Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas

  • Zhao Liu
  • Jiuai Sun
  • Lyndon Smith
  • Melvyn Smith
  • Robert Warr
Original Article


Computerised analysis on skin lesion images has been reported to be helpful in achieving objective and reproducible diagnosis of melanoma. In particular, asymmetry in shape, colour and structure reflects the irregular growth of melanin under the skin and is of great importance for diagnosing the malignancy of skin lesions. This paper proposes a novel asymmetry analysis based on a newly developed pigmentation elevation model and the global point signatures (GPSs). Specifically, the pigmentation elevation model was first constructed by computer-based analysis of dermoscopy images, for the identification of melanin and haemoglobin. Asymmetry of skin lesions was then assessed through quantifying distributions of the pigmentation elevation model using the GPSs, derived from a Laplace–Beltrami operator. This new approach allows quantifying the shape and pigmentation distributions of cutaneous lesions simultaneously. Algorithm performance was tested on 351 dermoscopy images, including 88 malignant melanomas and 263 benign naevi, employing a support vector machine (SVM) with tenfold cross-validation strategy. Competitive diagnostic results were achieved using the proposed asymmetry descriptor only, presenting 86.36 % sensitivity, 82.13 % specificity and overall 83.43 % accuracy, respectively. In addition, the proposed GPS-based asymmetry analysis enables working on dermoscopy images from different databases and is approved to be inherently robust to the external imaging variations. These advantages suggested that the proposed method has good potential for follow-up treatment.


Asymmetry analysis Computer-assisted diagnosis Cutaneous melanoma 



This study was supported by Technology Strategy Board (TSB) under the Grant Number TP/6/ICT/6/S/K1524H.


