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Longitudinal time-series of color retinal Fundus Image for Diabetic Retinopathy

by C.Jayakumari, R.Maruthi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 33 - Number 10
Year of Publication: 2011
Authors: C.Jayakumari, R.Maruthi
10.5120/4107-5225

C.Jayakumari, R.Maruthi . Longitudinal time-series of color retinal Fundus Image for Diabetic Retinopathy. International Journal of Computer Applications. 33, 10 ( November 2011), 43-46. DOI=10.5120/4107-5225

@article{ 10.5120/4107-5225,
author = { C.Jayakumari, R.Maruthi },
title = { Longitudinal time-series of color retinal Fundus Image for Diabetic Retinopathy },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 33 },
number = { 10 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 43-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume33/number10/4144-5225/ },
doi = { 10.5120/4107-5225 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:19:54.066050+05:30
%A C.Jayakumari
%A R.Maruthi
%T Longitudinal time-series of color retinal Fundus Image for Diabetic Retinopathy
%J International Journal of Computer Applications
%@ 0975-8887
%V 33
%N 10
%P 43-46
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetic retinopathy is a severe and widespread eye disease that affects many diabetic patients and it remains one of the leading causes of blindness Usually diabetic retinopathy is asymptomatic in the premature phase and intensifies as it grows. Hence, routine screening is essential to reduce the further complication to a significant level. In this paper, a state-of-art image processing techniques to automatically detect the occurrence of hard exudates in the fundus images are discussed. After the adaptive contrast enhancement as preprocessing stage, fuzzy C-means algorithm has been applied to extort the same. The standard deviation, intensity, edge strength and compactness of the extracted features of the fundus images have been fed as an inputs into a recurrent Echo state neural network to classify the extracted features as true candidate or not. A total of 50 images have been used to find the exudates and out of which 35 images consisting of both normal and abnormal are utilized to train the neural network and obtain 93% sensitivity and 100 % specificity.

References
  1. F. Amos, D. J. McCarty, and P. Zimmet, “5The rising global burden of diabetes and its complications: Estimates and projections to the year 2010,” Diabetic Med., vol. 14, pp. S57–S85, 1997
  2. E. J. Susman, W. J. Tsiaras, and K. A. Soper, “Diagnosis of diabetic eye disease,” JAMA, vol. 247, pp. 3231-3134, 1982.
  3. Early treatment diabetic retinopathy study research group, Archives of Ophthalmology, 103, 1796-1806, 1985.
  4. Michael Goldbaum, Saied Moezzi, AdamsTaylor, Shankar Chatterjee, Jeff Boyd, Edward Hunter, and Ramesh Jain, “Automated diagnosis and image understanding with object extraction, object classification, and inference in retinal images”, Department of Ophthalmology and Department of Engineering and Computer Science University of California La Jolla, California USA.
  5. Sinthanayothin C, Boyce J, Cook H, Williamson T, ” Automated localisationof the optic disc, fovea, and retinal blood vessels from digital colour fundusimages”, British Journal of phthalmology, 83, 902-910, 1999.
  6. Alireza Osareh, ”Automated identification of diabetic retinal exudates and the optic Disc PhD Thesis”, University of Bristol January, 2004.
  7. Jaeger, H.; Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach (Tech. Rep. No. 159).; Bremen: German National Research Center for Information Technology, 2002.
  8. Jaeger, H, ”The echo state approach to analyzing and training recurrent neural networks; (Tech. Rep. No. 148). Bremen”, German National Research Center for Information Technology, 2001.
  9. Lena Kallin Westin ”Receiver operating characteristic (ROC) analysis, Evaluating discriminance effects among decision support systems”, UMINF 01.18, ISSN-0348-0542.
  10. J.Anitha,C.Kezi Selva Vijilla “Automated multi level pathology identification techniques for abnormal retinal images using artificial neural network, Br J Ophthalmol doi:10.1136/bjophthalmol-2011-300032,2011
  11. Jagadish Nayak, P Subbanna Bhat, Rajendra Acharya U, Lim C M, Manjunath Kagathi, ”Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images”, Journal of Medical systems Springer, 32(2), 2008, 107-115.
  12. Garcia M, Hornero R, Sanchez C I, Lopez M I, Diez A, ”Feature Extraction and Selection for the Automatic Detection of Hard Exudates in Retinal Images”, 29th Annual International Conference of the IEEE, 22(26), 2007, 4969-4972.
  13. Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman,Sakchai Vongkittirux and Nattapol Wongkamchang “ Fine Exudate Detection using Morphological Reconstruction Enhancement”, Internatioal Journal of applied biomedical engineering vol 1,No.1,2010
  14. Sinthanayothin C, ”Image analysis for automatic diagnosis of diabetic retinopathy. PhD Thesis”, King’s College of London, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetic Retinopathy Hard exudates Fuzzy C-Means ESNN

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