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Automated Diagnosis of Skin Cancer Using Digital Image Processing and Mixture-of-Experts

  • Martin Kreutz
  • Maik Anschütz
  • Stefan Gehlen
  • Thorsten Grünendick
  • Klaus Hoffmann
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

The incidence of malignant melanoma, the most lethal form of skin cancers, has risen rapidly during the last decades. Fortunately, if detected early, even malignant melanoma can be treated successfully. Thus, in recent years, methods for automated detection and diagnosis of skin cancer, particulary malignant melanoma, have elicited much interest. In this paper we present an artificial neural network approach for the classification of skin lesions. Sophisticated image processing, feature extraction, pattern recognition and methods from the field of statistics and artificial neural networks are combined in order to achieve a fast and reliable diagnosis. With this approach, for reasonably balanced training and test sets, we are able to obtain above 90% correct classification of malignant and benign skin lesions coming from the DANAOS data collection.

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References

  1. 1.
    R J Friedman, D S Rigel, and A W Kopf. Early detection of malignant melanoma: The role of physician examination and self-examination of the skin. Ca-A Cancer J Clinicians, 35:130–151, 1985.CrossRefGoogle Scholar
  2. 2.
    W V Stoecker, W W Li, and R H Moss. Automatic detection of asymmetry in skin tumors. Computarized Medical Imaging and Graphics, 16:191–197, 1992.CrossRefGoogle Scholar
  3. 3.
    R Husemann, S Tölg, W v Seelen, P Altmeyer, P J Frosch, M Stücker, K Hoffmann, and S El-Gammal. Computerised diagnosis of skin cancer using neural networks. In P Altmeyer, K Hoffmann, and M Stucker, editors, Congress on Skin Cancer and UV Radiation (1996), pages 1052–1063. Springer, 1997.Google Scholar
  4. 4.
    W Stolz, R Schiffner, L Pillet, T Vogt, H Harms, T Schindewolf, M Landthaler, and W Abmayr. Improvement of monitoring of melanocytic skin lesions with the use of a computarized acquisition and surveillance unit with a skin surface microscopic television camera. J Am Acad Dermatol, 35, 1996.Google Scholar
  5. 5.
    F Ercal, A Chawla, W V Stoecker, H-C Lee, and R H Moss. Neural network diagnosis of malignant melanoma from color images. IEEE Trans Biomed Eng, 41:837–845, 1994.CrossRefGoogle Scholar
  6. 6.
    G Pott, R Husemann, L Eckert, T Grünendick, S Lux, and P Altmeyer. Danaos - Automated skin cancer diagnosis with neural networks. In Proc 7th EADV, 1998.Google Scholar
  7. 7.
    S E Umbaugh, R H Moss, and W V Stoecker. An automatic color segmentation algorithm with application to identification of skin tumor borders. Computarized Medical Imaging and Graphics, 16:227–235, 1992.CrossRefGoogle Scholar
  8. 8.
    F Ercal, M Mognati, W V Stoecker, and R H Moss. Detection of skin tumor boundaries in color images. IEEE Trans Medical Imaging, 12:624–628, 1993.CrossRefGoogle Scholar
  9. 9.
    L Xu, M Jackowski, A Goshtasby, C Yu, D Roseman, S Bines, A Dhawan, and A Huntley. Segmentation of skin cancer images. Image and Vision Computing, 17:65–74, 1999.CrossRefGoogle Scholar
  10. 10.
    P Schmid. Lesion detection in dermatoscopic images using anisotropic diffusion and morphological flooding. In Proc Int Conf Image Processing (ICIP’99), volume 3, pages 449–453. IEEE Signal Process Soc, 1999.Google Scholar
  11. 11.
    E Claridge, P N Hall, M Keefe, and J P Allen. Shape analysis for classification of malignant melanoma. J Biomed Eng, 14:229–234, 1992.CrossRefGoogle Scholar
  12. 12.
    W V Stoecker, C S Chiang, and R H Moss. Texture in skin lesions: Comparison of three methods to determine smoothness. Computerized Medical Imaging and Graphics, 16:179–190, 1992.CrossRefGoogle Scholar
  13. 13.
    J-F Liu and J C-M Lee. An efficient and effective texture classification approach using a new notion in wavelet theory. In Proc ICPR’96, pages 820–824, 1996.Google Scholar
  14. 14.
    R Porter and N Canagarajah. Robust rotation-invariant texture classification: Wavelet, Gabor filter and GMRF based schemes. IEEE Proceedings - Vision, Image and Signal Processing, 144:188ff, 1997.Google Scholar
  15. 15.
    M Binder, H Kittler, A Seeber, A Steiner, H Pehamberger, and K Wolff. Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an ANN. Melanoma Res, 8:261–266, 1998.CrossRefGoogle Scholar
  16. 16.
    H Handels, T Ross, J Kreusch, H H Wolff, and S J Poppi. Feature selection for optimized skin tumor recognition using GAs. ArtiF Intell Med, 16:283–297, 1999.CrossRefGoogle Scholar
  17. 17.
    R A Jacobs, M I Jordan, S J Nowlan, and G E Hinton. Adaptive mixture of local experts. Neural Comput, 3:79–87, 1991.CrossRefGoogle Scholar
  18. 18.
    A P Dempster, N M Laird, and D B Rubin. Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc B, 39:1–38, 1977.MathSciNetzbMATHGoogle Scholar
  19. 19.
    M I Jordan and L Xu. Convergence results for the EM approach to mixture of experts architectures. Neural Networks, 8:1409–1431, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Martin Kreutz
    • 1
    • 2
  • Maik Anschütz
    • 1
    • 2
  • Stefan Gehlen
    • 1
    • 2
  • Thorsten Grünendick
    • 1
    • 2
  • Klaus Hoffmann
    • 3
  1. 1.Zentrum für Neuroinformatik GmbHBochumGermany
  2. 2.ZN Vision Technologies AGBochumGermany
  3. 3.Dermatologische Klinik der Ruhr-Universität Bochum im St. Josef HospitalBochumGermany

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