Advertisement

Skin Lesions Image Analysis Utilizing Smartphones and Cloud Platforms

  • Charalampos Doukas
  • Paris Stagkopoulos
  • Ilias MaglogiannisEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1256)

Abstract

This chapter presents the state of the art on mobile teledermoscopy applications, utilizing smartphones able to store digital images of skin areas depicting regions of interest (lesions) and perform self-assessment or communicate the captured images with expert physicians. Mobile teledermoscopy systems consist of a mobile application that can acquire and identify moles in skin images and classify them according their severity and Cloud infrastructure exploiting computational and storage resources. The chapter presents some indicative mobile applications for skin lesions assessment and describes a proposed system developed by our team that can perform skin lesion evaluation both on the phone and on the Cloud, depending on the network availability.

Key words

Image analysis Skin lesions Skin cancer Melanoma Mobile dermoscopy Mobile computing Teledermatology Cloud infrastructures Android iOS Smartphones 

References

  1. 1.
    Marks R (2000) Epidemiology of melanoma. Clin Exp Dermatol 25:459–463CrossRefGoogle Scholar
  2. 2.
    Ultraviolet radiation and the INTERSUN Programme, source: World Health Organization. http://www.who.int/uv/faq/skincancer/en/. Accessed 31 Aug 2013
  3. 3.
    Stern RS (2010) Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol 146(3):279–282CrossRefGoogle Scholar
  4. 4.
    Rogers HW, Weinstock MA, Harris AR et al (2010) Incidence estimate of nonmelanoma skin cancer in the United States, 2006. Arch Dermatol 146(3):283–287CrossRefGoogle Scholar
  5. 5.
    Reed KB, Brewer JD, Lohse CM, Bringe KE, Pruit CN, Gibson LE (2012) Increasing incidence of melanoma among young adults: an epidemiological study in Olmsted County, Minnesota. Mayo Clin Proc 87(4):328–334CrossRefGoogle Scholar
  6. 6.
    American Cancer Society. Cancer facts & figures 2013. http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-036845.pdf. Accessed 31 Aug 2013
  7. 7.
    Pariser RJ, Pariser DM (1987) Primary care physicians errors in handling cutaneous disorders. J Am Acad Dermatol 17:239–245CrossRefGoogle Scholar
  8. 8.
    Maglogiannis I, Doukas C (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed 13(5):721–733CrossRefGoogle Scholar
  9. 9.
    Argenziano G et al (2003) Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J Am Acad Dermatol 48(5):680–693CrossRefGoogle Scholar
  10. 10.
    Argenziano G, Fabbrocini G, Carli P, De Giorgi V, Sammarco E, Delfino M (1998) Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch Dermatol 134(12):1563–1570CrossRefGoogle Scholar
  11. 11.
    Betta G, Di Leo G, Fabbrocini G, Paolillo A, Scalvenzi M (2005) Automated application of the “7-point checklist” diagnosis method for skin lesions: estimation of chromatic and shape parameters. In: Proceedings of the IEEE instrumentation and measurement technology conference (IMTC ‘05), May 2005, pp 1818–1822Google Scholar
  12. 12.
    Maglogiannis I (2003) Automated segmentation and registration of dermatological images. J Math Model Algorithm 2:277–294CrossRefGoogle Scholar
  13. 13.
    Maglogiannis I, Pavlopoulos S, Koutsouris D (2005) An integrated computer supported acquisition, handling and characterization system for pigmented skin lesions in dermatological images. IEEE Trans Inf Technol Biomed 9(1):86–98CrossRefGoogle Scholar
  14. 14.
    Massone C, Brunasso AMG, Campbell TM, Peter Soyer H (2009) Mobile teledermoscopy: melanoma diagnosis by one click? Semin Cutan Med Surg 28(3):203–205, ISSN 1085-5629CrossRefGoogle Scholar
  15. 15.
    AlShahwan F, Moessner K, Carrez F. (2011) ‘Distributing resource intensive mobile web services’. 2011 International Conference on Innovations in Information Technology, IIT 2011, pp. 41–46Google Scholar
  16. 16.
    AlShahwan F, Moessner K, Carrez F (2011) Distributing resource intensive mobile web services. 2011 International conference on innovations in information technology, IIT 2011, 25–27 Apr, Abu Dhabi, pp 41–46Google Scholar
  17. 17.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann R, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18, doi:10.1145/1656274.1656278, http://doi.acm.org/10.1145/1656274.1656278CrossRefGoogle Scholar
  18. 18.
    Walter SD (2005) The partial area under the summary ROC curve. Stat Med 24(13):2025–2040CrossRefGoogle Scholar
  19. 19.
    Gatsonis C, Paliwal P (2006) Meta-analysis of diagnostic and screening test accuracy evaluations: methodologic primer, research, fundamentals of clinical research for radiologists. Am J Roentgenol 187:271–281CrossRefGoogle Scholar
  20. 20.
    Okeanos Cloud Infrastructure. https://okeanos.grnet.gr/home/
  21. 21.
    Maragoudakis M, Maglogiannis I (2011) A medical ontology for intelligent web-based skin lesions image retrieval. Health Informatics J 17(2):140–157CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Charalampos Doukas
    • 1
  • Paris Stagkopoulos
    • 1
  • Ilias Maglogiannis
    • 1
    Email author
  1. 1.Department of Digital SystemsUniversity of PiraeusPiraeusGreece

Personalised recommendations