Skip to main content

Advertisement

SpringerLink
Log in

Reliable Feature Selection for Automated Angle Closure Glaucoma Mechanism Detection

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Glaucoma is an eye disease where a loss of vision occurs as a result of progressive optic nerve damage usually associates with high intraocular pressure. A subtype of glaucoma called primary angle-closure glaucoma (PACG) has been observed to be the result of one or more mechanisms such as Pupil block, Plateau iris, Peripheral iris roll, and Lens in the anterior segment of the eye. Reliable features in anterior segment images are important for determining the specific mechanisms involved in PACG. In this paper, first the discriminant features are selected by several feature selection algorithms in the context of PACG detection based on anterior segment optical coherence tomography (AS-OCT) images, and then a novel criteria is proposed to further select more reliable features. Our approach is based on selecting the top-ranked features in each algorithm and its rank combination for selection of the best features. Compared with the features selected by the individual feature selection methods, the features selected by our method achieves the best performance in terms of the accuracy of classification of the four PACG mechanisms by using AdaBoost classifier.

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

Similar content being viewed by others

References

  1. Quigley, H. A., and Broman, A. T., “The number of people with glaucoma worldwide in 2010 and 2020”. Br. J. Ophthalmol. 90(3):262–267, 2006.

    Article  Google Scholar 

  2. Lowe, R. F., “Aetiology of the anatomical basis for primary angle-closure glaucoma. Biometrical comparisons between normal eyes and eyes with primary angle-closure glaucoma”. Br. J. Ophthalmol. 54(3):161–169, 1970.

    Article  Google Scholar 

  3. Acharya, U. R., Dua, S., Du, X., Sree, S. V., and Chua, C. K., “Automated diagnosis of glaucoma using texture and higher order spectra features”. IEEE Trans. Inf. Technol. Biomed. 15(3):449–455, 2011.

    Article  Google Scholar 

  4. García G. S., Galilea E. H., “Using artificial neural networks to identify glaucoma stages,”. In: The Mystery of Glaucoma, Intech Publishers, 331–352, 2011.

  5. Pachiyappan, A., Das, U. N., Murthy, T. V. S. P., and Tatavarti, R., “Automated diagnosis of diabetic retinopathy and glaucoma using Fundus and OCT images”. Lipids Health Dis. 11(73):1–10, 2012.

    Google Scholar 

  6. Mookiah, M. R. K., Acharya, U. R., Lim, C. M., Petznick, A., and Suri, J. S., “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features”. Knowl.-Based Syst. 33:73–82, 2012.

    Article  Google Scholar 

  7. Krishnan, M. M. R., “Automated glaucoma detection using hybrid feature extraction in retinal fundus images”. J. Mech. Med. Biol. 13(1):1–21, 2013.

    Article  Google Scholar 

  8. Nayak, J., Acharya, U. R., Bhat, P. S., Shetty, N., and Lim, T.-C., “Automated diagnosis of glaucoma using digital fundus images”. J. Med. Syst. 33:337–346, 2009.

    Article  Google Scholar 

  9. Tham, Y. C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., and Cheng, C. Y., “Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis”. Ophthalmology 121(11):2081–2090, 2014.

    Article  Google Scholar 

  10. How, A. C., Baskaran, M., Kumar, R. S., He, M., Foster, P. J., Lavanya, R., Wong, H. T., Chew, P. T. K., Friedman, D. S., and Aung, T., “Changes in anterior segment morphology after laser peripheral iridotomy: an anterior segment optical coherence tomography study”. Ophthalmology 119(7):1383–1387, 2012.

    Article  Google Scholar 

  11. Shabana, N., Aquino, M. C., See, J., Tan, A. M., Nolan, W. P., Hitchings, R., Young, S. M., Loon, S. C., Sng, C. C., Wong, W., and Chew, P. T. K., “Quantitative evaluation of anterior chamber parameters using anterior segment optical coherence tomography in primary angle closure mechanisms”. Clin. Experiment. Ophthalmol. 40:792–801, 2012.

    Article  Google Scholar 

  12. Coyne A, Shovlin J, “AS-OCT Technology: Analyzing the Anterior Segment”, Review of Optometry, 2012. [Online]. Available: http://www.revoptom.com/continuing_education/tabviewtest/lessonid/108148/.

  13. Han, J.-H., Kang, J. U., and Song, C. G., “Polarization sensitive subcutaneous and muscular imaging based on common path optical coherence tomography using near infrared source”. J. Med. Syst. 35:521–526, 2011.

    Article  Google Scholar 

  14. Acharya, U. R., Yun, W. L., Ng, E. Y., Yu, W., and Suri, J. S., “Imaging system of human eye: A review”. J. Med. Syst. 32(4):301–315, 2008.

    Article  Google Scholar 

  15. Nongpiur, M. E., Ku, J. Y., and Aung, T., “Angle closure glaucoma: a mechanistic review”. Curr. Opin. Ophthalmol. 22(2):96–101, 2011.

