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What Is the Role of Color Symmetry in the Detection of Melanomas?

  • Margarida Ruela
  • Catarina Barata
  • Jorge S. Marques
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8033)

Abstract

Several computer aided diagnosis (CAD) systems have been proposed to detect melanomas in dermoscopy images. Most of them rely on the extraction of several types of visual features: color, texture and shape. However, the role of each type of feature is seldom assessed. This paper proposes several features for the analysis of color symmetry in skin lesions and assesses their performance under a wide variety of system configurations. We have obtained very high detection scores (SE=96%, SP=83%) by only using color symmetry features, showing that they play a major role in the analysis of dermoscopy images.

Keywords

Skin Lesions Melanoma Computer Aided Diagnosis (CAD) system Color Symmetry Lesion Classification 

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References

  1. 1.
    American Cancer Society: Melanoma Skin Cancer, http://www.cancer.org/acs/groups/cid/documents/webcontent/003120-pdf
  2. 2.
  3. 3.
    Nachbar, F., Stolz, W., Merkle, T., Cognetta, A.B., Vogt, T., Landthaler, M., Bilek, P., Braun-Falco, O., Plewig, G.: The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Am. Acad. Dermatol. 30(4), 551–559 (1994)CrossRefGoogle Scholar
  4. 4.
    Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134, 1563–1570 (1998)CrossRefGoogle Scholar
  5. 5.
    Iyatomi, H., Celebi, M.E., Oka, H., Tanaka, M.: An Improved Internet-Based Melanoma Screening System with Dermatologist-like Tumor Area Extraction Algorithm. Comp. Medical Imaging and Graphics 32, 566–579 (2008)CrossRefGoogle Scholar
  6. 6.
    Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Comp. Medical Imaging and Graphics 31(6), 362–371 (2007)CrossRefGoogle Scholar
  7. 7.
    Ganster, H., Pinz, A., Rohrer, R., Wildling, E., Blinder, M., Kittler, H.: Automated Melanoma Recognition. IEEE Trans. on Biom. Eng. 20(3), 233–239 (2001)Google Scholar
  8. 8.
    Gotkowick-Krusin, D., Elbaum, M., Szwaykowski, P., Kopf, A.W.: Can early malignant melanoma be differenciated from atypical melanocytic nevus by in vivo techniques? Part II. Automatic machine vision classification. Skin Research and Technology 3, 15–22 (1997)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Seidenari, S., Pellacani, G., Grana, C.: Pigment distribution in melanocytic lesion images: a digital parameter to be employed for computer-aided diagnosis. Skin Research and Technology 11, 236–241 (2005)CrossRefGoogle Scholar
  11. 11.
    Seidenari, S., Pellacani, G., Grana, C.: Asymmetry in dermoscopic melanocytic lesion images: a computer description based on colour distribution. Acta Derm Venereol 86(2), 123–128 (2006)Google Scholar
  12. 12.
    Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S.: Two Systems for the Detection of Melanomas in Dermoscopy Images using Texture and Color Features. IEEE System Journal (accepted, 2013)Google Scholar
  13. 13.
    Mindru, F., Moons, T., Van Gool, L.: Recognizing Color Patterns Irrespective of Viewpoint and Illumination. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 368–373 (1999)Google Scholar
  14. 14.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer (2002)Google Scholar
  15. 15.
    Mendonça, T., Ferreira, P.M., Marques, J., Marçal, A.R.S., Rozeira, J.: Accepted for presentation in Proc. PH2 - A Dermoscopic Image Database for Research and Benchmarking. IEEE EMBC (2013)Google Scholar
  16. 16.
    Barata, C., Marques, J.S., Rozeira, J.: A System for the Detection of Pigment Network in Dermoscopy Images Using Directional Filters. IEEE Trans. on Biom. Eng. 59(10), 2744–2754 (2012)CrossRefGoogle Scholar
  17. 17.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification and scene analysis. Part 1. Pattern classification. Wiley (2001)Google Scholar
  18. 18.
    Viola, P., Michael, J.: Robust real-time face detection. Inter. J. of Comp. Vision 57, 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Margarida Ruela
    • 1
  • Catarina Barata
    • 1
  • Jorge S. Marques
    • 1
  1. 1.Institute for Systems and Robotics, Instituto Superior TecnicoLisboaPortugal

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