Station for Mechanics, Xiangtan UniversityXiangtanChina
  3. 3.Department of RadiologyThe First Hospital of ChangshaChangshaChina

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