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Sampling with level set for pigmented skin lesion segmentation

  • Tiejun Yang
  • Yaowen Chen
  • Jiewei Lu
  • Zhun FanEmail author
Original Paper
  • 26 Downloads

Abstract

Melanoma is the deadliest form of skin cancer, and its incidence is increasing. The first step in automated melanoma analysis of dermoscopy images is to segment the area of the lesion from the surrounding skin. To improve the accuracy and adaptability of segmentation, an algorithm called sampling with level set by integrating color and texture (SLS-CT) is proposed that not only designs a new way to incorporate textural and color features in the definition of the energy functional but also utilizes an index called texture level, proposed in this work, to automatically decide the weight of each feature in the combined energies. First, at the preprocessing stage, hair and black frame removal is applied, and a potential lesion area is then obtained using Otsu thresholding and entropy maximization. Thereafter, the probability distribution of prior color in this potential lesion area is calculated as well. Second, Gabor wavelet-based texture features are extracted and integrated with the prior color into the evolving energies of the level set based on the texture level. To achieve global optimization, a Markov chain Monte Carlo sampling approach guided by the combined energy is adopted in evolving the level set, which ultimately defines a border in the image to segment a lesion from normal skin. Finally, morphological operations are used for postprocessing. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms, demonstrating that the proposed algorithm outperforms the other tested ones in terms of accuracy and adaptability to different databases.

Keywords

Pigmented skin lesion Level set Texture Markov chain Monte Carlo Image segmentation 

Notes

Acknowledgements

This research was partly supported by the Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques (2013GDDSIPL-03), the Guangxi Natural Science Foundation (2018JJB170004), the Guangxi young and middle-aged teachers basic ability promotion project (2017KY0247), the Project of Cultivating a Thousand Young and Middle-aged Teachers in Guangxi Universities and the Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System under Grant No. 2018A-07.

