Skip to main content
SpringerLink
Log in

An Automated Grading and Diagnosis System for Evaluation of Dry Eye Syndrome

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This article describes methods used to determine the severity of Dry Eye Syndrome (DES) based on Oxford Grading Schema (OGS) automatically by developing and applying a decider model. The number of dry punctate dots occurred on corneal surface after corneal fluorescein staining can be used as a diagnostic indicator of DES severity according to OGS; however, grading of DES severity exactly by carefully assessing these dots is a rather difficult task for humans. Taking into account that current methods are also subjectively dependent on the perception of the ophtalmologists coupled with the time and resource intensive requirements, enhanced diagnosis techniques would greatly contribute to clinical assessment of DES. Automated grading system proposed in this study utilizes image processing methods in order to provide more objective and reliable diagnostic results for DES. A total of 70 fluorescein-stained cornea images from 20 patients with mild, moderate, or severe DES (labeled by an ophthalmologist in the Keratoconus Center of Yildirim Beyazit University Ataturk Training and Research Hospital) used as the participants for the study. Correlations between the number of dry punctate dots and DES severity levels were determined. When automatically created scores and clinical scores were compared, the following measures were observed: Pearson’s correlation value between the two was 0.981; Lin’s Concordance Correlation Coefficients (CCC) was 0.980; and 95% confidence interval limites were 0.963 and 0.989. The automated DES grade was estimated from the regression fit and accordingly the unknown grade is calculated with the following formula: Gpred = 1.3244 log(Ndots) - 0.0612. The study has shown the viability and the utility of a highly successful automated DES diagnostic system based on OGS, which can be developed by working on the fluorescein-stained cornea images. Proper implemention of a computationally savvy and highly accurate classification system, can assist investigators to perform more objective and faster DES diagnoses in real-world scenerios.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Sullivan, D.A., & Stern, M.E., & Tsubota, K., & Dartt, D.A., & Sullivan, R.M., & Bromberg, B.B., “Lacrimal Gland Tear Film, and Dry Eye Syndromes 3”. (1st ed.), Springer, (Part B), US, 2002.

  2. Lemp, M. A., Basic principles and classifications of dry eye disorders, in: The Dry Eye: A comprehensive Guide. Berlin: Springer-Verlag, 1992.

    Google Scholar 

  3. Acharya, U.R., & Ng, E.Y.K., & Suri, J. S., & Campilho, A., (Eds.), Image Analysis and Modeling in Ophthalmology”, (1st ed.), CRC Press, 294–9, 2014.

  4. Garcia, L.R., Advancing the diagnosis of Dry Eye Syndrome: development of dynamic, automated tear film Break-Up assessment. Thesis (PhD), Universidade Da Coruna, Facultade de Informatica, Departamento de Computacion, 2014.

  5. Lin, H., and Yiu, S. C., Dry eye disease: A review of diagnostic approaches and treatments. Saudi Journal of Ophthalmology 28(3):173–181, 2014.

    Article  Google Scholar 

  6. Asbell, P., & Lemp, M., Dry Eye Disease: The Clinician's Guide to Diagnosis and Treatment. (1st ed.). TNY, (Chapter 1), 2006.

  7. Foulks, G. H., Lemp, M. A., Vester, J. V., Sutphin, J., Murube, J., and Novack, G. D., The definition and classification of dry eye disease: Report of the Definition and Classification Subcommittee of the International Dry Eye Workshop. The Ocular Surface 5:75–92, 2007.

    Article  Google Scholar 

  8. Begley, C. G., Chalmers, R. L., Abetz, L., Venkataraman, K., Mertzanis, P., Caffery, B. A., Snyder, C., Edrington, T., Nelson, D., and Simpson, T., The relationship between habitual patient-reported symptoms and clinical signs among patients with dry eye of varying severity. Invest. Ophthalmol. Vis. Sci. 44:4753–4761, 2003.

