Model Based Approach for Melanoma Segmentation

  • Karol Kropidłowski
  • Marcin Kociołek
  • Michał Strzelecki
  • Dariusz Czubiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8671)


is no suitable golden standard for assessment and comparison of segmentation methods applied to skin lesions images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria defined by dermatologists. We present a model based approach for melanocytic image segmentation as a tool to improve computer aided diagnosis. During the research it was necessary to correct non-uniform image illumination caused by dermatoscope lightning. The correction algorithm based on dermatoscope light intensity estimation was used. The proposed segmentation method is based on histogram skin modeling. Preliminary test results are promising, for the analyzed melanoma images mean Jaccard index of 89.48% and mean sensitivity of 92.45% were obtained (when compared to expert assessment).


Segmentation Method Segmentation Result Jaccard Index Healthy Skin Image Analysis Technique 
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.
    Jablonska, S., Chorzelski, T.: Choroby skory, Wydawnictwo Lekarskie, Warsaw (in Polish 2004)Google Scholar
  2. 2.
    Strzelecki, M., Szczypinski, P., Materka, A., Klepaczko, A.: A software tool for automatic classification and segmentation of 2D/3D medical images. Nuclear Instruments & Methods In Physics Research A 702, 137–140 (2013)CrossRefGoogle Scholar
  3. 3.
    Silveira, M., Nascimento, J.C., Marques, J.S., Maral, A.R.S., Mendona, T., Yamauchi, S., Maeda, J., Rozeira, J.: Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images. IEEE Journal of Selected Topics in Signal Processing 3(1), 35–45 (2009)CrossRefGoogle Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson International EditionGoogle Scholar
  5. 5.
    Beuren, A.T., Pinheiro, R.J.G., Facon, J.: Color approach of melanoma lesion segmentation. In: IWSSIP 2012, Vienna, Austria, April 11-13, pp. 284–287 (2012) ISBN 978-3-200-02328-4Google Scholar
  6. 6.
    Norton, K.-A., Iyatomi, H., Celebi, M.E., Ishizaki, S., Sawada, M., Suzaki, R., Kobayashi, K., Tanaka, M., Ogawa, K.: Three-phase general border detection method for dermoscopy images using non-uniform illumination correction. Skin Research and Technology 18, 290–300 (2012), doi:10.1111/j.1600-0846.2011.00569.xCrossRefGoogle Scholar
  7. 7.
    Garnavi, R., Aldeen, M., Celebi, M.E., Varigos, G., Finch, S.: Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Computerized Medical Imaging and Graphics 35, 105–115 (2011)CrossRefGoogle Scholar
  8. 8.
    Celebi, M.E., Kingravi, H.A., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H., Malters, J.M., Grichnik, J.M., Marghoob, A.A., Rabinovitz, H.S., Menzies, S.W.: Border detection in dermoscopy images using statistical region Mering. Skin Research and Technology 14, 347–353 (2008)CrossRefGoogle Scholar
  9. 9.
    Zhou, H., Chen, M., Gass, R., Ferris, L., Drogowski, L., Rehg, J.M.: Spatially constrained segmentation of dermoscopy images. In: The 5th IEEE International Symposium on ISBI 2008, pp. 800–803 (2008)Google Scholar
  10. 10.
    Celebi, M.E., Aslandogan, Y.A., Stoecker, W.V., Iyatomi, H., Oka, H., Chen, X.: Unsupervised border detection in dermoscopy images. Skin Research and Technology 13, 454–462 (2007)CrossRefGoogle Scholar
  11. 11.
    Iyatomi, H., Oka, H., Saito, M., Miyake, A., Kimoto, M., Yamagami, J., Kobayashi, S., Tanikawa, A., Hagiwara, M., Ogawa, K., Argenziano, G., Soyer, H.P., Tanaka, M.: Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer - based extraction methods for an automatic melanoma diagnostic system. Melanoma Research 16, 183–190 (2006)CrossRefGoogle Scholar
  12. 12.
    Chalfoun, J., Kociolek, M., Dima, A., Halter, M., Cardone, A., Peskin, A., Bajcsy, P., Brady, M.: Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells. Journal of Microscopy 249(1), 41–52 (2013)CrossRefGoogle Scholar
  13. 13.
    Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)Google Scholar
  15. 15.
    Smaoui, N., Bessassi, S.: A developed system for melanoma diagnosis. International Journal of Computer Vision and Signal Processing 3(1), 10–17 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karol Kropidłowski
    • 1
  • Marcin Kociołek
    • 1
  • Michał Strzelecki
    • 1
  • Dariusz Czubiński
    • 2
  1. 1.Institute of ElectronicsŁódź University of TechnologyŁódźPoland
  2. 2.DerMed Training CenterŁódźPoland

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