Cancerous Lesion Detection from Nevoscope Skin Surface Images via Parametric Color Clustering

  • Nikhil J. Dhinagar
  • Ivan Glasgo
  • Mehmet Celenk
  • Mehmet A. Akinlar
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


This paper describes a new approach to analyze the spectral information of the samples of skin tissue that are localized in the spatial plane of microscopic image for discrimination of three different skin cancerous lesion prognoses. First, a cancerous lesion image is segmented from the skin surface based on Otsu’s optimal histogram thresholding technique. This allows us to localize the abnormal area in the skin tissue that is affected most as compared to the surrounding cells that appear brighter in color. Color clusters of the segmented darker lesions are used to obtain the three-dimensional (3D) spectral distribution function in the (R, G, B) color space. The Maximum Likelihood (ML) parameter estimation is utilized for calculation of the mean vector and co-variance matrix of the Gaussian (or normal) density approximation of skin samples and with the Mahalonobis distance as similarity measure in the learning and the testing phases of the pattern recognition system.


Skin cancer lesion detection Maximum Likelihood (ML) parameter estimation Mahalonobis distance classifier Color Clustering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Housman, T.S., et al.: Skin cancer is among the most costly of all cancers to treat for the Medicare population. J. Am. Acad. Dermatol. 48(3), 425–429 (2003)CrossRefGoogle Scholar
  2. 2.
    Dhinagar, N.J., Celenk, M., Akinlar, M.: Noninvasive screening and discrimination of skin images for early melanoma detection. In: IEEE iCBBE 2011, Wuhan, China (2011)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Dhinagar, N.J., Celenk, M.: Non-invasive detection and classification of skin cancer from visual and cross-sectional images. In: IEEE ISABEL 2011, Barcelona, Spain (2011)Google Scholar
  6. 6.
    Carpenter, P.M., Linden, K.G., McLaren, C.E.: Nuclear morphometry and molecular biomarkers of actinic keratosis, sun-damaged and nonexposed skin. Cancer Epdemiol. Biomarkers 13, 1996–2002 (2004)Google Scholar
  7. 7.
    Korde, V.R., et al.: Using optical coherence tomography to evaluate skin sun damage and precancer. Lasers in Surgery and Medicine 39, 687–695 (2007)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Harland, C.C., et al.: Differentiation of common benign pigmented skin lesions from melanoma by high-resolution ultrasound. British J. of Derm. 143, 281–289 (2000)CrossRefGoogle Scholar
  10. 10.
    Taheri, S.: Decision support system – Skin cancer. Canadian J. on Image Processing and Computer Vision 2(2), 12–15 (2011)Google Scholar
  11. 11.
    Ballerini, L., Li, X., Fisher, R.B., Aldridge, B., Rees, J.: Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 312–319. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Schwartz, W.R., Pedrini, H.: Color Textured Image Segmentation Based on Spatial Dependence Using 3D Co-occurrence Matrices and Markov Random Fields. In: 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic, pp. 81–87 (2007)Google Scholar
  13. 13.
    Kato, Z., Pong, T.C., Song, G.Q.: Unsupervised segmentation of color textured images using a multi-layer MRF model. In: Proc. Int. Conf. Image Processing, Barcelona, Spain, pp. 961–964 (2003)Google Scholar
  14. 14.
    Hernandez, O.J., Khotanzad, A.: Color Image Segmentation Using Multispectral Random Field Texture Model & Color Content Features. JCS&T 4(3) (2004)Google Scholar
  15. 15.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 9, 62–66 (1997)Google Scholar
  16. 16.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. 1 (1992)Google Scholar
  17. 17.
  18. 18.
  19. 19.
    Celenk, M., Conley, T., Willis, J., Graham, J.: Predictive network anomaly detection and visualization. IEEE Trans. on Info. For. and Sec. 5(2), 288–299 (2010)CrossRefGoogle Scholar
  20. 20.
    Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  21. 21.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2009)Google Scholar
  22. 22.
    Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice Hall (1990)Google Scholar
  23. 23.
    Dhinagar, N.J., Celenk, M.: Power spectra based classification of cancerous nevoscope skin images. In: IEEE ICCAIE 2011, Penang, Malaysia (2011)Google Scholar
  24. 24.
  25. 25.
    Harland, C.C., Kale, S.G., Jackson, P., Mortimer, P.S., Bamber, J.C.: Differentiation of common benign pigmented skin lesions from melanoma by high-resolution ultrasound. British Journal of Dermatology 143, 281–289 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikhil J. Dhinagar
    • 1
  • Ivan Glasgo
    • 1
  • Mehmet Celenk
    • 1
  • Mehmet A. Akinlar
    • 2
  1. 1.School of EECSOhio UniversityAthensUSA
  2. 2.Department of MathematicsBilecik UniversityBilecikTurkey

Personalised recommendations