  1. 1.
    Bae Y, Nelson J, Jung B (2008) Multimodal Facial Colour Imaging Modality for Objective Analysis of Skin Lesions. J BioMed Opt 13: 064007Google Scholar
  2. 2.
    Bauer P, Cristofolinni P, Boi S (2000) Digital epiluminescence microscopy: usefulness in the differential diagnosis of cutaneous pigmentary lesion. A statistical comparison between visual and computer inspection. Melanoma Res 10:345–349PubMedCrossRefGoogle Scholar
  3. 3.
    Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Dietterich T, Becker S, Ghahramani Z (eds) Advances in neural information processing systems 14. MIT press, Vancouver, pp 585–591Google Scholar
  4. 4.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefGoogle Scholar
  5. 5.
    Binder M, Kittler H, Seeber A, Steiner A, Pehamberger H, Wolff K (1998) Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and artificial neural networks. Melanoma Res 8:261–266PubMedCrossRefGoogle Scholar
  6. 6.
    Blum A, Luedtke H, Ellwanger U, Schwabe R, Rassner G, Garbe C (2004) Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Br J Dermatol 151:1029–1038PubMedCrossRefGoogle Scholar
  7. 7.
    d’Amico M, Ferri M, Stanganelli I (2004). Qualitative asymmetry measure for melanoma detection. In: Leahy R, unser M, Fessler J (eds) The 2nd IEEE international symposium on biomedical imaging: nano to macro, IEEE service centre, Arlington, pp 1155–1158. doi: 10.1109/ISBI.2004.1398748
  8. 8.
    Dawson B, Barker J, Ellis J, Grassam E, Cotterill A, Fisher W, Feather W (1980) A theoretical and experimental study of light absorption and scattering by in vivo skin. Phys Med Biol 25:695–709PubMedCrossRefGoogle Scholar
  9. 9.
    Diepgen TL, Yihune G (2012) Dermatology information system. Accessed on 15 Feb 2012
  10. 10.
    Ehrsam E (2012) Dermoscopy. Accessed on 15 Feb 2012
  11. 11.
    Ganster H, Pinz P, Rohrer R, Wildling E, Binder M, Kittler H (2001) Automated melanoma recognition. IEEE Trans Med Imaging 20(3):233–239PubMedCrossRefGoogle Scholar
  12. 12.
    Hoffmann K, Gambichler T, Rick A, Kreutz M, Anschuetz M, Grünendick T, Orlikov A, Gehlen S, Perotti R, Andreassi L, Newton Bishop J, Césarini JP, Fischer T, Frosch PJ, Lindskov R, Mackie R, Nashan D, Sommer A, Neumann M, Ortonne JP, Bahadoran P, Penas PF, Zoras U, Altmeyer P (2003) Diagnostic and neural analysis of skin cancer (DANAOS). Br J Dermatol 149:801–819PubMedCrossRefGoogle Scholar
  13. 13.
    Iyatomi H, Oka H, Celebi ME, Hashimotob M, Hagiwara M, Tanaka M, Ogawa K (2008) An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput Med Imaging Graph 32(7):566–579PubMedCrossRefGoogle Scholar
  14. 14.
    Jeppe HC, Mads S, Zhong L, Sun C, Morten J (2010) Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion. Skin Res Technol 16(1):98–108CrossRefGoogle Scholar
  15. 15.
    Jost J (2002) Riemannian geometry and geometric analysis. Springer-Verlag, BerlinGoogle Scholar
  16. 16.
    Lee TK, Ng V, Gallagher R, Coldman A, McLean D (1997) DullRazor: a software approach to hair removal from images. Comput Biol Med 27:533–543PubMedCrossRefGoogle Scholar
  17. 17.
    Lee TK, Claridge E (2005) Predictive power of irregular border shapes for malignant melanomas. Skin Res Technol 11:1–8PubMedCrossRefGoogle Scholar
  18. 18.
    Liu Z, Sun J, Smith M, Smith L, Warr R (2012) Unsupervised sub-segmentation for pigmented skin lesions. Skin Res Technol 18(1):77–87. doi: 10.1111/j.1600-0846.2011.00534 PubMedCrossRefGoogle Scholar
  19. 19.
    Liu Z, Smith L, Sun J, Smith M, Warr R (2011) Biological indexes based reflectional asymmetry for classifying cutaneous skin lesions. In: Fichtinger G, Martel A, Peters TM (eds) 14th International conference on medical image computing and computer assisted intervention. LNCS, vol 6893. Springer, Toronto, pp 124–132Google Scholar
  20. 20.
    Menzies W, Bischof L, Talbot H, Gutenev A, Avramidis M, Wong L, Lo SK, Mackellar G, Skladnev V, McCarthy W, Kelly J, Cranney B, Lye P, Rabinovitz H, Oliviero M, Blum A, Varol A, De’Ambrosis B, McCleod R, Koga H, Grin C, Braun R, Johr R (2005) The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Arch Dermatol 141:1388–1396PubMedCrossRefGoogle Scholar
  21. 21.
    Morton CA, Mackie RM (1998) Clinical accuracy of the diagnosis of cutaneous malignant melanoma. Br J Dermatol 138(2):283–287PubMedCrossRefGoogle Scholar
  22. 22.
    Ng V, Cheung D (1997) Measuring asymmetries of skin lesions. In: Tien J, Malmborg C, Pet-Edwards J, Mollaghasemi M, Embrechts M (eds) IEEE international conference on computational cybernetics and simulation, IEEE Service Centre, Orlando, pp 4211–4216. doi: 10.1109/ICSMC.1997.637360
  23. 23.
    Piccolo D, Ferrari A, Peris K, Diadone R, Ruggeri B, Chimenti S (2002) Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study. Br J Dermatol 147:481–486PubMedCrossRefGoogle Scholar
  24. 24.
    Rustamov R (2007) Laplace–Beltrami eigenfunctions for deformation invariant shape representation. In: Belyawv A, Garland M (eds) Eurographics symposium on geometry processing. Eurographics Association, Aire-la-Ville, pp 225–233Google Scholar
  25. 25.
    Sboner A, Eccher C, Blanzieri E, Bauer P, Cristofolini M, Zumiani G, Forti S (2003) A multiple classifier system for early melanoma diagnosis. Artif Intell Med 27(1):29–44PubMedCrossRefGoogle Scholar
  26. 26.
    Schmid P, Guillod J, Thiran J (2003) Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 27:65–78CrossRefGoogle Scholar
  27. 27.
    Seidenari S, Pelacani G, Grana C (2006) Asymmetry in dermoscopic melanocytic lesion images: a computer description based on colour distribution. Acta Derm Venereol 86:123–128PubMedGoogle Scholar
  28. 28.
    Seidenari S, Pellacani G, Grana C (2003) Computer description of colours in dermoscopic melanocytic lesion images reproducing clinical assessment. Br J Dermatol 149:523–529PubMedCrossRefGoogle Scholar
  29. 29.
    Stoecker W, Li W, Moss R (1992) Automatic detection of asymmetry in skin tumors. Comput Med Imaging Graph 16:191–197PubMedCrossRefGoogle Scholar
  30. 30.
    Stolz W, Braun-Falco O, Landthaler M, Bilek P, Cognetta A (2002) Color atlas of dermatoscopy, 2nd edn. Blackwell, BerlinGoogle Scholar
  31. 31.
    Tenenhaus A, Nkengne A, Horn JF, Serruys C, Giron A, Fertil B (2010) Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions. Skin Res Technol 16:85–97PubMedCrossRefGoogle Scholar
  32. 32.
    Tomasi C, Manduchi R (1998) Bilateral Filtering for Gray and Color Images. In: Davis L, Zisserman A, Yachida M, Narasimhan R (eds) The 6th IEEE international conference on computer vision, IEEE Computer Society, Bombay, pp 839–846. doi: 10.1109/ICCV.1998.710815
  33. 33.
    Trefethen LN, David B (1997) Numerical linear algebra. Society for Industrial and Applied Mathematics, SIAM, Philadelphia, p 258Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  • Zhao Liu
    • 1
  • Jiuai Sun
    • 1
  • Lyndon Smith
    • 1
  • Melvyn Smith
    • 1
  • Robert Warr
    • 2
  1. 1.Machine Vision LabUniversity of the West of EnglandBristolUK
  2. 2.Plastic SurgeryFrenchay Hospital, NHS TrustBristolUK

Personalised recommendations