Segmentation and Measurement of Exudates in Fundus Images of the Retina for Detection of Retinal Disease
Keywords:Retinal Fundus Images, Optic Disc, Blood Vessels, Automated Segmentation of Exudates
Retinal diseases are asymptomatic in nature with the significant effect of vision loss. With the growing number of retinopathy cases each year there is a requirement of the evaluation of large amount of database. This has led to the development of numerous automated and semi automated evaluation methods to track the retinal diseases. In this study, a simpler automated method is developed to diagnose retinal disease as far as exudates are concerned. Previously used segmentation methods do not generate very satisfactory results mainly because the composition of exudates which are degenerated regions in retinal fundus image is non homogeneous. Therefore an alternative method for segmentation is developed to use the homogeneity of healthy regions than degenerated regions. The developed technique initially separates the healthy regions like blood vessels and optic disc from the retinal fundus images and classifies as healthy. Further the dynamic region growing method is employed for the segmentation of exudates in the images containing diabetic retinal disease. The technique developed is examined on various retinal images and the outcomes reveal that the presented technique performs better than the previous proposed methods for the segmentation of exudates.
. M. Wilson, P. Soliz, S.C. Nemeth, Computer-aided methods for quantitative assessment of longitudinal changes in retinal images resenting with maculopathy. Medical Imaging, 2002. 4681: p. 150-170.
. C.I. Sanchez, R. Hornero, M.I. Lopez, J. Poza, Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy. Engineering in medicine and Biology Society, IEMBS. 26th Annual International Conference of the IEEE, 2004: p. 1624-1627.
. T. Smith, V. Sivagnanavel, J.K. Chan, et al., An inter-institutional comparative study of drusen segmentation and quantification using a digital technique of fundus background reconstruction. Investigative Ophthalmology & Visual Science, 2004. 45.
. K. Rapantzikos, M. Zervakis, K. Balas, Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration, Medical Image Analysis, 2003: p. 95-108.
. T.J. Wolfensberger, P.A.M. Hamilton, Diabetic retinopathy- an historical review. Royal Swets & Zeitlinger. Seminar in Ophthalmology, 2001. 16: p.2-7.
. R.T. Smith, J.K. Chan, T. Nagasaki, et al., Automated detection of macular drusen using geometric background leveling and threshold selection, Archives of Ophthalmology, 2005. 23: p.200-206.
. Walter, J.C. Klein, P. Massin, A. Erginay, A contribution of image processing to the diagnosis of diabetic retinopathy - detection of exudates in color fundus images of the human retina, IEEE Transactions on Medical Imaging, 2002. 21: p.1236-1243.
. A. Sopharak, B. Uyyanonvara, S. Barman, et al., Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Computerized Medical Imaging and Graphics, 2009. 32: p.720-727.
. A.W. Reza, C. Eswaran, S. Hati, Diabetic retinopathy: a quad tree based blood vessel detection algorithm using RGB components in fundus images. Journal of medical systems, 2008. 32: p.147-155.
. M. Niemeijer, B. van Ginneken, S.R. Russell, et al., Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative ophthalmology & Visual Science, 2007. 48: p. 2260-2267.
. T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, J.-C. Klein, Automatic detection of microaneurysms in color fundus images. Medical Image Analysis, 2007. 112: p. 555-566.
. K. Estabridis, R.J.P. de Figueiredo, Automatic detection and diagnosis of diabetic retinopathy ICIP. IEEE International Conference, 2007.2: p.445-448.
. S.S. Lee, M. Rajeswari, D. Ramachandram, B. Shaharuddin, Screening of diabetic retinopathy-automatic segmentation of optic disc in colour fundus. Proceedings of DFMA. The 2nd International Conference on Distributed Frameworks for Multimedia Applications, 2006: p. 37-43.
. M. García, C.I. Sánchez, J. Poza, M.I. López, R. Hornero, Detection of hard exudates in retinal images using a radial basis function classifier. Annals of Biomedical Engineering. Springer,2009.37: p. 1448-1463.
. S.S. Basha, K.S. Prasad, Automatic detection of hard exudates in diabetic retinopathy in diabetic retinopathy using morphological segmentation and fuzzy logic. IJCSNS. International Journal of Computer Science and Network Security, 2008. 8: p. 211-218.
. G. Quellec, M. Lamard, P.M. Josselin, G. Cazugel, B. Cochener, C. Roux, Optimal wavelet transform for the detection of micraaneurysms in retina photographs. IEEE Transactions on Medical Imaging, 2008. 27: p.1230-1241.
. C. Agurto, V. Murray, E. Barriga, et al., Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE Transactions on Medical Imaging, 2010. 29: p. 502-512.
. D. Satyarthi, B.A.N. Raju, S. Dandapat, Detection of diabetic retinopathy in fundus images vector quantization technique. Annual India Conference. IEEE INDICON, 2006: p. 1-4.
. C. Sinthanayothin, J.F. Boyce, H.L. Cook, T.H. Williamson, Automated location of the optic disc, foves, and retinal blood vessels from digital color fundus images. British Journal of Ophthalmology, 1999. 83: p.902-910.
. H. Li, O. Chutatape, Automatic location of optic disc in retinal images. Proc. IEEE-ICIP 2, 2001: p. 837-840.
. O. Chutatape, L. Zheng, S.M. Krishnan, Retinal blood vessel detection and tracking by matched Gaussian and kalman filters. A tutorial review. Proc. 20th IEEE Conf. on Engineering in Medicine and Biology Society, 1998: p. 3144-3149.
. H. Narasimha-Iyer, A. Can, B. Roysam, C.V. Stewart, H.L. Tanenbaum, A. Majerovics, H. singh, Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Transactions on Biomedical Engineering, 2006. 53: p.1084-1098.
. M. Niemeijer, B. Ginneken, F. Haar, Automatic detection of the optic disc, fovea, and vascular arch in digital color photographs of the retina. Proceedings of the British Machine Vision Conference, 2005: p. 109-118.
. K.A. Vermeer, M. Vos F., H.G. Lemij, et al., A model based method for retinal blood vessel detection.
Computers in Biology and Medicine, 2004. 34: p. 209-219.
. H. Li, W. Hsu, M. Li Lee, Ho. Wang, A piecewise Gaussian model for profiling and differentiating retina vessels. Proceedings of ICIP-2003, 2003. 1: p. 69-72.
. A. Osareh, M. Mirmehdi, B. Thomas, R. Markham, Comparison of color spaces for optic disc localization in retinal images. Proc. 16th IEEE Int. Conf. Pattern Recognition, 2002. 1: p. 743-746.
. M. Al-Rawi, M. Qutaishat, M. Arrar, An improved matched filter for blood vessel detection of digital retinal images. Computers in Biology and Medicine. 37: p. 262-267.
. K. Akita, H.A. Kuga, Computer method of understanding macular fundus images. Pattern Recognition, 1982. 15: p.1-443.
. C. Sinthanayothin, Vi. Kongbunkiat, S. Phoojaruenchanachai, A. Ingalavanija, Automated screening system for diabetic retinopathy. Proc. lSPAO3, 2003: p. 915-920.
. J.A. Xu, O. Chutatape, Auto-adjusted 3-D optic disk viewing from low – resolution stereo fundus image.
Computers in Biology and Medicine, 2006. 36: p. 921-940.
. C. Köse, Fully automatic segmentation of coronary vessel structures in poor quality X-ray angiogram images. Lecture Notes in Computer Science. LNCS. Springer. 4109:
. M. Niemeijer, M.D. Abramoff, B. van Ginneken, Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEE transactions on Biomedical Engineering, 2007. 26: p. 116-127.