    Article  Google Scholar 

  9. Adatia, F. A., Michaeli-Cohen, A., Naor, J., Caffery, B., and Slomovic, A., Correlation between corneal sensitivity, subjective dry eye symptoms and corneal staining in Sjogren’s syndrome. Can. J. Ophthalmol. 39:767–771, 2004.

    Article  Google Scholar 

  10. Vitale, S., & Goodman, L. A., & Reed, G. F., & Smith, J. A., Comparison of the NEI-VFQ and OSDI questionnaires in patients with Sjogren’s syndrome-related dry eye, Health and Quality of Life Outcomes, 2(44), 2004.

    Article  Google Scholar 

  11. Liu, Z., and Pflugfelder, S. C., Corneal surface irregularity and the effect of artificial tears in aqueous tear deficiency. Ophthalmology 106(5):939–943, 1999.

    Article  CAS  Google Scholar 

  12. Goto, E., Yagi, Y., Matsumoto, Y., and Tsubota, K., Impaired functional visual acuity of dry eye patients. Am J. Ophthalmol. 133(2):181–186, 2002.

    Article  Google Scholar 

  13. Bron, A. J., Diagnosis of dry eye. Survey of Opthalmology, 221–6, 2001.

  14. Goto, T., Zheng, X., Klyce, S. D., Kataoka, H., Uno, T., Karon, M., Tatematsu, Y., Bessyo, T., Tsubota, K., and Obashi, Y., A new method for tear film stability using videokeratography. Am J. Ophthalmol. 135(5):607–612, 2003.

    Article  Google Scholar 

  15. Gilbard, J. P., Human tear film electrolyte concentrations in health and dry-eye disease. Int. Ophthalmol. Clin. 34(1):27–36, 1994.

    Article  CAS  Google Scholar 

  16. Murube, J., Tear osmolarity. The Ocular Surface 4(2):62–73, 2006.

    Article  Google Scholar 

  17. Tomlinson, A., Khanal, S., Ramaesh, K., Diaper, C., and McFadyen, A., Tear film osmolarity: determination of a referent for dry eye diagnosis. Invest. Ophthalmol. Vis. Sci. 47(10):4309–4315, 2006.

    Article  Google Scholar 

  18. Tsubota, K., Fujihara, T., Saito, K., and Takeuchi, T., Conjunctival epithelium expression of HLA-DR in dry eye patients. Ophthalmologica 213(1):16–19, 1999.

    Article  CAS  Google Scholar 

  19. Fenga, C., Aragona, P., Cacciola, A., Spinela, R., Nola, C. D., Ferreri, F., and Rania, L., Melbonian gland dysfunction and ocular discomform in video display terminal workers. Eye 22:91–95, 2008.

    Article  CAS  Google Scholar 

  20. García-Resúa, C., Lira, M., and Yebra-Pimentel, E., Evaluacióon superficial en jóvenes universitarios. Rev. Esp. Contact 12:37–41, 2005.

    Google Scholar 

  21. Kaštelan, S., Tomić, M., Salopek-Rabatić, J., and Novak, B., Diagnostic Procedures and Management of Dry Eye. Biomed. Res. Int. 2013:1–6, 2013.

    Google Scholar 

  22. Tutt, R., Bradley, A., Begley, C., and Thibos, L. N., Optical and visual impact of tear break-up in human eyes. Invest. Ophthalmol. Vis. Sci. 41:4117–4123, 2000.

    CAS  PubMed  Google Scholar 

  23. Stevenson, W., Chauhan, S., and Dana, R., Dry eye disease: an immunemediated ocular surface disorder. Arch. Ophthalmol. 130(1):90–100, 2012.

