CFP last date
20 December 2024
Reseach Article

Automatic Detection of Optic Disc in Digital Retinal Images

by Kittipol Wisaeng, Nualsawat Hiransakolwong, Ekkarat Pothiruk
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 5
Year of Publication: 2014
Authors: Kittipol Wisaeng, Nualsawat Hiransakolwong, Ekkarat Pothiruk
10.5120/15569-4105

Kittipol Wisaeng, Nualsawat Hiransakolwong, Ekkarat Pothiruk . Automatic Detection of Optic Disc in Digital Retinal Images. International Journal of Computer Applications. 90, 5 ( March 2014), 15-20. DOI=10.5120/15569-4105

@article{ 10.5120/15569-4105,
author = { Kittipol Wisaeng, Nualsawat Hiransakolwong, Ekkarat Pothiruk },
title = { Automatic Detection of Optic Disc in Digital Retinal Images },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 5 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number5/15569-4105/ },
doi = { 10.5120/15569-4105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:14.876566+05:30
%A Kittipol Wisaeng
%A Nualsawat Hiransakolwong
%A Ekkarat Pothiruk
%T Automatic Detection of Optic Disc in Digital Retinal Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 5
%P 15-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

On the research work leading to automatic detection of optic disc from retinal images is very essential and crucial for expert ophthalmologists to diagnose diseases. Many of techniques can achieve good performance on retinal feature that is clearly visible. Unfortunately, it is a normal situation that the color retinal images in Thailand are poor-quality images. The existing algorithm cannot detected poor-quality images. Therefore, this study is a part of larger efforts to develop a novel method for detection of optic disc in poor-quality retinal images. A novel method is presented towards the development for detection of optic disc in poor-quality retinal images. The digital retinal images are detected by using morphological method and Otsu's algorithm after the key preprocessing steps, i. e. , color normalization, contrast enhancement and noise removal. This enables the difference in the proposed method compared to other approaches and the algorithm can achieve good performance even on poor-quality retinal images. The proposed method was evaluated using the local dataset and the publicly available of the STARE project's dataset. The optic disc was detected correctly in 91. 35% using the STARE dataset and 97. 61% using the local dataset. This system intends to help expert ophthalmologists in screening process to detect of optic disc faster and more easily.

References
  1. Sinthanayothin, C. , 1998. Automated localization of the optic disc, fovea and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. , pp: 902-910. PMID: 10413690
  2. Gagnon, L. , Marc, L. , Marie-Carole, B. 2001. Procedure to detect anatomical structures in optical fundus images. Processing of the Conference on Medical Image, (MI 01), San Diego, pp: 1218-1225. DOI: 10. 1117/12. 430999
  3. Abdel-Ghafar, R. A. , 2004. Detection and characterization of the optic disc in glaucoma and diabetic retinopathy. Proceedings of the Medical Image Understanding Analysis Conference, (AC '04), London, UK, pp: 23-24.
  4. Lalonde, M. 2001. Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans. Med. Imaging, PMID: 11700746
  5. Li, H. , Chutatape, O. 2003. A model-based approach for automated feature extraction in fundus images. Proceedings of the 9th IEEE International Conference on Computer Vision, (CV '03), IEEE Computer Society. pp: 394-399. DOI: 10. 1109/ICCV. 2003. 1238371
  6. Haar, T. F. 2005. Automatic localization of the optic disc in digital color images of human retinal. Utrecht University.
  7. Xu, C. , 1997. Gradient vector flow: A new external force for snakes. Proceeding of the IEEE Conference on Computer Vision Pattern Recognition, Jun. 17-19, IEEE Xplore Press, San Juan, pp: 66-71. DOI: 10. 1109/CVPR. 1997. 609299
  8. Osareh, A. , Mirmehdi, M. , Thomas, B. , Markham, R. 2002. Classification and localisation of diabetic-related eye disease. Proceeding of the 7th European Conference on Computer Vision Copenhagen, Vision, May 28-31, Denmark, pp: 502-516. DOI: 10. 1007/3-540-47979-1_34S
  9. Sinthanayothin, C. , 1999. Image analysis for automatic diagnosis of diabetic retinopathy, Ph. D. Dissertation, UK.
  10. Meenalosini, S. , Janet, J. , Kannan, E. 2012. A novel approach in malignancy detection of computer aided diagnosis. Am. J. Applied Sci. , 9: 1020-1029. DOI: 10. 3844/ajassp. 2012. 1020. 1029
  11. Osareh, A. 2009. Retinal markers for early detection of eye disease, automated image detection of retinal pathology. DOI: 10. 1201/9781420037005. ch5
  12. Andres, G. , Millán, M. S. 2011. Retinal image analysis: Preprocessing and feature extraction. J. Physics, 274: 1-8. DOI: 10. 1088/17426596/274/1/012039
  13. Walter, T. , Klein, J. C. 2001. Segmentation of color fundus images of the human retina: Detection of the optic disc and vascular tree using morphological techniques. Proceedings of the 2nd International Symposium on Medical Data Analysis, Oct. 8-9, pp: 282-287. DOI: 10. 1007/3-540-45497-7_43
  14. Chrastek, R. , Matthias, W. , Klaus, D. , Georg, M. , Heinrich, N. 2002. Optic disc segmentation in retinal images. Bildverarbeitung Fur Die Medizin, pp. 263-266. DOI: 10. 1007/978-3-642-55983-9_60
  15. Barrett, S. F. , Naess, E. , Molvik, T. 2001. Employing the hough transform to locate the optic disk. Biomed. Sci. Instrum. , PMID: 11347450
  16. Hoover, A. 1998. Fuzzy convergence. Proceeding of the Conference Computer Vision and Pattern Recognition, Jun. 23-25, IEEE Xplore Press, Santa Barbara, pp: 716-721. DOI: 10. 1109/CVPR. 1998. 698682
  17. Lowell, J. , Hunter, A. , Steel, D. , Basu, A. 2004. Optic nerve head segmentation. IEEE Trans. Med. Image, 23: 256-264. DOI: 10. 1109/TMI. 2003. 823261
  18. Tobin, K. W. , Tobin, J. R. , Chaum, E. , Govindasamy, V. P. , Karnowski, T. P. , 2006. Characterization of the optic disc in retinal imagery using a probabilistic approach. Med. Image, 6144: 1088-1097. DOI: 10. 1117/12. 641670
  19. Abramoff, M. D. , Niemeijer, M. 2006. The automatic detection of the optic disc location in retinal images using optic disc location regression. Proceeding IEEE Engineering in Medical and Biology Society, Aug. 30-30, IEEE Xplore Press, New York, pp: 4432-4435. DOI: 10. 1109/IEMBS. 2006. 259622
Index Terms

Computer Science
Information Sciences

Keywords

Optic Disc Morphological Method Otsu's Methods STARE Databases Expert Ophthalmologists