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Skin Lesion Segmentation Based on Region-Edge Markov Random Field

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

This paper presents a probabilistic model based on Markov Random Field (MRF) theory to achieve skin lesion segmentation. MRF theory plays a significant potential role in the image segmentation field. It has several models based on its theory such as region-based MRF model and edge-based MRF model to detect object, boundaries and other relevant information in an image. The proposed method aims to combine the advantages of these two models by computing the product of the regional likelihood function and edge likelihood function. Regional features and edge features are used to solve the maximum a posteriori (MAP) estimation problem to find the best estimation for better image segmentation. The algorithm starts from pre-processing obtained by convolution technique, and iteratively refines the segmentation by taking into account several metrics of region homogeneity under a probabilistic framework. The technical content is described in detail, and the algorithm was tested on the International Skin Imaging Collaboration (ISIC) database, showing its potential. The proposed method shows a significant improvement when compared with individual lesion segmentation methods in ISIC 2018 challenge with overall results achieved as Jaccard Index of \(76.40\%\).

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References

  1. Besag, J.: On the statistical analysis of dirty pictures. J. Roy. Stat. Soc.: Ser. B (Methodol.) 48(3), 259–279 (1986)

    MathSciNet  MATH  Google Scholar 

  2. Derin, H., Elliott, H.: Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 39–55 (1987)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Celebi, M.E., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)

    Article  Google Scholar 

  5. Chien, S.Y., Huang, Y.W., Chen, L.G.: Predictive watershed: a fast watershed algorithm for video segmentation. IEEE Trans. Circ. Syst. Video Technol. 13(5), 453–461 (2003)

    Article  Google Scholar 

  6. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 5, 603–619 (2002)

    Article  Google Scholar 

  7. Detmar, M.: Tumor angiogenesis. J. Invest. Dermatol. Symp. Proc. 5(1), 20–23 (2000)

    Article  Google Scholar 

  8. 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 

  9. Di Zenzo, S.: A note on the gradient of a multi-image. Comput. Vis. Graph. Image Process. 33(1), 116–125 (1986)

    Article  Google Scholar 

  10. Torkashvand, F., Fartash, M.: Automatic segmentation of skin lesion using Markov random field. Can. J. Basic Appl. Sci. 3(3), 93–107 (2015)

    Google Scholar 

  11. Eltayef, K., Li, Y., Liu, X.: Lesion segmentation in dermoscopy images using particle swarm optimization and Markov random field. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 739–744. IEEE (2017)

    Google Scholar 

  12. Krishnamachari, S., Chellappa, R.: Multiresolution Gauss-Markov random field models for texture segmentation. IEEE Trans. Image Process. 6(2), 251–267 (1997)

    Article  Google Scholar 

  13. Noda, H., Shirazi, M.N., Kawaguchi, E.: MRF-based texture segmentation using wavelet decomposed images. Pattern Recogn. 35(4), 771–782 (2002)

    Article  Google Scholar 

  14. Xia, Y., Feng, D., Zhao, R.: Semi-supervised segmentation of textured images by using coupled MRF model. In: TENCON 2005-2005 IEEE Region 10 Conference, pp. 1–5. IEEE (2005)

    Google Scholar 

  15. Xia, G.S., He, C., Sun, H.: An unsupervised segmentation method using Markov random field on region adjacency graph for SAR images. In: 2006 CIE International Conference on Radar, pp. 1–4. IEEE (2006)

    Google Scholar 

  16. Yu, Q., Clausi, D.A.: IRGS: image segmentation using edge penalties and region growing. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2126–2139 (2008)

    Article  Google Scholar 

  17. Kuo, W.F., Sun, Y.N.: Watershed segmentation with automatic altitude selection and region merging based on the Markov random field model. Int. J. Pattern Recognit Artif Intell. 24(1), 153–171 (2010)