    Article  CAS  Google Scholar 

  24. DEWS Reports, “Methodologies to diagnose and monitor dry eye disease: report of the Diagnostic Methodology Subcommittee of the International Dry Eye WorkShop pp. 108–52 [online]” http://www.tearfilm.org/dewsreport/pdfs/TOS-0502-DEWS-noAds.pdf

  25. Bron, A. J., The Doyne Lecture. Reflections on the tears. Eye (Lond) 11:583–602, 1997.

    Article  Google Scholar 

  26. Bron, A., Evans, V. E., and Smith, J. A., Grading of corneal and conjunctival staining in the context of other dry eye tests. Cornea 22(7):640–650, 2003.

    Article  Google Scholar 

  27. King-Smith, P. E., Fink, B. A., and Fogt, N., Three interferometric methods for measuring the thickness of layers of the tear film. Optom. Vis. Sci. 76(1):19–32, 1999.

    Article  CAS  Google Scholar 

  28. Goto, E., Dogru, M., Kojima, T., and Tsubota, K., Computer-synthesis of an interference color chart of human tear lipid layer, by a colorimetric approach. Invest. Ophthalmol. Vis. Sci. 44(11):4693–4697, 2003.

    Article  Google Scholar 

  29. Remeseiro, B., Ramos, L., Penas, M., Martínez, E., Penedo, M., and Mosquera, A., Colour texture analysis for classifying the tear film lipid layer: a comparative study. International Conference on Digital Image Computing: Techniques and Applications (DICTA):268–273, 2011.

  30. Remeseiro, B., Bolon-Canedo, V., Peteiro-Barral, D., Alonso-Betanzos, A., Guijarro-Berdinas, B., Mosquera, A., Penedo, M. G., and Sáanchez-Maroño, N., A Methodology for Improving Tear Film Lipid Layer Classification. IEEE Journal of Biomedical and Health Informatics 18(4):1485–1493, 2014.

    Article  Google Scholar 

  31. Remeseiro, B., & Mosquera, A., & Gonzalez Penedo, M., CASDES: a computer-aided system to support dry eye diagnosis based on tear film maps, Journal of Biomedical and Health Informatics, 1(1), 2015.

  32. Rodriguez, J. D., Lane, K. J., Ousler, G. W., Angjeli, E., Smith, L. M., and Abelson, M. B., Automated grading system for evaluation of superficial punctate keratitis associated with dry eye. Invest. Ophthalmol. Vis. Sci. 56:2340–2347, 2015.

    Article  Google Scholar 

  33. Arslan, A., & Şen, B., & Delen, D., & Uysal, B. S., & Çelebi, F. V., & Çakmak, H. B., “A Computer-Aided Grading System for Clinical Evaluation of Dry Eye Syndrome”, Proceedings of the Global Conference on Healthcare Systems Engineering and Management 2016, Istanbul, Turkey, 2016.

  34. Remeseiro, B., Ramos, L., Barreira, N., Mosquera, A., and Yebra-Pimentel, E., Colour Texture Segmentation of Tear Film Lipid Layer Images. LNCS: Computer Aided Systems Theory, Revised Selected Papers EUROCAST 2013(8112):140–147, 2013.

  35. Ramos, L., Barreira, N., Mosquera, A., Penedo, M. G., Yebra-Pimentel, E., and Garcíıa-Resúa, C., Analysis of parameters for the automatic computation of the tear film Break-Up Time test based on CCLRU standards. Comput. Methods Prog. Biomed. 113(3):715–724, 2014.

    Article  CAS  Google Scholar 

  36. Chun, Y. S., Yoon, W. B., Kim, K. G., and Park, I. K., Objective Assessment of Corneal Staining Using Digital Image Analysis. Invest. Ophthalmol. Vis. Sci. 55(12):7896–7903, 2014.

    Article  Google Scholar 

  37. Acharya, U. R., Tan, J. H., Koh, J. E. W., and Tong, L., Automated Diagnosis of Dry eye using Infrared Thermography Images. Infrared Phys. Technol. 71:263–271, 2015.

    Article  Google Scholar 

  38. Remeseiro, B., & Mosquera, A., Penedo, M., & García-Resúa, C., Tear film maps based on the lipid interference patterns, 6th International Conference on Agents and Artificial Intelligence (ICAART), France, 732–9. 2014.

