CFP last date
20 December 2024
Reseach Article

An Approach for the Detection of Proliferative Diabetic Retinopathy

Published on April 2012 by J. Sweetline Arputham, G. Tamilpavai, S. Tamilselvi
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 8
April 2012
Authors: J. Sweetline Arputham, G. Tamilpavai, S. Tamilselvi
9200afd2-8764-44eb-8e81-9f611c810e5e

J. Sweetline Arputham, G. Tamilpavai, S. Tamilselvi . An Approach for the Detection of Proliferative Diabetic Retinopathy. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 8 (April 2012), 25-29.

@article{
author = { J. Sweetline Arputham, G. Tamilpavai, S. Tamilselvi },
title = { An Approach for the Detection of Proliferative Diabetic Retinopathy },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 8 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 25-29 },
numpages = 5,
url = { /proceedings/icon3c/number8/6061-1062/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A J. Sweetline Arputham
%A G. Tamilpavai
%A S. Tamilselvi
%T An Approach for the Detection of Proliferative Diabetic Retinopathy
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 8
%P 25-29
%D 2012
%I International Journal of Computer Applications
Abstract

Proliferative diabetic retinopathy is the most advanced stage of diabetic retinopathy, and is classified by the growth of new blood vessels. These blood vessels are abnormal and fragile, and are susceptible to leaking blood and fluid onto the retina, which can cause severe vision loss. This paper proposes a method by combining prior works of Keith A. Goatman et al. (2011) and Gopal Datt Joshi et al (2011) for the detection of proliferative diabetic retinopathy. First, vessel-like patterns are segmented by using Ridge Strength Measurement and Watershed lines. The second step is measuring the vessel pattern obtained. Many features that are extracted from the blood vessels such as shape, position, orientation, brightness, contrast and line density have been used to quantitate patterns in retinal vasculature. Based on the features extracted, the segment is classified as normal or abnormal by using Support Vector Machine Classifier. The obtained accuracy may be sufficient to reduce the workload of an ophthalmologist and to prioritize the patient grading queues.

