Skip to main content

A Quantitative Model of Cutaneous Melanoma Diagnosis Using Thermography

  • Conference paper
  • First Online:
Mathematical and Computational Approaches in Advancing Modern Science and Engineering

Abstract

Cutaneous melanoma is the most commonly diagnosed cancer and its incidence is on the rise worldwide. Early detection and differentiation of a malignant melanoma from benign cutaneous lesions provides an excellent chance for treating the disease. Thermography is a non-invasive tool that can be used to detect and monitor skin lesions. We model heat transfer in a skin region containing a lesion. The model which is governed by the Pennes equation uses the steady state temperature at the skin surface to determine whether there is an underlying lesion. Numerical simulations from the model ascertain whether the lesion is malignant or benign.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stern, R.S.: Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch. Dermatol. 146, 279–282 (2010)

    Article  Google Scholar 

  2. American Cancer Society: Cancer Facts & Figures. http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf (2015)

  3. Estee, L., Psaty, B.A., Allan, C., Halpern, M.D.: Current and emerging technologies in melanoma diagnosis: the state of the art. Clin. Dermatol. 27, 35–45 (2009)

    Article  Google Scholar 

  4. Arora, N., Martins, D., Ruggerio, D., Tousimis, E., Swistel, A.J., Osborne, M.P., Simmons, R.M.: Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Am. J. Surg. 196, 523–526 (2008)

    Article  Google Scholar 

  5. Bronzino, J.D.: Medical Devices and Systems. CRC/Taylor & Francis, Boca Raton (2006)

    Book  Google Scholar 

  6. González, F.J.: Thermal simulation of breast tumors. Revista Mexicana de Fisica, 53, 323–326 (2007)

    Google Scholar 

  7. González, F.J.: Non-invasive estimation of the metabolic heat production of breast tumors using digital infrared imaging. QIRT J. 8, 139–148 (2011)

    Article  Google Scholar 

  8. Lin, Q.Y., Yang, H.Q., Xie, S.S., Wang, Y.H., Ye, Z., Chen, S.Q.: Detecting early breast tumour by finite element thermal analysis. J. Med. Eng. Technol. 33, 274–280 (2009)

    Article  Google Scholar 

  9. Sudharsan, N.M., Ng, E.Y.K., Teh, S.L.: Surface temperature distribution of a breast with and without tumour. Comput. Methods Biomech. Biomed. Eng. 2, 187–199 (1999)

    Article  Google Scholar 

  10. Agnelli, J.P., Barrea, A.A., Turner, C.V.: Tumor location and parameter estimation by thermography. Math. Comput. Model.: Int. J. 53, 1527–1534 (2011)

    Google Scholar 

  11. Mital, M., Scott, E.P.: Thermal detection of embedded tumors using infrared imaging. J. Biomech. Eng. 129, 33–39 (2007)

    Article  Google Scholar 

  12. Paruch, M., Majchrzak, E.: Identification of tumor region parameters using evolutionary algorithm and multiple reciprocity boundary element method. Eng. Appl. Artif. Int. 20, 647–655 (2007)

    Article  Google Scholar 

  13. Deng, Z., Liu, J.: Mathematical modeling of temperature mapping over skin surface and its implementation in thermal disease diagnostics. Comput. Biol. Med. 34, 495–521 (2004)

    Article  MathSciNet  Google Scholar 

  14. Pirtini Cetingül, M., Herman, C.: A heat transfer model of skin tissue for the detection of lesions: sensitivity analysis. Phys. Med. Biol. 55, 5933–5951 (2010)

    Article  Google Scholar 

  15. Pirtini Cetingül, M., Herman, C.: Quantification of the thermal signature of a melanoma lesion. Int. J. Therm. Sci. 50, 421–431 (2011)

    Article  Google Scholar 

  16. Bhowmik, A., Repaka, R., Mishra, S.C.: Thermographic evaluation of early melanoma within the vascularized skin using combined non-Newtonian blood flow and bioheat models. Comput. Biol. Med. 53, 206–219 (2014)

    Article  Google Scholar 

  17. Agyingi, E., Wiandt, T., Maggelakis, S.: Thermal detection of a prevascular tumor embedded in breast tissue. Math. Biosci. Eng. 12, 907–915 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Maggelakis, S.A., Savakis, A.E.: Heat transfer in tissue containing a prevascular tumor. Appl. Math. Lett. 8, 7–10 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  19. Pennes, H.H.: Analysis of tissue and arterial blood temperatures in the resting forearm. J. Appl. Physiol. 1, 93–122 (1948)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ephraim Agyingi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Agyingi, E., Wiandt, T., Maggelakis, S. (2016). A Quantitative Model of Cutaneous Melanoma Diagnosis Using Thermography. In: Bélair, J., Frigaard, I., Kunze, H., Makarov, R., Melnik, R., Spiteri, R. (eds) Mathematical and Computational Approaches in Advancing Modern Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-30379-6_16

Download citation

Publish with us

Policies and ethics