Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

  • Sina KhakAbi
  • Tim K. Lee
  • M. Stella Atkins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


This paper presents a novel approach to analysis and classification of skin lesions based on their growth pattern. Our method constructs a tree structure for every lesion by repeatedly subdividing the image into sub-images using color based clustering. In this method, segmentation which is a challenging task is not required. The obtained multi-scale tree structure provides a framework that allows us to extract a variety of features, based on the appearance of the tree structure or sub-images corresponding to nodes of the tree. Preliminary features (the number of nodes, leaves, and depth of the tree, and 9 compactness indices of the dark spots represented by the sub-images associated with each node of the tree) are used to train a supervised learning algorithm. Results show the strength of the method in classifying lesions into malignant and benign classes. We achieved Precision of 0.855, Recall of 0.849, and F-measure of 0.834 using 3-layer perceptron and Precision of 0.829, Recall of 0.832, and F-measure of 0.817 using AdaBoost on a dataset containing 112 malignant and 298 benign lesion dermoscopic images.


Tree Structure Dark Spot Dark Pixel Cluster Stage Preliminary Feature 
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  1. 1.
    Argenziano, G., Soyer, H.P., et al.: Interactive Atlas of Dermoscopy (Book and CD-ROM). Edra Medical Publishing and New Media (2000)Google Scholar
  2. 2.
    Argenziano, G., Soyer, H.P., et al.: Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the internet. Journal of the American Academy of Dermatology 48(5), 679–693 (2003)CrossRefGoogle Scholar
  3. 3.
    Betta, G., Di Leo, G., Fabbrocini, G., Paolillo, A., Scalvenzi, M.: Automated application of the 7-point checklist diagnosis method for skin lesions: Estimation of chromatic and shape parameters. In: IEEE Instrumentation and Measurement Technology Conference, vol. 3, pp. 1818–1822 (2005)Google Scholar
  4. 4.
    Binder, M., Schwarz, M., et al.: Epiluminescence microscopy: A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch. Dermatol. 131(3), 286–291 (1995)CrossRefGoogle Scholar
  5. 5.
    Clark, W.H., Ainsworth, A.M., Bernardino, E.A., Yang, C.H., Mihm, C.M., Reed, R.J.: The developmental biology of primary human malignant melanomas. Semin. Oncol. 2(1), 83–103 (1975)Google Scholar
  6. 6.
    Erickson, C., Driscoll, M.S.: Melanoma epidemic: Facts and controversies. Clinics in Dermatology 28(3), 281–286 (2010)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Amsterdam (2001)Google Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRefGoogle Scholar
  9. 9.
    Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D.: Dullrazor ®: A software approach to hair removal from images. Computers in Biology and Medicine 27(6), 533–543 (1997)CrossRefGoogle Scholar
  10. 10.
    Lee, T.K., McLean, D.I., Atkins, M.S.: Irregularity index: A new border irregularity measure for cutaneous melanocytic lesions. Medical Image Analysis 7(1), 47–64 (2003)CrossRefGoogle Scholar
  11. 11.
    Maglogiannis, I., Doukas, C.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Transactions on Information Technology in Biomedicine 13(5), 721–733 (2009)CrossRefGoogle Scholar
  12. 12.
    Marks, R.: Epidemiology of melanoma. Clinical and Experimental Dermatology 25(6), 459–463 (2000)CrossRefGoogle Scholar
  13. 13.
    Zhou, H., Chen, M., Zou, L., Gass, R., Ferris, L., Drogowski, L., Rehg, J.: Spatially constrained segmentation of dermoscopy images. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 800–803 (May 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sina KhakAbi
    • 1
    • 2
    • 3
  • Tim K. Lee
    • 1
    • 2
    • 3
  • M. Stella Atkins
    • 1
    • 2
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada
  2. 2.Department of Dermatology and Skin ScienceUniversity of British Columbia and Vancouver Coastal Health Research InstituteCanada
  3. 3.Cancer Control Research ProgramBC Cancer Research CentreCanada

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