Eyelid measurements using digital video processing

ABSTRACT

The aim of this paper is to present an automatic eyelid measurement system based on digital video processing techniques. Currently, the protocol to measure the palpebral fissure (PF) and the marginal reflex distance (MRD) requires the use of a millimetric ruler. This procedure is subject to error and the accuracy and reproducibility of the results depend on the experience of the examiner. The computer vision system introduced in this paper uses two near infrared light sources synchronized with the camera to robustly detect and track the pupil, and then segment the limbus and the eyelids. The corneal reflection generated by the light sources are used to create a reference point that is used to define the vertical line along which the measurements are taken, and to determine when the patient is actually looking at the camera. Our experimental results show that the system is robust to the presence of eyelashes, glasses, and contact lenses, and the measurements can be accurate to tenths of millimeters.

REFERENCES

• 1: A. Amir, D. Koons, M. Flickner, and C. Morimoto. Method and apparatus for determining eye contact. IBM Docket no. AM9-98-137 (patente requerida), December 1998.
• 2: K. Bobridis, A. Assi, A. Indar, C. Bunce, and A. Tyers. Repeatability and reproducibility of upper eyelid measurements. British Journal of Ophthalmology, 85:99--101, 2001.
• 3: A. Cruz and A. Baccega. Analise bidimensional computadorizada da fenda palpebral. Arq Bras Oftalmologia, 64(1):13--19, 2001.
• 4: A. T. Duchowski. A breadth-first survey of eye tracking applications. Behavioral Research Methods, Instruments, and Computers, pages 1--16, 2002.
• 5: J. Holds. focus on digital external photography. Rev Ophthalmol, pages 78--81, 2001.
• 6: E. B. Jr. Evaluation of the eyelids. In Ophthalmic Plastic Surgery - Decision Making and Techniques, pages 43--49. McGraw-Hill, 2002.
• 7: E. B. Jr. Surgical anatomy of the eyelids. In Ophthalmic Plastic Surgery -- Decision Making and Techniques, pages 25--41. McGraw-Hill, 2002.
• 8: R. Kothari and J. Mitchell. Detection of eye locations in unconstrained visual images. In International Conference on Image Processing, volume I, pages 519--522, Lausanne, Switzerland, September 1996.
• 9: S. Matayoshi. Anatomia cirúrgica. In Manual de cirurgia plástica ocular, pages 1--19, São Paulo, 2004. Roca.
• 10: C. Morimoto, D. Koons, A. Amir, and M. Flickner. Pupil detection and tracking using multiple light sources. Image and Vision Computing, 18(4):331--336, March 2000.
• 11: Carlos H. Morimoto , Thiago T. Santos , Adriano S. Muniz, Automatic Iris Segmentation Using Active Near Infra Red Lighting, Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing, p.37, October 09-12, 2005 [doi>10.1109/SIBGRAPI.2005.14]
• 12: S. Read, M. Collins, L. Carney, and R. Iskander. The morphology of the palpebral fissure in different directions of vertical gaze. Optometry & Vision Science, 83(10):715--722, October 2006.
• 13: B. Smith and F. Nesi. Practical Techniques in Ophthalmic plastic surgery. Mosby, St. Louis, 1981.
• 14: E. Soares, A. Figueiredo, L. Oliveira, and M. M. aes. Blefaroptse. In Cirurgia Plástica Ocular, pages 77--152. Roca, 1997.
• 15: G. Wyszecki and W. Stiles. Color Science: Concepts and Methods, Quantitative Data and Formulae. John Wiley & Sons, New York, 1982.

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES

Additional Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.0 General

Keywords:
eye detection, eye tracking, eyelid measurements