Feature selection for computer-aided angle closure glaucoma mechanism detection

Journal of Medical Imaging and Health Informatics

Volumn 2, Issue 4, 2012, Pages 438-444

  Wirawan, Adrianto ^a   Kwoh, Chee Keong ^b   Chew, Paul Tec Kuan ^c   Aquino, Maria Cecilia D ^c   Loon, Seng Chee ^c   See, Jovina ^c   Zheng, Ce ^c   Lin, Weisi ^b  

^a JOHANNES GUTENBERG UNIVERSITY   (Germany)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c NATIONAL UNIVERSITY HEALTH SYSTEM   (Singapore)

Author keywords

Angle closure glaucoma; Computer aided detection; Feature selection; Machine learning

Indexed keywords

EID: 84881151011     PISSN: 21567018     EISSN: 21567026     Source Type: Journal    
DOI: 10.1166/jmihi.2012.1119     Document Type: Conference Paper

References (23)

1. R. A. Hitchings and G. L. Spaeth, The optic disc in glaucoma, ii: Correlation of appearance of the optic disc with the visual field. Br. J. Ophthalmol. 61, 107 (1977).
2. S. Resnikoff, D. Pascolini, D. Etya'ale, I. Kocur, R. Pararajasegaram, G. P. Pokharel, and S. P. Mariotti, Global data on visual impairment in the year 2002. Bull World Health Organ 82, 844 (2004).
3. D. Mickler, P. Foster, and P. T. Hung, Angle closure glaucoma classification and racial variation. Asian J. Ophthalmol. 3, (2001).
4. H. Quigley and A. T. Broman, The number of people with glaucoma worldwide in 2010 and 2020. British J. Ophthalmol. 90, 262 (2006).
5. J. Cheng, J. Liu, B. H. Lee, D. W. K. Wong, F. Yin, T. Aung, M. Baskaran, P. Shamira, and T. Yin Wong, Closed angle glaucoma detection in RetCam images, Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE (2010), pp. 4096-4099.
6. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, Optical coherence tomography. Science 254, 1178 (1991).
7. S. Radhakrishnan, A. M. Rollins, J. E. Roth, S. Yazdanfar, V. Westphal, D. S. Bardenstein, and J. A. Izatt, Real-time optical coherence tomography of the anterior segment at 1310 nm. Arch. Ophthalmol. 119, 1179 (2001).
8. S. Radhakrishnan, J. Goldsmith, D. Huang, V. Westphal, D. K. Dueker, A. M. Rollins, J. A. Izatt, and S. D. Smith, Comparison of optical coherence tomography and ultrasound biomicroscopy for detection of narrow anterior chamber angles. Arch. Ophthalmol. 123, 1053 (2005).
9. F. Memarzadeh, Y. Li, V. Chopra, R. Varma, B. A. Francis, and D. Huang, Anterior segment optical coherence tomography for imaging the anterior chamber after laser peripheral iridotomy. Am. J. Ophthalmol. 143, 877 (2007).
10. Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, and J. S. Schuman, Optical coherence tomography machine learning classifiers for glaucoma detection: A preliminary study. Invest. Ophthalmol. Vis. Sci. 46, 4147 (2005).
11. M. L. Huang and H. Y. Chen, Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 46, 4121 (2005).
12. H. Peng, F. Long, and C. Ding, Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1226 (2005).
13. Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119 (1997).
14. R. Battiti, Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks 5, 537 (1994).
15. N. Kwak and C. H. Choi, Input feature selection for classification problems. IEEE Transactions on Neural Networks 13, 143 (2002).
16. C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data, Journal of bioinformatics and computational biology. 3, 185 (2005).
17. C. Zheng, C. K. Kwoh, C. Aquino, J. See, A. Chia, W. Lin, F. C. Ng-Lee, S. H. Ong, T. Aung, and P. T. K. Chew, Comparing machine learning classifier for diagnosing different angle closure mechanisms from anterior segment optical coherence tomography imaging, The Association for Research in Vision and Ophthalmology (ARVO), Fort Lauderdale, Florida (2010).
18. S. Radhakrishnan, J. Goldsmith, D. Huang, V. Westphal, D. K. Dueker, A. M. Rollins, J. A. Izatt, and S. D. Smith, Comparison of optical coherence tomography and ultrasound biomicroscopy for detection of narrow anterior chamber angles. Archives of Ophthalmology 123, 1053 (2005).
19. W. P. Nolan, J. L. See, P. T. Chew, D. S. Friedman, S. D. Smith, S. Radhakrishnan, C. Zheng, P. J. Foster, and T. Aung, Detection of primary angle closure using anterior segment optical coherence tomography in Asian eyes. Ophthalmology 114, 33 (2007).
20. F. Aptel and P. Denis Optical coherence tomography quantitative analysis of iris volume changes after pharmacologic mydriasis. Ophthalmology 117, 3 (2010).
21. C. K. Leung, C. Y. Cheung, H. Li, S. Dorairaj, C. K. Yiu, A. L. Wong, J. Liebmann, R. Ritch, R. Weinreb, and D. S. Lam, Dynamic analysis of dark-light changes of the anterior chamber angle with anterior segment OCT. Investigative Ophthalmology and Visual Science 48, 4116 (2007).
22. ImageJ. Image Processing and Analysis in Java. http://rsbweb.nih.gov/ij/
23. Srikanth Prabhu, Chandan Chakraborty, R. N. Banerjee, and A. K. Ray, Study of retinal biometric systems with respect to feature classification for recognition and diabetic retinopathy. J. Med. Imaging Health Inf. 1, 97 (2011).