References

  1. 1.
    Celebi, M., Mendonca, T., Marques, J.: From dermoscopy to mobile teledermatology. In: Emre Celebi, M., Mendonca, T., Marques J.S. (eds.) Dermoscopy Image Analysis, pp. 385–418. CRC Press, Boca Raton (2015). https://www.taylorfrancis.com/books/9781482253269 CrossRefGoogle Scholar
  2. 2.
    Oliveira, R.B., Papa, J.P., Pereira, A.S., Tavares, J.M.R.S.: Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput. Appl. 29, 613–636 (2018)CrossRefGoogle Scholar
  3. 3.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 33, 148–153 (2009)CrossRefGoogle Scholar
  4. 4.
    Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56, 69–90 (2012)CrossRefGoogle Scholar
  5. 5.
    Filho, M., Ma, Z., Tavares, J.M.: A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices. J. Med. Syst. 39, 1–12 (2015)CrossRefGoogle Scholar
  6. 6.
    Oliveira, R.B., Filho, M.E., Ma, Z., Pereira, A.S.: Computational methods for the image segmentation of pigmented skin lesions. Comput. Methods Progr. Biomed. 131, 127–141 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhou, H., Schaefer, G., Sadka, A.H., Celebi, M.E.: Anisotropic mean shift based fuzzy C-means segmentation of dermoscopy images. IEEE J. Select. Top. Signal Process. 3, 26–34 (2009)CrossRefGoogle Scholar
  8. 8.
    Ashour, A.S., Hawas, A.R., Guo, Y., Wahba, M.A.: A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images. SIViP 12, 1311–1318 (2018)CrossRefGoogle Scholar
  9. 9.
    Dey, N., Rajinikanth, V., Ashour, A.S., Tavares, J.M.R.S.: Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10, 51 (2018)CrossRefGoogle Scholar
  10. 10.
    Oliveira, R.B., Marranghello, N., Pereira, A.S., Tavares, J.M.R.S.: A computational approach for detecting pigmented skin lesions in macroscopic images. Expert Syst Appl Int J 61, 53–63 (2016)CrossRefGoogle Scholar
  11. 11.
    Zhou, H., Li, X., Schaefer, G., Celebi, M.E., Miller, P.: Mean shift based gradient vector flow for image segmentation. Comput. Vis. Image Underst. 117, 1004–1016 (2013)CrossRefGoogle Scholar
  12. 12.
    Li, W., Li, F., Du, J.: A level set image segmentation method based on a cloud model as the priori contour. Signal Image Video Process. (2018).  https://doi.org/10.1007/s11760-018-1334-5
  13. 13.
    Ma, Z., Tavares, J.M.R.S.: Effective features to classify skin lesions in dermoscopic images. Expert Syst. Appl. 84, 92–101 (2017)CrossRefGoogle Scholar
  14. 14.
    Oliveira, R.B., Pereira, A.S., Tavares, J.M.R.S.: Pattern recognition in macroscopic and dermoscopic images for skin lesion diagnosis. In: VipIMAGE 2017, Lecture Notes in Computational Vision and Biomechanics, vol. 27, pp. 504–514. Springer, Cham (2018)Google Scholar
  15. 15.
    Roth, H.R., Lu, L., Farag, A., Shin, H.C., Liu, J., Turkbey, E.B., Summers, R.M.: DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: Medical Image Computing and Computer-Assisted Intervention, vol. 9349, pp. 556–564. Munich (2015)Google Scholar
  16. 16.
    Hu, P., Yang, T.J.: Pigmented skin lesions detection using random forest and wavelet based texture. In: Proceeding of SPIE 10024, pp. 1X1–1X7 (2016)Google Scholar
  17. 17.
    Jafari, M.H., Nasresfahani, E., Karimi, N., Soroushmehr, S.M.R., Samavi, S., Najarian, K.: Extraction of skin lesions from non-dermoscopic images using deep learning. CoRR abs/1609.02374 (2016)Google Scholar
  18. 18.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)CrossRefzbMATHGoogle Scholar
  19. 19.
    Silveira, M., Nascimento, J.C., Marques, J.S., Marcal, A.R.S., Mendonca, T., Yamauchi, S., Maeda, J., Rozeira, J.: Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J. Selected Top. Signal Process. 3, 35–45 (2009)CrossRefGoogle Scholar
  20. 20.
    Erkol, B., Moss, R.H., Stanley, R.J., Stoecker, W.V., Hvatum, E.: Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res. Technol. 11, 17–26 (2005)CrossRefGoogle Scholar
  21. 21.
    Nascimento, J.C., Marques, J.S.: Adaptive snakes using the EM algorithm. IEEE Trans. Image Process. 14, 1678–1686 (2005)CrossRefGoogle Scholar
  22. 22.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)CrossRefzbMATHGoogle Scholar
  23. 23.
    Ma, Z., Tavares, J.M.: A novel approach to segment skin lesions in dermoscopic images based on a deformable model. IEEE J. Biomed. Health Inf. 20, 615–623 (2016)CrossRefGoogle Scholar
  24. 24.
    Chang, J., Fisher, J.W.: Efficient MCMC sampling with implicit shape representations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2081–2088. Providence (2011)Google Scholar
  25. 25.
    Oliveira, R.B., Pereira, A.S., Tavares, J.M.R.S.: Computational diagnosis of skin lesions from dermoscopic images using combined features. Neural Comput. Appl. (2018).  https://doi.org/10.1007/s00521-018-3439-8
  26. 26.
    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: Celebi, M.E., Mendonca, T., Marques, J.S. (eds.) Dermoscopy Image Analysis, pp. 97–129. CRC Press, Boca Raton (2015)CrossRefGoogle Scholar
  27. 27.
    Celebi, M., Iyatomi, H., Schaefer, G., Stoecker, W.: Approximate lesion localization in dermoscopy images. Skin Res. Technol. 15, 314–322 (2010)CrossRefGoogle Scholar
  28. 28.
    Lee, T., Ng, V., Gallagher, R., Coldman, A., Mclean, D.: DullRazor: a software approach to hair removal from images. Comput. Biol. Med. 27, 533–543 (1997)CrossRefGoogle Scholar
  29. 29.
    Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40, 200–210 (2013)CrossRefGoogle Scholar
  30. 30.
    Mokrzycki, W.S., Tatol, M.: Color difference Delta E—A survey. Mach. Graph. Vis. 20, 383–411 (2011)Google Scholar
  31. 31.
    Schaefer, G., Rajab, M.I., Celebi, M.E., Iyatomi, H.: Colour and contrast enhancement for improved skin lesion segmentation. Comput. Med. Imaging Graph. 35, 99–104 (2011)CrossRefGoogle Scholar
  32. 32.
    Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)CrossRefGoogle Scholar
  33. 33.
    An, N.-Y., Pun, C.-M.: Color image segmentation using adaptive color quantization and multiresolution texture characterization. SIViP 8, 943–954 (2014)CrossRefGoogle Scholar
  34. 34.
    Lee, T.S.: Image representation using 2D Gabor wavelet. IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (2002)Google Scholar
  35. 35.
    Tsai, S.C., Tzeng, W.G., Wu, H.L.: On the Jensen–Shannon divergence and variational distance. IEEE Trans. Inform. Theory 51, 3333–3336 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Baumgartner, J., Flesia, A.G., Gimenez, J., Pucheta, J.: A new image segmentation framework based on two-dimensional hidden Markov models. Integr. Comput. Aided Eng. 23, 1–13 (2016)CrossRefGoogle Scholar
  37. 37.
    Celebi, E.M., Quan, W., Sae, H., Hitoshi, I., Gerald, S.: Lesion border detection in dermoscopy images using ensembles of thresholding methods. Skin Res. Technol. 19, e252–e258 (2013)CrossRefGoogle Scholar
  38. 38.
    Mendonca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., Rozeira, J.: PH2—A dermoscopic image database for research and benchmarking. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5437–5440 (2013). http://www.fc.up.pt/addi/ph2%20database.html
  39. 39.
    Celebi, M., Kingravi, H., Aslandogan, Y., Stoecker, W., Moss, R., Malters, J., Grichnik, J., Marghoob, A., Rabinovitz, H., Menzies, S.: Border detection in dermoscopy images using statistical region merging. Skin Res. Technol. 14, 347–353 (2008)CrossRefGoogle Scholar
  40. 40.
    Ahn, E., Kim, J., Bi, L., Kumar, A., Li, C., Fulham, M., Feng, D.D.: Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J. Biomed. Health Inf. 21, 1685–1693 (2017)CrossRefGoogle Scholar
  41. 41.
    Garnavi, R., Aldeen, M., Celebi, M.E., Varigos, G., Finch, S.: Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput. Med. Imaging Graph. 35, 105–115 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques, Department of Electrical EngineeringShantou UniversityGuangdongPeople’s Republic of China

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