    Article  Google Scholar 

  16. Ng, W. T., and Morgan, W., “Mechanisms and treatment of primary angle closure: a review”. Clin. Experiment. Ophthalmol. 40(4):e218–e228, 2012.

    Article  Google Scholar 

  17. Leung, K., Chan, W. M., Ko, C. Y., Chui, S. I., Woo, J., Tsang, M. K., and Tse, R. K., “Visualization of anterior chamber angle dynamics using optical coherence tomography”. Ophthalmology 112:980–984, 2005.

    Article  Google Scholar 

  18. Sng, C. C., Aquino, M. C., Liao, J., Ang, M., Zheng, C., Loon, S. C., and Chew, P. T. K., “Pretreatment anterior segment imaging during acute primary angle closure: insights into angle closure mechanisms in the acute phase”. Ophthalmology 121(1):119–125, 2014.

    Article  Google Scholar 

  19. Wirawan, A., Kwoh, C. K., Chew, P. T. K., Aquino, M. C. D., Seng, C. L., See, J., Zheng, C., and Lin, W., “Feature selection for computer-aided angle closure glaucoma mechanism detection”. J. Med. Imaging Health Informat. 2(4):438–444, 2012.

    Article  Google Scholar 

  20. Saeys Y, Abeel T, Peer YV, “Robust feature selection using ensemble feature selection techniques”. In: Proc. 25th European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Springer-Verlag, Berlin, Heidelberg, 313–325, 2008.

  21. Peng, H., Long, F., and Ding, C., “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy”. IEEE Trans. Pattern Anal. Mach. Intell. 27(8):1226–1238, 2005.

    Article  Google Scholar 

  22. Şen B, Peker M, Çavuşoğlu A, Çelebi FV, “A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms”, J. Med. Syst. 38, no. 18, 2014. (DOI: 10.1007/s10916-014-0018-0).

  23. He, X., Cai, D., and Niyogi, P., “Laplacian score for feature selection”, in Advances in Neural Information Processing System, vol. 17. MIT Press, Cambridge, 2005.

    Google Scholar 

  24. Liu H, Setiono R, “Chi2: Feature selection and discretization of numeric attributes”. In: Proc. 7th IEEE Int. Conf. Tools with Artificial Intelligence, 388–391, 1995.

  25. Duda, R. O., Hart, P. E., and Stork, D. G., “Pattern classification”. Wiley, New York, 2001.

    Google Scholar 

  26. Zhao, Z., Morstatter, F., Sharma, S., Alelyani, S., Anand, A., and Liu, H., “Advancing feature selection research: ASU Feature selection repository”. TR-10-007, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, 2007.

    Google Scholar 

  27. Cawley G, Talbot N, Girolami M, “Sparse multinomial logistic regression via Bayesian L1 regularisation”. In: Advances in Neural Information Processing Systems 19:209–216, 2007.

  28. Li, Y. J., Hsu, D. F., and Chung, S. M., “Combination of multiple feature selection methods for text categorization by using combinatorial fusion analysis and rank-score characteristic”. Int. J. Artif. Intell. Tools 22(2):1350001–1350025, 2013.

    Article  Google Scholar 

  29. Freund, Y., and Schapire, R. E., “A Decision-Theoretic generalization of on-line learning”. J. Comput. Syst. Sci. 55:119–139, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  30. Yoo, I., Alafaireet, P., Marinov, M., Keila, P.-H., Gopidi, R., Chang, J.-F., and Hua, L., “Data mining in healthcare and biomedicine: A survey of the literature”. J. Med. Syst. 36(4):2431–2448, 2012.

    Article  Google Scholar 

  31. Ozcift, A., and Gulten, A., “A robust multi-class feature selection strategy based on rotation forest ensemble algorithm for diagnosis of Erythemato-Squamous diseases”. J. Med. Syst. 36:941–949, 2012.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Ministry of Education (MoE) AcRF Tire 1 Funding, Singapore, under Grant M4010981.020 RG36/11.

Conflict of interest

There is no conflict of interest.

Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore

    S. Issac Niwas, Weisi Lin & Chee Keong Kwoh

  2. The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Xiaolong Bai

  3. School of Electrical and Electronics Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore

    Xiaolong Bai

  4. Eye Surgery Centre, National University Health System (NUHS), Singapore, 119228, Singapore

    M. Cecilia Aquino

  5. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, 119228, Singapore

    Chelvin C. Sng & P. T. K. Chew

Authors
  1. S. Issac Niwas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weisi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaolong Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chee Keong Kwoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chelvin C. Sng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Cecilia Aquino
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. P. T. K. Chew
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Issac Niwas.

Additional information

This article is part of the Topical Collection on Patient Facing Systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niwas, S.I., Lin, W., Bai, X. et al. Reliable Feature Selection for Automated Angle Closure Glaucoma Mechanism Detection. J Med Syst 39, 21 (2015). https://doi.org/10.1007/s10916-015-0199-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0199-1

Keywords

Search

Navigation