CFP last date
20 December 2024
Reseach Article

Article:Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques

by Neera Singh, Ramesh Chandra Tripathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 2
Year of Publication: 2010
Authors: Neera Singh, Ramesh Chandra Tripathi
10.5120/1186-1648

Neera Singh, Ramesh Chandra Tripathi . Article:Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques. International Journal of Computer Applications. 8, 2 ( October 2010), 18-23. DOI=10.5120/1186-1648

@article{ 10.5120/1186-1648,
author = { Neera Singh, Ramesh Chandra Tripathi },
title = { Article:Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 8 },
number = { 2 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume8/number2/1186-1648/ },
doi = { 10.5120/1186-1648 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:56:30.629474+05:30
%A Neera Singh
%A Ramesh Chandra Tripathi
%T Article:Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 8
%N 2
%P 18-23
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetic retinopathy (DR) is a common retinal complication 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.

References
  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 Image Processing Conf. (IMVIP’99), Sept. 1999, pp. 103–115.
  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 2001
  8. Subhasis Chaudhuri, 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 Morphological Techniques, 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 Open Internet 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 the IEEE 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. J 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.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing Automated Diagnostic System