IJRCS – Volume 4 Issue 1 Paper 5

Posted on by Researchscripteditor

AUTOMATED DETECTION OF SEVERITY OF DIABETIC RETINOPATHY USING RETINAL GRADING ALGORITHM

Author’s Name : S Pushpalatha

Volume 04 Issue 01  Year 2017  ISSN No:  2349-3828  Page no: 17-20

12

Abstract:

Diabetic Retinopathy (DR) is a common eye disease associated with diabetes. It is a major cause of blindness in middle as well as older age groups. Therefore early detection through regular screening and timely intervention will be highly beneficial in effectively controlling the progress of the disease. Since the ratio of people afflicted with the disease to the number of eye specialist who can screen these patients is very high, there is a need of automated diagnostic system for diabetic retinopathy changes in the eye so that only diseased persons can be referred to the specialist for further intervention and treatment. Various aspects and stages of retinopathy are analyzed by examining the colored retinal images. Micro aneurysms are small saccular pouches caused by local distension of capillary walls and appear as small red dots. Their walls are thin and rupture easily to cause hemorrhages. Hard exudates are yellow lipid deposits which appear as bright yellow lesions. The bright circular region is called the optic disk. The fovea defines the center of the retina, and is the region of highest visual acuity. The spatial distribution of exudates and microaneurysms and hemorrhages, especially in relation to the fovea can be used to determine the severity of diabetic retinopathy. Image analysis tools can be used for automated detection of these various features and stages of Diabetes Retinopathy and can be referred to the specialist accordingly for intervention, thus making it a very effective tool for effective screening of Diabetic Retinopathy patients. DR patients require frequent, at least six monthly screening of vast number of patients and automating the process will go a long way in relieving the burden on the specialist and reducing the most common cause of preventable blindness.

keywords:

Diabetic Retinopathy (DR), Optic Disc, Exudates, Microaneurysms, Hemorrhages, Retinal Grading Algorithm (RGA)

Reference:

  1. C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H.Williamson, “Automated localization of the optic disc, fovea and retinal blood vessels from digital color fundus images,” Br. J. Opthalmol., vol. 83, pp. 231–238, Aug. 1999.
  2. S. Tamura and Y. Okamoto, “Zero-crossing interval correction in tracing eye-fundus blood vessels,” Pattern Recogn., vol. 21, no. 3, pp. 227–233, 1988.
  3. A. Pinz, M. Prantl, and P. Datlinger, “Mapping the human retina,” IEEE Trans. Med. Imag., vol. 1, pp. 210–215, Jan. 1998.
  4. K. Akita and H. Kuga, “A computer method of understanding ocular fundus images,” Pattern Recogn., vol. 15, no. 6, pp. 431– 443, 1982.
  5. F. Mendels, C. Heneghan, and J.-P. Thiran, “Identification of the optic disk boundary in retinal images using active contours,” in Proc. Irish Machine Vision
  6. João V. B. Soares, Jorge J. G. Leandro, Roberto M. Cesar Jr., Herbert F. Jelinek, and Michael J. Cree, Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 25, NO. 9, SEPTEMBER 2006.
  7. Frédéric Zana and Jean-Claude Klein, Segmentation of Vessel-Like Patterns Using Mathematical Morphology and Curvature Evaluation ,IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO.7, JULY 2001Subhasis Chaudhuri.
  8. Shankar Chatterjee, Norman Katz. Mark Nelson and Michael Goldbaum, Detection of Blood Vessels in Retinal Images Using Two-Dimensional Matched Filters, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 8, NO. 3,
    SEPTEMBER 1989
  9. Tamar Peli and Eli Peli, Fundus Image Analysis Using Mathematical Morphology, in Vision Science and Its Applications, 1994 Technical Digest Series, Vol. 2 (Optical Society of America, Washington, DC, 1994), pp. 224-227.
  10. Thomas Walter and Jean-Claude Klein, Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using MorphologicalTechniques, J. Crespo, V. Maojo, and  F.Martin (Eds.): ISMDA 2001, LNCS 2199, pp. 282–287, 2001. @ Springer-Verlag Berlin Heidelberg 2001
  11. Carla Agurto, Victor Murray, Eduardo Barriga, Sergio Murillo, Marios Pattichis, , Herbert Davis, Stephen Russell, Michael Abràmoff, and Peter Soliz, Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection, IEEE Trans Med Imaging.2010 February; 29(2):502-512
  12. D.Abraham Chandy, V.Vijaya Kumari. Genetic Algorithm Based Location of Optic Disc in Retinal Images.Academic OpenInternet Journal Volume 17, 2006.
  13. H. Li, O. Chutatape. “Automatic Location of Optic Disc in Retinal images”. IEEE ICIP, 2001, pp. 837-840.
  14. H. Li and O. Chutatape. “Fundus image features extraction”. Proceedings of the 22nd Annual International Conference of theIEEE Engineering in Medicine and Biology Society, Vol. 4,2000, pp.3071 -3073.
  15. Giri Babu Kande, T. Satya Savithri, P. Venkata Subbaiah, M. R. N. Tagore. Automatic detection and boundary estimation of optic disk in fundus images using geometric active contours. Biomedical Science and Engineering, 2009, 2, 90-95.
  16. A Osareh, M Mirmehdi, B Thomas, R Markham. Automated identification of diabetic retinal exudates in digital colour images.Br J Ophthalmol 2003; 87:1220 1223.
  17. Yong Yang, Shuying Huang, Nini Rao. An Automatic Hybrid method for Retinal blood vessel Extraction. Int. J. Appl. Math. Comput. Sci., 2008, Vol. 18, No. 3, 399–407.
  18. Automated Feature Extraction for Early Detection of Diabetic Retinopathy in Fundus Images. www.cse.iitm.ac.in.