Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysis

  • Maria João M. Vasconcelos
  • Luís Rosado
  • Márcia Ferreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)


Skin cancer is the most common of all cancer types and Malignant Melanoma is the most dangerous form of it, thus prevention is vital. Risk assessment of skins lesions is usually done through the ABCD rule (asymmetry, border, color and differential structures) that classifies the lesion as benign, suspicious or highly suspicious of Malignant Melanoma. A methodology to assess the asymmetry of a skin lesion image in relation to each axis of inertia, for both dermoscopic and mobile acquired images, is presented. It starts by extracting a set of 310 of asymmetry features, followed by testing several feature selection and machine learning classification methods in order to minimize the classification error. For dermoscopic images, the developed methodology achieves an accuracy of 87% regarding asymmetry classification while, for mobile acquired images the accuracy reaches 73.1%.


Support Vector Machine Feature Selection Discrete Fourier Transform Support Vector Machine Classifier Feature Selection Method 
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.


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  1. 1.
    Rajpara, S., Botello, A., Townend, J., Ormerod, A.: Systematic review of dermoscopy and digital dermoscopy/artificial intelligence for the diagnosis of melanoma. British Journal of Dermatology 161, 591–604 (2009)CrossRefGoogle Scholar
  2. 2.
    Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artificial Intelligence in Medicine 56, 69–90 (2012)CrossRefGoogle Scholar
  3. 3.
    Masood, A., Ali Al-Jumaily, A.: Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms. International Journal of Biomedical Imaging (2013)Google Scholar
  4. 4.
    Stolz, W., Riemann, A., Cognetta, A., Pillet, L., Abmayr, W., Holzel, D., Bilek, P., Nachbar, F., Landthaler, M.: Abcd rule of dermatoscopy-a new practical method for early recognition of malignant-melanoma. European Journal of Dermatology 4, 521–527 (1994)Google Scholar
  5. 5.
    Argenziano, G., Soyer, H.P., De Giorgio, V., Piccolo, D., Carli, P., Delno, M., Ferrari, A., Hofmann-Wellenhof, R., Massi, D., Mazzocchetti, G., et al.: Interactive atlas of dermoscopy (2000)Google Scholar
  6. 6.
    Mendonça, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: Ph 2-a dermoscopic image database for research and benchmarking. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5437–5440. IEEE (2013)Google Scholar
  7. 7.
  8. 8.
    Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 31, 362–373 (2007)CrossRefGoogle Scholar
  9. 9.
    Cavalcanti, P.G., Scharcanski, J.: Macroscopic pigmented skin lesion segmentation and its inuence on lesion classification and diagnosis. In: Color Medical Image Analysis, pp. 15–39. Springer (2013)Google Scholar
  10. 10.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 610–621 (1973)Google Scholar
  11. 11.
    Chang, W.Y., Huang, A., Yang, C.Y., Lee, C.H., Chen, Y.C., Wu, T.Y., Chen, G.S.: Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PloS One 8, e76212 (2013)Google Scholar
  12. 12.
    Gonzalez, R.C., Woods, R.E.: Digital image processing, pp. 132–134. Prentice Hall (2002)Google Scholar
  13. 13.
    Aswin, R.B., Jaleel, J.A., Salim, S.: Implementation of ann classifier using matlab for skin cancer detection. International Journal of Computer Science and Mobile Computing. ICMIC, 87–94 (2013)Google Scholar
  14. 14.
    Cheerla, N., Frazier, D.: Automatic melanoma detection using multi-stage neural networks. International Journal of Innovative Research in Science, Engineering and Technology 3, 9164–9183 (2014)Google Scholar
  15. 15.
    Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15, 1437–1447 (2003)CrossRefGoogle Scholar
  16. 16.
    Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clus-tering. IEEE Transactions on Knowledge and Data Engineering 17, 491–502 (2005)CrossRefGoogle Scholar
  17. 17.
    Alcon, J.F., Ciuhu, C., Ten Kate, W., Heinrich, A., Uzunbajakava, N., Krekels, G., Siem, D., De Haan, G.: Automatic imaging system with decision support for inspection of pig-mented skin lesions and melanoma diagnosis. IEEE Journal of Selected Topics in Signal Processing 3, 14–25 (2009)CrossRefGoogle Scholar
  18. 18.
    Scharcanski, J., Celebi, M.E.: Computer vision techniques for the diagnosis of skin cancer (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria João M. Vasconcelos
    • 1
  • Luís Rosado
    • 1
  • Márcia Ferreira
    • 2
  1. 1.Fraunhofer Portugal AICOSPortoPortugal
  2. 2.Portuguese Institute of OncologyPortoPortugal

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