Skip to main content

Advertisement

SpringerLink
Log in

An Efficient Automated Algorithm to Detect Ocular Surface Temperature on Sequence of Thermograms Using Snake and Target Tracing Function

  • ORIGINAL PAPER
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Functional infrared (IR) imaging is widely adopted in medical field nowadays, with more emphasis on breast cancer and ocular abnormalities. In this article, an algorithm is presented to accurately locate the eye and cornea in ocular thermographic sequences, which were recorded utilizing functional infrared imaging. The localization is achieved by snake algorithm coupled with a newly proposed target tracing function. The target tracing function enables automated localization, allows the absence of any manual assistance before the algorithm runs. Genetic algorithm is used to perform the search for global minimum on the function to produce desired localization. On all the cases we have studied, in average the region encircled by the algorithm covers 92% of the true ocular region. As for the non-ocular region covered, it only accounts for less than 5% of the encircled region.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Holmberg, A., The temperature of the eye during application of hot packs and after milk injections. Acta Ophthalmol. (Copenhagen) 30(4):347–364, 1952.

    Article  Google Scholar 

  2. Tan, J. H., Ng, E. Y. K., Acharya, U. R., and Chee, C., Infrared thermography on ocular surface temperature: A review. Infrared Phys. Technol. 52:97–108, 2009.

    Article  Google Scholar 

  3. Fatt, I., and Forester, J. F., Errors in eye tissue temperature measurements when using a metallic probe. Exp. Eye Res. 14(3):270–276, 1972.

    Article  Google Scholar 

  4. Rosenbluth, R. F., and Fatt, I., Temperature measurements in the eye. Exp. Eye Res. 25(4):325–341, 1977.

    Article  Google Scholar 

  5. Efron, N., Young, G., and Brennan, N., Ocular surface temperature. Curr. Eye Res. 8(9):901–906, 1989.

    Google Scholar 

  6. Tan, J.-H., Ng, E. Y. K., Acharya, U. R., and Chee, C., Study of normal ocular thermogram using textural parameters. Infrared Phys. Technol. 53(2):120–126, 2010.

    Article  Google Scholar 

  7. Morgan, P. B., Soh, M. P., and Efron, N., Corneal surface temperature decrease with age. Contact Lens Anterior Eye 22(1):11–13, 1999.

    Article  Google Scholar 

  8. Purslow, C., Wolffsohn, J. S., and Santodomingo-Rubido, J., The effect of contact lens wear on dynamic ocular surface temperature. Contact Lens Anterior Eye 28:29–36, 2005.

    Article  Google Scholar 

  9. Chiang, H. K., Chen, C. Y., Cheng, H. Y., Chen, K.-H., and Chang, D. O., Development of infrared thermal imager for dry eye diagnosis, in Proceedings of SPIE—The International Society for Optical Engineering, San Diego, CA, USA, 2006.

  10. Alio, J., and Padron, M., Normal variations in the thermographic pattern of the orbitoocular regionocular region. Diagn. Imaging 51(2):93–98, 1982.

    Google Scholar 

  11. Morgan, P. B., Soh, M. P., Efron, N., and Tullo, A. B., Potential applications of ocular thermography. Optom. Vis. Sci. 70(7):568–576, 1993.

    Article  Google Scholar 

  12. Acharya, U. R., Ng, E., Yee, G., Hua, T., and Kagathi, M., Analysis of normal human eye with different age groups using infrared images. J. Med. Syst. 33(3):207–213, 2009.

    Article  Google Scholar 

  13. Tan, J. H., Ng, E. Y. K., and Acharya, U. R., Detection of eye and cornea on IR thermogram using genetic snake algorithm, in 9th International Conference on Quantitative Infrared Thermography, Krakow, Poland, 143–150, 2008.

  14. Tan, J. H., Ng, E. Y. K., and Acharya, U. R., Automated detection of eye and cornea on infrared thermogram using snake and target tracing function coupled with genetic algorithm. Quantitative InfraRed Thermography International Journal 6(1):21–36, 2009.

    Article  Google Scholar 

  15. Kass, M., Witkin, A., and Terzopoulos, D., Snakes: active contour models. Int. J. Comput. Vision 1:321–331, 1988.

    Article  Google Scholar 

  16. Xu, C., and Prince, J. L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3):359–369, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  17. Schindler, K., and Suter, D., Object detection by global contour shape. Pattern Recognit. 41(12):3736–3748, 2008.

    Article  MATH  Google Scholar 

  18. Montanari, U., On the optimal detection of curves in noisy pictures. Commun. ACM 14(5):335–345, 1971.

    Article  MATH  Google Scholar 

  19. Martelli, A., Edge detection using heurisitic search methods, 1972.

  20. Blake, A., and Yuille, A. (Eds.), Active vision. MIT, Cambridge, 1992.

    Google Scholar 

  21. Menet, S., Saint-Marc, P., Medioni, and G., B-snakes: Implementations and applications to setero, in Proc. DARPA Image Understanding Workshop. Pittsburgh, PA, 720–726, 1990.

  22. Staib, L. H., and Duncan, J. S., Parametrically deformable contour models, in Proc. Computer Vision and Pattern Recognition, San Diego, CA, 1989.

  23. Cohen, L. D., and Cohen, I., A finite element method applied to new active contour models and 3D reconstruction from cross sections, in IEEE Computer Society Conference, Osaka, Japan, 587–591, 1990.

  24. Liang, J., McInerney, T., and Terzopoulos, D., United snakes. Med. Image Anal. 10(2):215–233, 2006.

    Article  Google Scholar 

  25. Tan, J. H., Ng, E. Y. K., Acharya, U. R., and Chee, C., Automated study of ocular thermal images: Comprehensive analysis of corneal health with different age group subjects and validation (accepted), Digital Signal Processing, 2010.

  26. Mori, A., Oguchi, Y., Okusawa, Y., Ono, M., Fujishima, H., and Tsubota, K., Use of high-speed, high-resolution thermography to evaluate the tear film layer. Am. J. Ophthalmol. 124(6):729–735, 1997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50, Nanyang Avenue, Singapore, 639798, Singapore

    Jen Hong Tan & E. Y. K. Ng

  2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, Singapore

    Rajendra Acharya U

  3. Adjunct NUH Scientist, Office of Biomedical Research, National University Hospital of Singapore, Singapore, Singapore

    E. Y. K. Ng

Authors
  1. Jen Hong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Y. K. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajendra Acharya U
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Y. K. Ng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tan, J.H., Ng, E.Y.K. & Acharya U, R. An Efficient Automated Algorithm to Detect Ocular Surface Temperature on Sequence of Thermograms Using Snake and Target Tracing Function. J Med Syst 35, 949–958 (2011). https://doi.org/10.1007/s10916-010-9552-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9552-6

Keywords

Search

Navigation