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Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images

  • Karol KropidlowskiEmail author
  • Marcin Kociolek
  • Michal Strzelecki
  • Dariusz Czubinski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9972)

Abstract

There is no suitable standard for the detection of blue whitish veil atypical vascular pattern and regression structures applied to skin lesion images. This information however is important in assessment of melanoma in skin dermatoscopic images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria required by dermatologists. In this paper the application of color based image features for detection of blue whitish veil and atypical vas-cular pattern is presented. Preliminary test results are promising; for analyzed melanoma images the accuracy of developed methods provides 78 % correctly detected blue whitish veils, 84 % correctly detected atypical vascular pattern, and 86,5 % correctly detected regression structures. This paper is a contribution to the computer aided diagnostic system implementing the ELM 7-point check-list aimed at melanoma detection.

Keywords

Malignant melanoma Image processing Blue whitish veil Typical and atypical vascular pattern 7-point checklist Regression structures Color based classification 

References

  1. 1.
    Jablonska, S., Chorzelski, T.: Choroby Skory, 5th edn. PZWL, Warszawa (2001)Google Scholar
  2. 2.
    LeDuc, T.: World Life Expectancy. www.worldlifeexpectancy.com
  3. 3.
    Betta, G., Di Leo, G., Fabbrocini, G., Paolillo, A., Sommella, P.: Dermoscopic image-analysis system: estimation of atypical pigment network and atypical vascular pattern. In: IEEE International Workshop on Medical Measurement and Applications, MeMeA 2006, vol. 2006, pp. 63–67, April 2006Google Scholar
  4. 4.
    Di Leo, G., Paolillo, A., Sommella, P., Fabbrocini, G., Rescigno, O.: A software tool for the diagnosis of melanomas. In: IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (2010)Google Scholar
  5. 5.
    Janowski, P., Strzelecki, M., Brzezinska-Blaszczyk, E., Zalewska, A.: Computer analysis of normal and basal cell carcinoma mast cells. Med. Sci. Monitor 7(2), 260–265 (2001)Google Scholar
  6. 6.
    Sathiya, S.B., Kumar, S.S., Prabin, M.: A survey on recent computer-aided diagnosis of Melanoma. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, pp. 1387–1392 (2014)Google Scholar
  7. 7.
    Jaworek-Korjakowska, J., Tadeusiewicz, R.: Determination of border irregularity in dermoscopic color images of pigmented skin lesions. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6459–6462 (2014)Google Scholar
  8. 8.
    Grzymala-Busse, P., Grzymala-Busse, J. W., Hippe, Z.S.: Melanoma prediction using data mining system LERS. In: 25th Annual International Computer Software and Applications Conference, COMPSAC 2001, pp. 615–620 (2001)Google Scholar
  9. 9.
    Smaoui, N.: A developed system for melanoma diagnosis. Int. J. Comput. Vis. Signal Process. 3(1), 10–17 (2013)Google Scholar
  10. 10.
    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: 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings, vol. 3, pp. 17–19, May 2005Google Scholar
  11. 11.
    Di Leo, G., Paolillo, A., Sommella, P., Fabbrocini, G.: Automatic diagnosis of melanoma: a software system based on the 7-point check-list. In: Proceedings of Annual Hawaii International Conference on System Science, pp. 1–10 (2010)Google Scholar
  12. 12.
    Kropidłowski, K., Kociołek, M., Strzelecki, M., Czubiński, D.: Model based approach for melanoma segmentation. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 347–355. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11331-9_42 Google Scholar
  13. 13.
    Kropidlowski, K., Kociolek, M., Strzelecki, M., Czubinski, D.: Nevus atypical pigment network distinction and irregular streaks detection in skin lesions images. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, pp. 66–70 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Karol Kropidlowski
    • 1
    Email author
  • Marcin Kociolek
    • 1
  • Michal Strzelecki
    • 1
  • Dariusz Czubinski
    • 2
  1. 1.Institute of ElectronicsŁódź University of TechnologyŁódźPoland
  2. 2.DerMed Training CenterŁódźPoland

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