References
  1. Keith A. Goatman, Alan D. Fleming, Sam Philip, Graeme J. Williams, John A. Olson, and Peter F. Sharp, "Detection of New Vessels on the Optic Disc Using Retinal Photographs,"IEEE Trans. Med. Imag. , vol. 30, no. 4,pp. 972-979,April 2011.
  2. M. Niemeijer, B. V. Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abramoff, "Automatic detection of red lesions in digital color fundus photographs," IEEE Trans. Med. Imag. , vol. 24, no. 5, pp. 584–592, May 2005.
  3. J. H. Hipwell, F. Strachan, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, "Automated detection of microaneurysms in digital red-free photographs: A diabetic retinopathy screening tool," Diabetic Med. , vol. 17, pp. 588–594, 2000.
  4. T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, and J. C. Klein, "Automatic detection of microaneurysms in color fundus images," Med. Image Anal. , vol. 11, pp. 555–566, 2007.
  5. A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp,"Automated detection of exudates for diabetic retinopathy screening," Phys. Med. Biol. , vol. 52, pp. 7385–7396, 2007.
  6. C. I. Sánchez, M. García, A. Mayo, M. I. López, and R. Hornero, "Retinal image analysis based on mixture models to detect hard exudates," Med. Image Anal. , vol. 13, pp. 650–658, 2009.
  7. M. Niemeijer, B. V. Ginneken, S. R. Russel, M. S. A. Suttorp-Schulten, and M. D. Abramoff, "Automated detection and differentiation of drusen, exudates and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis," Investigate Ophthalmol. Vis. Sci. , vol. 48, pp. 2260–2267, 2007.
  8. C. I. Sánchez, M. García, A. Mayo, M. I. López, and R. Hornero, "Retinal image analysis based on mixture models to detect hard exudates," Med. Image Anal. , vol. 13, pp. 650–658, 2009.
  9. A. D. Fleming, K. A. Goatman, S. Philip, G. J. Williams, G. J. Prescott, G. S. Scotland, P. McNamee, G. P. Leese, W. Wykes, P. F. Sharp, and J. A. Olson, "The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy," Br. J. Ophthalmol. , vol. 94, no. 6, pp. 706–711, 2010.
  10. T. Walter, J. C. Klein, P. Massin, and 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 Trans. Med. Imag. , vol. 21, no. 10, pp. 1236–1243, Oct. 2002.
  11. P. H. Gregson, Z. Shen, R. C. Scott, and V. Kozousek, "Automated grading of venous beading," Comput. Biomed. Res. , vol. 28, pp. 291–304, 1995.
  12. C. W. Yang, D. J. Ma, S. C. Chao, C. M. Wang, C. H. Wen, C. S. Lo,P. C. Chung, and C. I. Chang, "Computer-aided diagnostic detection system of venous beading in retinal images," Opt. Eng. , vol. 39, pp. 1293–1303, 1995.
  13. H. F. Jelinek, M. J. Cree,J. J. G. Leandro, J. V. B. Soares, R. M. C. Jr, and A. Luckie, "Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy," J. Opt. Soc. Am. A, vol. 24, pp. 1448–1456, 2007.
  14. A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp, "Automatic detection of retinal anatomy to assist diabetic retinopathy screening," Phys. Med. Biol. , vol. 52, pp. 331–345, 2007.
  15. Thitiporn Chanwimaluang, Guoliang Fan, and Stephen R. Fransen" Hybrid Retinal Image Registration" IEEE Trans. on Info. Tech. in Biomedicine vol. 10, no. 1, pp. 129-142, Jan. 2006.
  16. M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, "Fast detection of the optic disc and fovea in color fundus photographs," Med. Image Anal. , vol. 13, pp. 869–870, 2009.
  17. Gopal Datt Joshi, Jayanthi Sivaswamy and S. R. Krishnadas,"Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment", IEEE Trans. Med. Imag. , vol. 30, no. 6, pp. 1192–1205, Mar. 2011.
  18. A. Hoover, V. Kouznetsova, and M. Goldbaum, "Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response," IEEE Trans. Med. Imag. , vol. 19, no. 3, pp. 203–210, Mar. 2000.
  19. F. Zana and J. C. Klein, "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation," IEEE Trans. Image Process. , vol. 10, no. 7, pp. 1010–1019, Jul. 2001.
  20. J. J. Staal, M. D. Abr'amoff, M. Niemeijer, M. A. Viergever, and B. V. Ginneken, "Ridge based vessel segmentation in color images of the retina," IEEE Trans. Med. Imag. , vol. 23, no. 4, pp. 501–509, Apr. 2004.
  21. T. Lindeberg, "Scale-space theory:A basic tool for analysing structures at different scales," J. Appl. Stat. , vol. 21, pp. 225–270, 1994.
  22. F. Meyer, "Topographic distance and watershed lines," Signal Process. , vol. 38, pp. 113–125, 1994.
  23. T. Walter and J. C. Klein, "Ch. Automatic Analysis of Color Fundus Photographs and its Application to the Diagnosis of Diabetic Retinopathy," in Handbook of Biomedical Image Analysis. New York: Kluwer Academic/Plenum, 2005, vol. II, Segmentation Models, pt. B, pp. 315–368.
  24. Keith A. Goatman, Alan D. Fleming, Sam Philip,Graeme J. Williams, John A. Olson, and Peter F. Sharp "Detection of New Vessels on the Optic Disc Using Retinal Photographs" IEEE Trans. Med. Imag. , vol. 30, no. 4, pp. 972–979, Apr. 2011.
  25. B. E. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Computat. Learn. Theory, 1992, pp. 144–152.
  26. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
  27. T. F. Wu, C. J. Lin, and R. C. Weng, "Probability estimates for multiclass classification by pairwise coupling," J. Mach. Learn. Res. , vol. 5, pp. 975–1005, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetic Retinopathy Microaneurysm Vasculature Watershed Transformation Optic Disc