© The Institution of Engineering and Technology
An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.
References
-
-
1)
-
1. Mookia, M.R., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E.Y., Laude, A.: ‘Computer-aided diagnosis of diabetic retinopathy: a review’, Comput. Biol. Med., 2013, 43, (12), pp. 2136–2155 (doi: 10.1016/j.compbiomed.2013.10.007).
-
2)
-
11. Abry, P.: ‘Ondelettes et turbulence. Multirésolutions, algorithmes de décomposition, invariance d'échelles’ (Diderot, Paris, 1997).
-
3)
-
8. Vapnik, V.: ‘Statistical learning theory’ (Wiley, New York, USA, 1998).
-
4)
-
5. Mora, A., Vieira, P., Fonseca, J.: ‘Advances in image processing techniques for drusen detection and quantification in fundus images’. Emerging Trends in Technological Innovation, Int. Federation of Information Processing (IFIP) Advances in Information and Communication Technology, Springer Berlin Heidelberg, 2010, vol. 314, pp. 299–307.
-
5)
-
7. Ram, K., Joshi, G.D., Sivaswam, J.: ‘A successive clutter-rejection-based approach for early detection of diabetic retinopathy’, IEEE Trans. Biomed. Eng., 2011, 58, (3), pp. 664–673 (doi: 10.1109/TBME.2010.2096223).
-
6)
-
4. Remeseiro, B., Barreira, N., Calvo, D., Ortega, M., Penedo, M.G.: ‘Automatic drusen detection from digital retinal images: AMD prevention’, Lect. Notes Comput. Sci., 2009, 5717, pp. 187–194 (doi: 10.1007/978-3-642-04772-5_25).
-
7)
-
2. Zhang, X., Chutatape, O.: ‘Detection and classification of bright lesions in colour fundus images’. Proc. of IEEE ICIP, Singapore, 2004, pp. 139–142.
-
8)
-
9)
-
3. Fleming, D.A., Philip, S., Goatman, A.K., et al: ‘Automated detection of exudates for diabetic retinopathy screening’, Phys. Med. Biol., 2007, 52, (24), pp. 7385–7396 (doi: 10.1088/0031-9155/52/24/012).
-
10)
-
9. Mallat, S.: ‘A wavelet tour of signal processing’ (Academic Press, 1999).
-
11)
-
6. Agurto, C., Murray, V., Barriga, E., et al: ‘Multiscale AM-FM methods for diabetic retinopathy lesion detection’, IEEE Trans. Med. Imaging, 2010, 29, (2), pp. 502–512 (doi: 10.1109/TMI.2009.2037146).
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