Pattern Analysis of Dermoscopic Images Based on FSCM Color Markov Random Fields

  • Carlos S. Mendoza
  • Carmen Serrano
  • Begoña Acha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)


In this paper a method for pattern analysis in dermoscopic images of abnormally pigmented skin (melanocytic lesions) is presented. In order to diagnose a possible skin cancer, physicians assess the lesion according to different rules. The new trend in Dermatology is to classify the lesion by means of pattern irregularity. In order to analyze the pattern turbulence, lesions ought to be segmented into single pattern regions. Our classification method, when applied on overlapping lesion patches, provides a pattern chart that could ultimately allow for in-region single-texture turbulence analysis. Due to the color-textured appearance of these patterns, we present a novel method based on a Finite Symmetric Conditional Model (FSCM) Markov Random Field (MRF) color extension for the characterization and discrimination of pattern samples. Our classification success rate rises to 86%.


Color Space Markov Random Field Healthy Skin Markov Random Melanocytic Lesion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Stolz, W., Braun-Falco, O., Bilek, P., Landthaler, M., Burgdorf, W.H.C., Cognetta, A.B.: Color Atlas of Dermatoscopy. Blackwell Wissenschafts-Verlag, Berlin (2002)Google Scholar
  2. 2.
    Westerhoff, K., McCarthy, W.H., Menzies, S.W.: Increase in the sensitivity for melanoma diagnosis by primary care physicians using skin surface microscopy. British Journal of Dermatology 143(5), 1016–1020 (2000)CrossRefGoogle Scholar
  3. 3.
    Binder, M., Kittler, H., Seeber, A., Steiner, A., Pehamberger, H., Wolff, K.: Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network. Melanoma Research 8(3), 261–266 (1998)CrossRefGoogle Scholar
  4. 4.
    Schmidt, P.: Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Transactions on Medical Imaging 18(2), 164–171 (1999)CrossRefGoogle Scholar
  5. 5.
    Schmid-Saugeon, P., Guillod, J., Thiran, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics 27(1), 65–78 (2003)CrossRefGoogle Scholar
  6. 6.
    Stoecker, W.V., Li, W.W., Moss, R.H.: Automatic detection of asymmetry in skin tumors. Computerized Medical Imaging and Graphics 16(3), 191–197 (1992)CrossRefGoogle Scholar
  7. 7.
    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 Research and Technology 11(1), 17–26 (2005)CrossRefGoogle Scholar
  8. 8.
    Lee, T.K., Claridge, E.: Predictive power of irregular border shapes for malignant melanomas. Skin Research and Technology 11(1), 1–8 (2005)CrossRefGoogle Scholar
  9. 9.
    Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.: A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions. IEEE Transactions on Medical Imaging 22(8), 959–964 (2003)CrossRefGoogle Scholar
  10. 10.
    Golston, J.E., Moss, R.H., Stoecker, W.V.: Boundary detection in skin tumor images: An overall approach and a radial search algorithm. Pattern Recognition 23(11), 1235–1247 (1990)CrossRefGoogle Scholar
  11. 11.
    Stanley, R.J., Moss, R.H., Stoecker, W.V., Aggawal, C.: A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images. Computerized Medical Imaging and Graphics 27(5), 387–396 (2003)CrossRefGoogle Scholar
  12. 12.
    Tommasi, T., Torre, E.L., Caputo, B.: Melanoma recognition using representative and discriminative kernel classifiers. In: Beichel, R.R., Sonka, M. (eds.) CVAMIA 2006. LNCS, vol. 4241, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Tanaka, T., Torii, S., Kabuta, I., Shimizu, K., Tanaka, M.: Pattern classification of nevus with texture analysis. IEEJ Transactions on Electrical and Electronic Engineering 3(1), 143–150 (2008)CrossRefGoogle Scholar
  14. 14.
    Serrano, C., Acha, B.: Pattern analysis of dermoscopic images based on markov random fields. Pattern Recognition 42(6), 1052–1057 (2009)CrossRefGoogle Scholar
  15. 15.
    Panjwani, D., Healey, G.: Results using random field models for the segmentation of color images of natural scenes, pp. 714–719 (1995)Google Scholar
  16. 16.
    Kato, Z., Pong, T.C.: A markov random field image segmentation model for color textured images. Image and Vision Computing 24(10), 1103–1114 (2006)CrossRefGoogle Scholar
  17. 17.
    Tab, F.A., Naghdy, G., Mertins, A.: Scalable multiresolution color image segmentation. Signal Processing 86(7), 1670–1687 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Gao, J., Zhang, J., Fleming, M.G., Pollak, I., Cognetta, A.B.: Segmentation of dermatoscopic images by stabilized inverse diffusion equations, vol. 3, pp. 823–827 (1998)Google Scholar
  19. 19.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Tokyo (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Xia, Y., Feng, D., Zhao, R.: Adaptive segmentation of textured images by using the coupled markov random field model. IEEE Transactions on Image Processing 15(11), 3559–3566 (2006)CrossRefGoogle Scholar
  21. 21.
    Kashyap, R.L., Chellappa, R.: Estimation and choice of neighbors in spatial-interaction models of images. IEEE Transactions on Information Theory IT-29(1), 60–72 (1983)CrossRefzbMATHGoogle Scholar
  22. 22.
    Chen, Y., Hao, P.: Optimal transform in perceptually uniform color space and its application in image retrieval, vol. 2, pp. 1107–1110 (2004)Google Scholar
  23. 23.
    Manjunath, B.S., Simchony, T., Chellappa, R.: Stochastic and deterministic networks for texture segmentation. IEEE Transactions on Acoustics, Speech, and Signal Processing 38(6), 1039–1049 (1990)CrossRefGoogle Scholar
  24. 24.
    Manjunath, B.S., Chellappa, R.: Unsupervised texture segmentation using markov random field models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(5), 478–482 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Carlos S. Mendoza
    • 1
  • Carmen Serrano
    • 1
  • Begoña Acha
    • 1
  1. 1.Universidad of SevillaSevillaSpain

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