A colour image processing method for melanoma detection

  • O. Colot
  • R. Devinoy
  • A. Sombo
  • D. de Brucq
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


In this paper, we propose a method dedicated to classification between benign and malignant lesions in Dermatology in the aim to help the clinicians for melanoma diagnosis.

The proposed methodology reduces the very numerous informations contained in the digitized images to a finite set of parameters giving a description of the colour and the shape of the lesions.

The whole process is shared in three steps: preprocessing, segmentation and classification of the lesions.

The proposed method was applied on a data base of 38 lesions (20 benign lesions and 18 malignant lesions) in the aim to assess the feasability of the proposed method. The good classification rate obtained with the method is discussed and later tests to engage are underlined.


False Alarm Benign Lesion Malignant Lesion Gravity Center Segmentation Step 
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.


  1. 1.
    Friedman, R.J.: Early Detection of Melanoma: the Role of Physician Examination and Self Examination of the Skin. CA 35 (1985)130–151.PubMedGoogle Scholar
  2. 2.
    Golston, J.E., Moss, R.H., Stoecker, V.: Boundary Detection in Skin Tumor Images: an Overall Approach and a Radial Search Algorithm. Pattern Recognition. 23 (1990) 1235–1247.CrossRefGoogle Scholar
  3. 3.
    Cascinelli, N., Ferrario, M., Bufalino, R.: Results obtained by using a Computerized Image Analysis System designed as an Aid in Diagnosis of Cutaneous Melanoma. Melanoma Res. 2 (1992) 163–170.CrossRefPubMedGoogle Scholar
  4. 4.
    Umbaugh, S.E., Moss, R.H., Stoecker, V.: An automatic color segmentation algorithm with application to identification of skin tumor borders. Comp. Med. Imag. Graph. 16 (1992) 227–236.CrossRefGoogle Scholar
  5. 5.
    Scott, E.: Automatic Color Segmentation Algorithms with Application to Skin Tumor Feature Identification. IEEE Engineering in Medicine and Biology. (1993) 75–82.Google Scholar
  6. 6.
    Schindewolf, T., Stolz, W., Albert, R., Abmayr, W., Harms, H.: Classification of melanocytic lesions with color and texture analysis using digital image processing. Anal. Quant. Cytol. Histol, 15 (1993) 1–11.PubMedGoogle Scholar
  7. 7.
    Schindewolf T., Schiffner, R., Stolz, et al.: Evaluation of different image acquisition techniques for computer vision system in the diagnosis of malignant melanoma. J. Am. Acad. Dermatol. 31–1 (1994) 33–41.CrossRefGoogle Scholar
  8. 8.
    Hall, P.N., Claridge, E., Morris Smith, J.D.: Computer screening for early detection of melanoma — is there a future? British Jal of Derm. 132 (1995) 325–338.CrossRefGoogle Scholar
  9. 9.
    Colot O., Joly, P., Taouil, K., et al.: Analysis by means of image processing of benign and malignant melanocytic lesions. European Journal of Dermatology. 5 (1995) 441.Google Scholar
  10. 10.
    de Brucq D., Taouil, K., Colot, O., et al.: Segmentation d’images et extraction de contours pour l’analyse de lésions dermatologiques. Proc. Of the 15th Colloque GRETSI, Juan-les-Pins, France (1995) 1205–1208.Google Scholar
  11. 11.
    Kapur, J.N.: A new method for gray-level picture thresholding using entropy of the histogram. CVGIP 29 (1985) 273–285.Google Scholar
  12. 12.
    Hopcroft, J.E., Ullman, J.D.: Introduction to automata theory, languages and computation. Addison Wesley (1979).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • O. Colot
    • 1
  • R. Devinoy
    • 2
  • A. Sombo
    • 2
  • D. de Brucq
    • 2
  1. 1.PSI-LCIAINSA de RouenMont-Saint-Aignan CédexFrance
  2. 2.PSI-La3IUniversité RouenMont-Saint-Aignan CédexFrance

Personalised recommendations