  39. Wu, D., Boyer, K. L., Nichols, J. J., and King-Smith, P. E., Texture based prelens tear film segmentation in interferometry images. Mach. Vis. Appl. 21:253–259, 2010.

    Article  Google Scholar 

  40. Arslan, A., & Şen, B., & Çelebi, F.Ç., & Sertbaş, S. Automatic segmentation of region of interest for dry eye disease diagnosis system. Signal Processing and Communications Applications Conference, SIU, Zonguldak, 2016.

  41. Mohamed, M. A., Abou-El-Soud, M. A., and Eid, M. M., Iris Detection and Normalization in Image Domain Based on Morphological Features. IJCSI International Journal of Computer Science Issues 11(1):51–59, 2014.

    Google Scholar 

  42. Labati, R. D., and Scotti, F., Noisy iris segmentation with boundary regularization and reflections removal. Image Vis. Comput. 28:270–277, 2010.

    Article  Google Scholar 

  43. Gonzales, R. C., & Woods, R. E., & Eddins, S. L., Digital Image Processing Using Matlab. (2n ed.). Gatesmark, (Chapter 9), 2009.

  44. Iverson, J., Kamath, C., and Karypis, G., Evaluation of connected component labeling algorithms for distributed-memory systems. Parallel Comput. 44:53–68, 2015.

    Article  Google Scholar 

  45. Altman, D. G., and Bland, J. M., Measurement in medicine: the analysis of method comparison studies. Statistician 32:307–317, 1983.

    Article  Google Scholar 

  46. Efron, N., Morgan, P. B., and Katsara, S. S., Validation of grading scales for contact lens complications. Ophthalmic Physiol. Opt. 21:17–29, 2001.

    CAS  PubMed  Google Scholar 

  47. Efron, N., Grading scales for contact lens complications. Ophthalmic Physiol. Opt. 18:182–186, 1998.

    Article  CAS  Google Scholar 

  48. Rodriguez, J. D., Johnston, P. R., Ousler, G. W., Smith, L. M., and Abelson, M. B., Automated grading system for evaluation of ocular redness associated with dry eye. Journal of Clinical Ophthalmology 7:1–8, 2013.

    Google Scholar 

  49. Yoneda, T., Sumi, T., and Takahashi, A., Automated hyperemia software analysis: reliability and reproducibility in healthy subjects. Jpn. J. Ophthalmol. 56:1–7, 2012.

    Article  Google Scholar 

  50. Fieguth, P., and Simpson, T., Automated measurement of bulbar Redness. Invest. Ophthalmol. Vis. Sci. 43:340–347, 2002.

    PubMed  Google Scholar 

  51. Papas, E. B., Key factors in the subjective and objective assessment of conjunctival erythema. Invest. Ophthalmol. Vis. Sci. 41:687–691, 2000.

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Ankara Yıldırım Beyazıt University, Ulus, Ankara, Turkey

    Ayşe Bağbaba & Baha Şen

  2. Center for Health Systems Innovation, Spears School of Business, Department of Management Science and Information Systems, Oklahoma State University, Tulsa, Oklahoma, 74106, USA

    Dursun Delen

  3. Atatürk Education and Research Hospital, Çankaya, Ankara, Turkey

    Betül Seher Uysal

Authors
  1. Ayşe Bağbaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baha Şen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dursun Delen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Betül Seher Uysal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayşe Bağbaba.

Ethics declarations

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.” (26,379,996/218, 15.11.2017, Yildirim Beyazit University, Ankara).

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bağbaba, A., Şen, B., Delen, D. et al. An Automated Grading and Diagnosis System for Evaluation of Dry Eye Syndrome. J Med Syst 42, 227 (2018). https://doi.org/10.1007/s10916-018-1086-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-1086-3

Keywords

Search

Navigation