Detecting and Measuring Surface Area of Skin Lesions

  • Houman Mirzaalian-Dastjerdi
  • Dominique Töpfer
  • Michael Bangemann
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. Fully manual measurements and documentation of the healing process can be very cumbersome and imprecise. Existing technical solutions often require the user to delineate the lesion manually and rarely provide information on measurement precision or accuracy. We propose a method for segmenting and measuring lesions using a single image. Surface area of lesions on bent surfaces is estimated based on a paper ruler. Only roughly outlining the region of interest is required. Wound segmention evaluation was performed on 10 images, resulting in an accuracy of 0.98 ± 0.02. For surface measuring evaluation on 40 phantom images we found an absolute error of 0.32 ± 0.27 cm2 and a relative error of 5.2 ± 4.3%.


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  1. 1.
    Foltynski P, Ladyzynski P, Wojcicki JM. A new smartphone-based method for wound area measurement. Artific Organs. 2014;38(4):346–352.Google Scholar
  2. 2.
    Liu X, Kim W, Schmidt R, et al. Wound measurement by curvature maps: a feasibility study. Phys Measure. 2006;27(11):1107.Google Scholar
  3. 3.
    Treuillet S, Albouy B, Lucas Y. Three-dimensional assessment of skin wounds using a standard digital camera. IEEE Trans Med Imaging. 2009;28(5):752–762.Google Scholar
  4. 4.
    Wannous H, Lucas Y, Treuillet S. Enhanced assessment of the wound-healing process by accurate multiview tissue classification. IEEE Trans Med Imaging. 2011;30(2):315–326.Google Scholar
  5. 5.
    Grady L. Random walks for image segmentation. IEEE Trans Pattern Anal Machine Intell. 2006;28(11):1768–1783.Google Scholar
  6. 6.
    Shi L, Funt B, Hamarneh G. Quaternion color curvature. In: Color and Imaging Conference. vol. 2008. Society for Imaging Science and Technology; 2008. p. 338–341.Google Scholar
  7. 7.
    Baghaie A, Yu Z. Structure tensor based image interpolation method. AEU Int J Electronic Comm. 2015;69(2):515–522.Google Scholar
  8. 8.
    Jagannathan L, Jawahar C. Perspective correction methods for camera based document analysis. In: Proc. First Int. Workshop on Camera-based Document Analysis and Recognition; 2005. p. 148–154.Google Scholar
  9. 9.
    Lei G. Recognition of planar objects in 3-D space from single perspective views using cross ratio. IEEE Trans Robotic Automat. 1990;6(4):432–437.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Houman Mirzaalian-Dastjerdi
    • 1
    • 2
  • Dominique Töpfer
    • 2
  • Michael Bangemann
    • 3
  • Andreas Maier
    • 1
  1. 1.Department of Computer Science 5University of Erlangen-NürnbergErlangenDeutschland
  2. 2.Softgate GmbHErlangenDeutschland
  3. 3.Praxisnetz Nürnberg Süd e.V.NürnbergDeutschland

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