Advertisement

Severity of Cellulite Classification Based on Tissue Thermal Imagining

  • Jacek MazurkiewiczEmail author
  • Joanna Bauer
  • Michal Mosion
  • Agnieszka Migasiewicz
  • Halina Podbielska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

In this article we present a novel approach to cellulite classification that can be personlised based on non-contact thermal imaging using IR thermography. By analysing the superficial temperature distribution of the body it is possible to diagnose the stages of cellulite development. The study investigates thermal images of posterior of thighs of female volunteers and identifies cellulite areas in an automatic way using image processing. The Growing Bubble Algorithm has been used for thermal picture conversion into valid input vector for a neural network based classifier scheme. Using machine learning process of training the input database was prepared as the stage of cellulite classifier according to the state of the art Nürnberger-Müller diagnosis scheme. Our work demonstrates that it is possible to diagnose the cellulite with over 70% accuracy using a cost-effective, simple and unsophisticated classifier which operates on low-definition pictures. In essence, our work shows that IR-thermography, when coupled with computer aided image analysis and processing, can be a very convenient and effective tool to enable personalized diagnosis and preventive medicine to improve the quality of life of women suffering from cellulite problems.

Keywords

Thermal imaging Infrared thermography Cellulite MLP Image processing 

References

  1. 1.
    Avram, M.M.: Cellulite: a review of its physiology and treatment. J. Cosmet. Laset. Ther. 6(4), 181–185 (2004)CrossRefGoogle Scholar
  2. 2.
    Bauer, J., Deren, E.: Standardization of infrared thermal imaging in medicine and physiotherapy. Acta. Bio. Opt. Inform. Med. 20(1), 11–20 (2014)Google Scholar
  3. 3.
    Cellulite Statistics (2006). http://www.worldvillage.com/cellulite-statistics/. Accessed 3 July 2017
  4. 4.
    Emanuele, E.: A multilocus candidate approach identifies ACE and HIF1A as susceptibility genes for cellulite. J. Eur. Acad. Dermatol. Venereol. 24(8), 930–935 (2010)CrossRefGoogle Scholar
  5. 5.
    Faundez-Zanuy, M., Mekyska, J., Espinosa-Duro, V.: On the focusing of thermal images. Pattern Recogn. Lett. 32(11), 1548–1557 (2011)CrossRefGoogle Scholar
  6. 6.
    Galazka, M., Galeba, A., Nurein, H.: Cellulite as a medical and aesthetic problem - etiopathogenesis, symptoms, diagnosis and treatment. Hygeia Public Health 49(3), 425–430 (2014)Google Scholar
  7. 7.
    Goldman, M.P., Hexsel, D.: Cellulite Pathophysiology and Treatment. CRC Press, Boca Raton (2010)CrossRefGoogle Scholar
  8. 8.
    Janda, K., Tomikowska, A.: Cellulite - causes, prevention. Treatment. Ann. Acad. Med. Stettin. 60(1), 29–38 (2014)Google Scholar
  9. 9.
    Jankowski, M., Mazurkiewicz, J.: Road surface recognition system based on its picture. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7894, pp. 548–558. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38658-9_50CrossRefGoogle Scholar
  10. 10.
    Junqueira, J.P., Alfonso, M., de Mello Tucunduva, T.C., Bussamara Pinheiro, M.V., Bagatin, E.: Cellulite: a review. Surg. Cosmet. Dermatol. 2(3), 214–219 (2010)Google Scholar
  11. 11.
    Nürnberger, F., Müller, G.: So-called cellulite - an invented disease. J. Dermatol. Surg. Oncol. 4(3), 221–229 (1978)CrossRefGoogle Scholar
  12. 12.
    Ring, F.: The historical development of thermometry and thermal imaging in medicine. J. Med. Eng. Technol. 30(4), 192–198 (2006)CrossRefGoogle Scholar
  13. 13.
    Toet, A.: Natural colour mapping for multiband nightvision imagery. Inf. Fusion 4(3), 155–166 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jacek Mazurkiewicz
    • 1
    Email author
  • Joanna Bauer
    • 2
  • Michal Mosion
    • 3
  • Agnieszka Migasiewicz
    • 4
  • Halina Podbielska
    • 2
  1. 1.Department of Computer Engineering, Faculty of ElectronicsWroclaw University of Science and TechnologyWroclawPoland
  2. 2.Department of Biomedical Engineering, Faculty of Fundamental Problems of TechnologyWroclaw University of Science and TechnologyWroclawPoland
  3. 3.Comarch S.A.WroclawPoland
  4. 4.Department of Cosmetology, Faculty of PhysiotherapyWroclaw University School of Physical EducationWroclawPoland

Personalised recommendations