    Article  MathSciNet  Google Scholar 

  18. ISIC: Skin Lesion Analysis Towards Melanoma Detection (2019). https://challenge2018.isic-archive.com/. Accessed 13 June

  19. Jerant, A.F., Johnson, J.T., Demastes Sheridan, C., Caffrey, T.J.: Early detection and treatment of skin cancer. Am. Fam. Physician 62(2), 381–382 (2000)

    Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. Li, S.Z.: Markov Random Field Modeling in Image Analysis, 3rd edn. Springer, London (2009). https://doi.org/10.1007/978-1-84800-279-1

    Book  MATH  Google Scholar 

  22. Jaisakthi, S.M., Chandrabose, A., Mirunalini, P.: Automatic skin lesion segmentation using semi-supervised learning technique. arXiv preprint arXiv:1703.04301 (2017)

  23. Real, R., Vargas, J.M.: The probabilistic basis of Jaccard’s index of similarity. Syst. Biol. 45(3), 380–385 (1996)

    Article  Google Scholar 

  24. Recasens, M., Hovy, E.: BLANC: implementing the Rand index for coreference evaluation. Nat. Lang. Eng. 17(4), 485–510 (2011)

    Article  Google Scholar 

  25. 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 Dermatol. 151(10), 1081–1086 (2015)

    Article  Google Scholar 

  26. Salih, O., Viriri, S.: Skin cancer segmentation using a unified Markov random field. In: Bebis, G., et al. (eds.) ISVC 2018. LNCS, vol. 11241, pp. 25–33. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03801-4_3

    Chapter  Google Scholar 

  27. Salih, O., Viriri, S.: Skin lesion segmentation using enhanced unified Markov random field. In: Groza, A., Prasath, R. (eds.) MIKE 2018. LNCS (LNAI), vol. 11308, pp. 331–340. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05918-7_30

    Chapter  Google Scholar 

  28. 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)

  29. Sathya, B., Manavalan, R.: Image segmentation by clustering methods: performance analysis. Int. J. Comput. Appl. 29(11), 27–32 (2011)

    Google Scholar 

  30. Shi, J., Malik, J.: Normalized cuts and image segmentation. Departmental Papers (CIS), p. 107 (2000)

    Google Scholar 

  31. Garcia-Arroyo, J.L., Garcia-Zapirain, B.: Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding. arXiv preprint arXiv:1703.03888 (2017)

  32. Wong, A., Scharcanski, J., Fieguth, P.: Automatic skin lesion segmentation via iterative stochastic region merging. IEEE Trans. Inf Technol. Biomed. 15(6), 929–936 (2011)

    Article  Google Scholar 

  33. Chen, X., Zheng, C., Yao, H., Wang, B.: Image segmentation using a unified Markov random field model. IET Image Proc. 11(10), 860–869 (2017)

    Article  Google Scholar 

  34. Xu, L., et al.: Segmentation of skin cancer images. Image Vis. Comput. 17(1), 65–74 (1999)

    Article  MathSciNet  Google Scholar 

  35. Jahanifar, M., Tajeddin, N.Z., Asl, B.M., Gooya, A.: Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE J. Biomed. Health Inf. 23(2), 509–518 (2018)

    Article  Google Scholar 

  36. Galdran, A., et al.: Data-driven color augmentation techniques for deep skin image analysis. arXiv preprint arXiv:1703.03702 (2017)

  37. Gutierrez-Arriola, J.M., Gomez-Alvarez, M., Osma-Ruiz, V., Saenz-Lechon, N., Fraile, R.: Skin lesion segmentation based on preprocessing, thresholding and neural networks. arXiv preprint arXiv:1703.04845 (2017)

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Omran Salih, Serestina Viriri & Adekanmi Adegun

Authors
  1. Omran Salih
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  2. Serestina Viriri
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  3. Adekanmi Adegun
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Salih, O., Viriri, S., Adegun, A. (2019). Skin Lesion Segmentation Based on Region-Edge Markov Random Field. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

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