Color Detection in Dermoscopy Images Based on Scarce Annotations

  • Catarina BarataEmail author
  • M. Emre Celebi
  • Jorge S. Marques
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


Dermatologists often prefer clinically oriented Computer Aided Diagnosis (CAD) Systems. However, the development of such systems is not straightforward due to lack of detailed image annotations (medical labels and segmentation of their corresponding regions). Most of the times we only have access to medical labels that are not sufficient to learn proper models. In this study, we address this issue using the Correspondence-LDA algorithm. The algorithm is applied with success to the identification identification of relevant colors in dermoscopy images, obtaining a precision of 82.1 % and a recall of 90.4 %.


Melanoma diagnosis Correspondence-LDA Image annotation Color detection 



This work was funded by grant SFRH/BD/84658/2012 and by the FCT project FCT [UID/EEA/50009/2013].


  1. 1.
    Dreiseitl, S., Binder, M.: Do physicians value decision support? a look at the effect of decision support systems on physician opinion. Artif. Intel. Med. 33(1), 25–30 (2005)CrossRefGoogle Scholar
  2. 2.
    Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intel. Med. 56(2), 69–90 (2012)CrossRefGoogle Scholar
  3. 3.
    Argenziano, G., Soyer, H.P., De Giorgi, V., et al.: Interactive atlas of dermoscopy. In: EDRA (2000)Google Scholar
  4. 4.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Seidenari, S., Pellacani, G., Grana, C.: Computer description of colours in dermoscopic melanocytic lesion images reproducing clinical assessment. Br. J. Dermatol. 149(3), 523–529 (2003)CrossRefGoogle Scholar
  6. 6.
    Barata, C., Figueiredo, M.A.T., Celebi, M.E., Marques, J.S.: Color identification in dermoscopy images using gaussian mixture models. In: ICASSP 2014, pp. 3611–3615 (2014)Google Scholar
  7. 7.
    Blei, D., Jordan, M.: Modeling annotated data. In: 26th ACM SIGIR, pp. 127–134. ACM (2003)Google Scholar
  8. 8.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR 2005, vol. 2, pp. 524–531. IEEE (2005)Google Scholar
  9. 9.
    Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183–233 (1999)zbMATHCrossRefGoogle Scholar
  10. 10.
    Sra, S.: A short note on parameter approximation for von mises-fisher distributions: and a fast implementation of \({I}_{s}(x)\). Comput. Stat. 27(1), 177–190 (2012)zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Catarina Barata
    • 1
    Email author
  • M. Emre Celebi
    • 2
  • Jorge S. Marques
    • 1
  1. 1.Institute for Systems and RoboticsInstituto Superior TécnicoLisboaPortugal
  2. 2.Department of Computer ScienceLouisiana State UniversityShreveportUSA

Personalised recommendations