Skin Lesion Segmentation Using Enhanced Unified Markov Random Field

  • Omran Salih
  • Serestina ViririEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)


Markov Random Field (MRF) theory has a significant potential role in image segmentation field. It uses (pixels, regions, edges)-based on MRF models to detect objects, boundaries and other relevant information in an image. This paper proposes an extension of Unified Markov Random Field (UMRF) model to include edge-based features. Firstly, the proposed technique employs the likelihood function to combine the advantages of the pixel-based, region-based and edge-based MRF model, by computing the product of the pixel likelihood function, regional likelihood function and edge likelihood function. Secondly, the region-based macro texture features are extracted using the UMRF model. Then the edge-based features are extracted using the maximum gradient method to recover any significant lost information. A principled probabilistic inference is implemented to integrate various types of likelihood information and spatial constraints by iteratively updating the posterior probability of the proposed model. The segmentation process is completed when the iterations converge. The proposed enhanced UMRF technique which combines pixel-based, region-based and edge-based features achieved a higher skin lesion segmentation accuracy than MRF model which combines pixel-based and region-based only.


Markov Random Field Unified Markov Random Field Skin lesion Segmentation 


  1. 1.
    Celebi, M.E., et al.: Automatic detection of blue-white veil and related structures in dermoscopy images. Comput. Med. Imaging Graph. 32(8), 670–677 (2008)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Celebi, M.E., Wen, Q., Iyatomi, H., Shimizu, K., Zhou, H., Schaefer, G.: A state-of-the-art survey on lesion border detection in dermoscopy images. In: Dermoscopy Image Analysis, pp. 97–129 (2015)CrossRefGoogle Scholar
  4. 4.
    Chen, X., Zheng, C., Yao, H., Wang, B.: Image segmentation using a unified markov random field model. IET Image Process. 11(10), 860–869 (2017)CrossRefGoogle Scholar
  5. 5.
    Chien, S.-Y., Huang, Y.-W., Chen, L.-G.: Predictive watershed: a fast watershed algorithm for video segmentation. IEEE Trans. Circuits Syst. Video Technol. 13(5), 453–461 (2003)CrossRefGoogle Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  7. 7.
    Di Zenzo, S.: A note on the gradient of a multi-image. Comput. Vis. Graph. Image Process. 33(1), 116–125 (1986)CrossRefGoogle Scholar
  8. 8.
    Erdei, E., Torres, S.M.: A new understanding in the epidemiology of melanoma. Expert Rev. Anticancer Ther. 10(11), 1811–1823 (2010)CrossRefGoogle Scholar
  9. 9.
    Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M.F., Petkov, N.: Med-node: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42(19), 6578–6585 (2015)CrossRefGoogle Scholar
  10. 10.
    Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)CrossRefGoogle Scholar
  11. 11.
    Jie, F., Shi, Y., Li, Y., Liu,Z.: Interactive region-based MRF image segmentation. In: 2011 4th International Congress on Image and Signal Processing (CISP), vol. 3, pp. 1263–1267. IEEE (2011)Google Scholar
  12. 12.
    Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: Turbopixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, London (2009).
  14. 14.
    Nijsten, T., Louwman, M.W., Coebergh, J.W., et al.: Skin cancer epidemic in the Netherlands. Ned. Tijdschr. Geneeskd. 153, A768–A768 (2009)Google Scholar
  15. 15.
    Real, R., Vargas, J.M.: The probabilistic basis of jaccard’s index of similarity. Syst. Biol. 45(3), 380–385 (1996)CrossRefGoogle Scholar
  16. 16.
    Recasens, M., Hovy, E.: Blanc: implementing the rand index for coreference evaluation. Nat. Lang. Eng. 17(4), 485–510 (2011)CrossRefGoogle Scholar
  17. 17.
    Rogers, H.W., Weinstock, M.A., Feldman, S.R., Coldiron, B.M.: Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the us population 2012. JAMA Dermatology 151(10), 1081–1086 (2015)CrossRefGoogle Scholar
  18. 18.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  19. 19.
    Wartman, D., Weinstock, M.: Are we overemphasizing sun avoidance in protection from melanoma? Cancer Epidemiol. Prev. Biomarkers 17(3), 469–470 (2008)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