Detection of Pathological Myopia by PAMELA with Texture-Based Features through an SVM Approach
Pathological myopia is the seventh leading cause of blindness worldwide. Current methods for the detection of pathological myopia are manual and subjective. We have developed a system known as PAMELA (Pathological Myopia Detection Through Peripapillary Atrophy) to automatically assess a retinal fundus image for pathological myopia. This paper focuses on the texture analysis component of PAMELA which uses texture features, clinical image context and support vector machine-based classification to detect the presence of pathological myopia in a retinal fundus image. Results on a test image set from the Singapore Eye Research Institute show an accuracy of 87.5% and a sensitivity and specificity of 0.85 and 0.90 respectively. The results show good promise for PAMELA to be developed as an automatic tool for pathological myopia detection.
R. D. Sperduto, D. Seigel, J. Roberts et al., “Prevalence of myopia in the United States,” Arch Ophthalmol, vol. 101, pp. 405–407, 1983.View at: Google Scholar
T. Y. Wong, P. Foster, J. Hee et al., “Prevalence and risk factors for refractive errors in adult Chinese in Singapore,” Invest Ophthalmol Vis Sci, vol. 41, pp. 2486–2494, 2000.View at: Google Scholar
T. Tokoro, “On the definition of pathologic myopia in group studies,” Acta Ophthalmol. Suppl., vol. 185, pp. 107–108, 1988.View at: Google Scholar
M. Secretan, D. Kuhn, G. Soubrane, and G. Coscas, “Long-term visual outcome of choroidal neovascularization in pathologic myopia: natural history and laser treatment,” Eur J Ophthalmol, vol. 7, pp. 307–16, 1997.View at: Google Scholar
H. E. Grossniklaus and W. R. Green, “Pathologic findings in pathological myopia,” Retina, vol. 12, pp. 127–33, 1992.View at: Google Scholar
S. M. Saw, L. Tong, W. H. Chua et al., “Incidence and Progression of Myopia in Singaporean School Children,” Invest Ophthalmol Vis Sci., vol. 46, pp. 51–57, 2005.View at: Google Scholar
S. M. Saw, “How blinding is pathological myopia?” Br. J. Ophthalmol., vol. 90, pp. 525–526, 2006.View at: Google Scholar
W. M. Chan, M. Ohji, T. Y. Y. Lai, D. T. L. Liu, Y. Tano, and D. S. C. Lam, “Choroidal neovascularisation in pathological myopia: an update in management,” Br. J. Opth., vol. 89, no. 11, pp. 1522–1528.View at: Google Scholar
Y. F. Shih, T. C. Ho, C. K. Hsiao, and L. L.-K. Lin, “Visual outcomes for high myopic patients with or without myopic maculopathy: a 10 year follow up study,” Br. J. Opth, vol. 90, pp. 546–550, 2006.View at: Google Scholar
C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” in Proc. of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.View at: Google Scholar
A. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 476–480, 1999.View at: Google Scholar
I. J. Liu, D. W. K. Wong, J. H. Lim et al., “ARGALI: An Automatic Cup-To-Disc Ratio Measurement System For Glaucoma Analysis Using Level-Set Image Processing,” in 13th International Conference on Biomedical Engineering (ICBME2008), 2008.View at: Google Scholar
J. Liu, D. W. K. Wong, J. H. Lim et al., “ARGALI-an Automatic cup-to-disc Ratio measurement system for Glaucoma detection and AnaLysIs framework,” in Proceedings of SPIE, vol. 7260, 72603K, 2009.View at: Google Scholar
R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Prentice Hall, New Jersey, 2003.
V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, Second Edition, 2001.
C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001.